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tf-keras/tf_keras/engine/training.py at v2.18.0 · keras-team/tf-keras · GitHub
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Copyright 2015 The TensorFlow Authors. All Rights Reserved.","#","# Licensed under the Apache License, Version 2.0 (the \"License\");","# you may not use this file except in compliance with the License.","# You may obtain a copy of the License at","#","# http://www.apache.org/licenses/LICENSE-2.0","#","# Unless required by applicable law or agreed to in writing, software","# distributed under the License is distributed on an \"AS IS\" BASIS,","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","# See the License for the specific language governing permissions and","# limitations under the License.","# ==============================================================================","\"\"\"Training-related part of the TF-Keras engine.\"\"\"","","import copy","import itertools","import json","import warnings","import weakref","","import numpy as np","import tensorflow.compat.v2 as tf","from tensorflow.python.distribute import distribute_utils","from tensorflow.python.distribute import input_ops","from tensorflow.python.eager import context","from tensorflow.python.platform import tf_logging as logging","from tensorflow.python.util.tf_export import keras_export","from tensorflow.tools.docs import doc_controls","","from tf_keras import backend","from tf_keras import callbacks as callbacks_module","from tf_keras import optimizers","from tf_keras.dtensor import dtensor_api","from tf_keras.dtensor import layout_map as layout_map_lib","from tf_keras.engine import base_layer","from tf_keras.engine import base_layer_utils","from tf_keras.engine import compile_utils","from tf_keras.engine import data_adapter","from tf_keras.engine import input_layer as input_layer_module","from tf_keras.engine import training_utils","from tf_keras.metrics import base_metric","from tf_keras.mixed_precision import loss_scale_optimizer as lso","from tf_keras.optimizers import optimizer","from tf_keras.optimizers import optimizer_v1","from tf_keras.saving import pickle_utils","from tf_keras.saving import saving_api","from tf_keras.saving import saving_lib","from tf_keras.saving import serialization_lib","from tf_keras.saving.legacy import serialization","from tf_keras.saving.legacy.saved_model import json_utils","from tf_keras.saving.legacy.saved_model import model_serialization","from tf_keras.utils import generic_utils","from tf_keras.utils import io_utils","from tf_keras.utils import layer_utils","from tf_keras.utils import steps_per_execution_tuning","from tf_keras.utils import tf_inspect","from tf_keras.utils import tf_utils","from tf_keras.utils import traceback_utils","from tf_keras.utils import version_utils","from tf_keras.utils.mode_keys import ModeKeys","","try:"," import h5py","except ImportError:"," h5py = None","","","@keras_export(\"keras.Model\", \"keras.models.Model\")","class Model(base_layer.Layer, version_utils.ModelVersionSelector):"," \"\"\"A model grouping layers into an object with training/inference features.",""," Args:"," inputs: The input(s) of the model: a `keras.Input` object or a"," combination of `keras.Input` objects in a dict, list or tuple."," outputs: The output(s) of the model: a tensor that originated from"," `keras.Input` objects or a combination of such tensors in a dict,"," list or tuple. See Functional API example below."," name: String, the name of the model.",""," There are two ways to instantiate a `Model`:",""," 1 - With the \"Functional API\", where you start from `Input`,"," you chain layer calls to specify the model's forward pass,"," and finally you create your model from inputs and outputs:",""," ```python"," import tensorflow as tf",""," inputs = tf.keras.Input(shape=(3,))"," x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)"," outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)"," model = tf.keras.Model(inputs=inputs, outputs=outputs)"," ```",""," Note: Only dicts, lists, and tuples of input tensors are supported. Nested"," inputs are not supported (e.g. lists of list or dicts of dict).",""," A new Functional API model can also be created by using the"," intermediate tensors. This enables you to quickly extract sub-components"," of the model.",""," Example:",""," ```python"," inputs = keras.Input(shape=(None, None, 3))"," processed = keras.layers.RandomCrop(width=32, height=32)(inputs)"," conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed)"," pooling = keras.layers.GlobalAveragePooling2D()(conv)"," feature = keras.layers.Dense(10)(pooling)",""," full_model = keras.Model(inputs, feature)"," backbone = keras.Model(processed, conv)"," activations = keras.Model(conv, feature)"," ```",""," Note that the `backbone` and `activations` models are not"," created with `keras.Input` objects, but with the tensors that are originated"," from `keras.Input` objects. Under the hood, the layers and weights will"," be shared across these models, so that user can train the `full_model`, and"," use `backbone` or `activations` to do feature extraction."," The inputs and outputs of the model can be nested structures of tensors as"," well, and the created models are standard Functional API models that support"," all the existing APIs.",""," 2 - By subclassing the `Model` class: in that case, you should define your"," layers in `__init__()` and you should implement the model's forward pass"," in `call()`.",""," ```python"," import tensorflow as tf",""," class MyModel(tf.keras.Model):",""," def __init__(self):"," super().__init__()"," self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)"," self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)",""," def call(self, inputs):"," x = self.dense1(inputs)"," return self.dense2(x)",""," model = MyModel()"," ```",""," If you subclass `Model`, you can optionally have"," a `training` argument (boolean) in `call()`, which you can use to specify"," a different behavior in training and inference:",""," ```python"," import tensorflow as tf",""," class MyModel(tf.keras.Model):",""," def __init__(self):"," super().__init__()"," self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)"," self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)"," self.dropout = tf.keras.layers.Dropout(0.5)",""," def call(self, inputs, training=False):"," x = self.dense1(inputs)"," if training:"," x = self.dropout(x, training=training)"," return self.dense2(x)",""," model = MyModel()"," ```",""," Once the model is created, you can config the model with losses and metrics"," with `model.compile()`, train the model with `model.fit()`, or use the model"," to do prediction with `model.predict()`."," \"\"\"",""," _TF_MODULE_IGNORED_PROPERTIES = frozenset("," itertools.chain("," ("," \"_train_counter\","," \"_test_counter\","," \"_predict_counter\","," \"_steps_per_execution\","," \"_compiled_trainable_state\","," ),"," base_layer.Layer._TF_MODULE_IGNORED_PROPERTIES,"," )"," )"," _SCALAR_UPRANKING_ON = False",""," def __new__(cls, *args, **kwargs):"," # Signature detection"," if is_functional_model_init_params(args, kwargs) and cls == Model:"," # Functional model"," from tf_keras.engine import functional",""," return functional.Functional(skip_init=True, *args, **kwargs)"," else:"," return super(Model, cls).__new__(cls, *args, **kwargs)",""," @tf.__internal__.tracking.no_automatic_dependency_tracking"," @traceback_utils.filter_traceback"," def __init__(self, *args, **kwargs):"," self._is_model_for_instrumentation = True",""," # Special case for Subclassed Functional Model, which we couldn't detect"," # when __new__ is called. We only realize it is a functional model when"," # it calls super.__init__ with input and output tensor."," from tf_keras.engine import functional",""," if is_functional_model_init_params(args, kwargs) and not isinstance("," self, functional.Functional"," ):"," # Filter the kwargs for multiple inheritance."," supported_kwargs = ["," \"inputs\","," \"outputs\","," \"name\","," \"trainable\","," \"skip_init\","," ]"," model_kwargs = {"," k: kwargs[k] for k in kwargs if k in supported_kwargs"," }"," other_kwargs = {"," k: kwargs[k] for k in kwargs if k not in supported_kwargs"," }"," inject_functional_model_class(self.__class__)"," functional.Functional.__init__(self, *args, **model_kwargs)",""," # In case there is any multiple inheritance here, we need to call"," # the __init__ for any class that appears after the Functional"," # class."," clz_to_init = []"," found_functional_class = False"," for clz in self.__class__.__bases__:"," if issubclass(clz, functional.Functional):"," found_functional_class = True"," continue"," if found_functional_class:"," clz_to_init.append(clz)",""," if clz_to_init:"," for clz in clz_to_init:"," clz.__init__(self, *args, **other_kwargs)"," elif other_kwargs:"," # In case there are unused kwargs, we should raise an error to"," # user, in case they have a typo in the param name."," raise TypeError("," \"The following keyword arguments passed to `Model` aren't \""," \"supported: {}.\".format(other_kwargs)"," )"," return",""," # The following are implemented as property functions:"," # self.trainable_weights"," # self.non_trainable_weights"," # `inputs` / `outputs` will only appear in kwargs if either are"," # misspelled."," generic_utils.validate_kwargs("," kwargs,"," {"," \"trainable\","," \"dtype\","," \"dynamic\","," \"name\","," \"autocast\","," \"inputs\","," \"outputs\","," },"," )"," super().__init__(**kwargs)"," # By default, Model is a subclass model, which is not in graph network."," self._is_graph_network = False",""," self.inputs = None"," self.outputs = None"," self.input_names = None"," self.output_names = None"," # stop_training is used by callback to stop training when error happens"," self.stop_training = False"," self.history = None"," # These objects are used in the default `Model.compile`. They are not"," # guaranteed to be set after `Model.compile` is called, as users can"," # override compile with custom logic."," self.compiled_loss = None"," self.compiled_metrics = None",""," # This is True for Sequential networks and Functional networks."," self._compute_output_and_mask_jointly = False",""," # Don't reset compilation if already done. This may occur if calling"," # `__init__` (or `_init_graph_network`) on an already-compiled model"," # such as a Sequential model. Sequential models may need to rebuild"," # themselves after compilation."," self._maybe_create_attribute(\"_is_compiled\", False)"," self._maybe_create_attribute(\"optimizer\", None)",""," # Model must be created under scope of DistStrat it will be trained"," # with."," if tf.distribute.has_strategy():"," self._distribution_strategy = tf.distribute.get_strategy()"," else:"," self._distribution_strategy = None"," self._distribute_reduction_method = None",""," self._cluster_coordinator = None",""," # Defaults to value of `tf.config.experimental_functions_run_eagerly`."," self._run_eagerly = None"," # Initialize cache attrs."," self._reset_compile_cache()",""," # Fault-tolerance handler. Set in `ModelCheckpoint`."," self._training_state = None"," self._saved_model_inputs_spec = None"," self._saved_model_arg_spec = None"," self._checkpoint = tf.train.Checkpoint(root=weakref.ref(self))",""," self._steps_per_execution = None"," self._steps_per_execution_tuner = None"," self._autotune_steps_per_execution = False",""," self._layout_map = layout_map_lib.get_current_layout_map()",""," self._init_batch_counters()"," self._base_model_initialized = True",""," # `jit_compile` starts off with None as default and gets overwritten by"," # the value specified in `Model.compile`, and this is effective for"," # `fit`, `evaluate`, and `predict`."," self._jit_compile = None",""," def _create_counter_variable(self, init_value):"," \"\"\"Helper function for counter variable creation.",""," For the DTensor use case with layout map, since the variable are not"," tracked by model, they can't be visited by the layout map, and need to"," be properly initialized as DVariable."," \"\"\""," # This function should be removed after we move to the strategy based"," # implementation for DTensor."," if self._layout_map is None:"," agg = tf.VariableAggregation.ONLY_FIRST_REPLICA"," return tf.Variable(init_value, dtype=\"int64\", aggregation=agg)"," else:"," layout = dtensor_api.Layout.replicated("," mesh=self._layout_map.get_default_mesh(), rank=0"," )"," return dtensor_api.DVariable("," init_value, dtype=\"int64\", layout=layout"," )",""," @tf.__internal__.tracking.no_automatic_dependency_tracking"," def _init_batch_counters(self):"," # Untracked Variables, used to keep track of mini-batches seen in `fit`,"," # `evaluate`, and `predict`."," if not tf.inside_function():"," # Creating variables inside tf.function is not allowed, hence"," # these would otherwise prevent users from creating TF-Keras layers"," # inside tf.function."," # These variables are not connected to outputs so they have no"," # effect on graph generation anyway.",""," self._train_counter = self._create_counter_variable(0)"," self._test_counter = self._create_counter_variable(0)"," self._predict_counter = self._create_counter_variable(0)",""," def __setattr__(self, name, value):"," if not getattr(self, \"_self_setattr_tracking\", True):"," super().__setattr__(name, value)"," return",""," if all("," isinstance(v, (base_layer.Layer, tf.Variable))"," or base_layer_utils.has_weights(v)"," for v in tf.nest.flatten(value)"," ):"," try:"," self._base_model_initialized"," except AttributeError:"," raise RuntimeError("," \"It looks like you are subclassing `Model` and you \""," \"forgot to call `super().__init__()`.\""," \" Always start with this line.\""," )",""," super().__setattr__(name, value)",""," def __reduce__(self):"," if self.built:"," return ("," pickle_utils.deserialize_model_from_bytecode,"," (pickle_utils.serialize_model_as_bytecode(self),),"," )"," else:"," # SavedModel (and hence serialize_model_as_bytecode) only support"," # built models, but if the model is not built,"," # it may be possible to serialize as a plain Python object,"," # as long as the constituent parts (layers, optimizers, losses,"," # etc.) can be serialized as plain Python objects. Thus we call up"," # the superclass hierarchy to get an implementation of __reduce__"," # that can pickle this Model as a plain Python object."," return super().__reduce__()",""," def __deepcopy__(self, memo):"," if self.built:"," new = pickle_utils.deserialize_model_from_bytecode("," pickle_utils.serialize_model_as_bytecode(self)"," )"," memo[id(self)] = new"," else:"," # See comment in __reduce__ for explanation"," deserializer, serialized, *rest = super().__reduce__()"," new = deserializer(*serialized)"," memo[id(self)] = new"," if rest:"," state = copy.deepcopy(rest[0], memo=memo)"," new.__setstate__(state)"," return new",""," def __copy__(self):"," return self.__deepcopy__({})",""," @generic_utils.default"," def build(self, input_shape):"," \"\"\"Builds the model based on input shapes received.",""," This is to be used for subclassed models, which do not know at"," instantiation time what their inputs look like.",""," This method only exists for users who want to call `model.build()` in a"," standalone way (as a substitute for calling the model on real data to"," build it). It will never be called by the framework (and thus it will"," never throw unexpected errors in an unrelated workflow).",""," Args:"," input_shape: Single tuple, `TensorShape` instance, or list/dict of"," shapes, where shapes are tuples, integers, or `TensorShape`"," instances.",""," Raises:"," ValueError:"," 1. In case of invalid user-provided data (not of type tuple,"," list, `TensorShape`, or dict)."," 2. If the model requires call arguments that are agnostic"," to the input shapes (positional or keyword arg in call"," signature)."," 3. If not all layers were properly built."," 4. If float type inputs are not supported within the layers.",""," In each of these cases, the user should build their model by calling"," it on real tensor data."," \"\"\""," if self._is_graph_network:"," super().build(input_shape)"," return",""," if input_shape is None:"," raise ValueError("," \"Input shape must be defined when calling `build()` on \""," \"a `Model` subclass.\""," )"," valid_types = (tuple, list, tf.TensorShape, dict)"," if not isinstance(input_shape, valid_types):"," raise ValueError("," \"Specified input shape is not one of the valid types. \""," \"Please specify a batch input shape of type tuple or \""," \"list of input shapes. User provided \""," \"input type: {}.\".format(type(input_shape))"," )",""," if input_shape and not self.inputs:"," # We create placeholders for the `None`s in the shape and build the"," # model in a Graph. Since tf.Variable is compatible with both eager"," # execution and graph building, the variables created after building"," # the model in a Graph are still valid when executing eagerly."," if tf.executing_eagerly():"," graph = tf.__internal__.FuncGraph(\"build_graph\")"," else:"," graph = backend.get_graph()"," with graph.as_default():"," if isinstance(input_shape, list) and all("," d is None or isinstance(d, int) for d in input_shape"," ):"," input_shape = tuple(input_shape)"," if isinstance(input_shape, list):"," x = ["," base_layer_utils.generate_placeholders_from_shape(shape)"," for shape in input_shape"," ]"," elif isinstance(input_shape, dict):"," x = {"," k: base_layer_utils.generate_placeholders_from_shape("," shape"," )"," for k, shape in input_shape.items()"," }"," else:"," x = base_layer_utils.generate_placeholders_from_shape("," input_shape"," )",""," kwargs = {}"," call_signature = self._call_spec.full_argspec"," call_args = call_signature.args"," # Exclude `self`, `inputs`, and any argument with a default"," # value."," if len(call_args) \u003e 2:"," if call_signature.defaults:"," call_args = call_args[2 : -len(call_signature.defaults)]"," else:"," call_args = call_args[2:]"," for arg in call_args:"," if arg == \"training\":"," # Case where `training` is a positional arg with no"," # default."," kwargs[\"training\"] = False"," else:"," # Has invalid call signature with unknown positional"," # arguments."," raise ValueError("," \"Currently, you cannot build your model if it \""," \"has positional or keyword arguments that are \""," \"not inputs to the model, but are required for \""," \"its `call()` method. Instead, in order to \""," \"instantiate and build your model, `call()` \""," \"your model on real tensor data with all \""," \"expected call arguments. The argument \""," \"for `call()` can be a single list/tuple that \""," \"contains multiple inputs.\""," )"," elif len(call_args) \u003c 2:"," # Signature without `inputs`."," raise ValueError("," \"You can only call `build()` on a model if its \""," \"`call()` method accepts an `inputs` argument.\""," )"," try:"," self.call(x, **kwargs)"," except (tf.errors.InvalidArgumentError, TypeError) as e:"," raise ValueError("," \"You cannot build your model by calling `build` \""," \"if your layers do not support float type inputs. \""," \"Instead, in order to instantiate and build your \""," \"model, call your model on real tensor data (of \""," \"the correct dtype).\\n\\nThe actual error from \""," f\"`call` is: {e}.\""," )"," super().build(input_shape)",""," @traceback_utils.filter_traceback"," def __call__(self, *args, **kwargs):"," if self._layout_map is not None and not self.built:"," # Note that this method is only overridden for DTensor and layout"," # injection purpose."," # Capture the inputs and create graph input as replacement for model"," # to initialize its weights first."," copied_args = copy.copy(args)"," copied_kwargs = copy.copy(kwargs)",""," ("," inputs,"," copied_args,"," copied_kwargs,"," ) = self._call_spec.split_out_first_arg(copied_args, copied_kwargs)",""," def _convert_to_graph_inputs(x):"," if isinstance(x, (tf.Tensor, np.ndarray, float, int)):"," x = tf.convert_to_tensor(x)"," return input_layer_module.Input(x.shape)",""," # TODO(scottzhu): maybe better handle mask and training flag."," inputs = tf.nest.map_structure(_convert_to_graph_inputs, inputs)"," copied_args = tf.nest.map_structure("," _convert_to_graph_inputs, copied_args"," )"," copied_kwargs = tf.nest.map_structure("," _convert_to_graph_inputs, copied_kwargs"," )",""," with layout_map_lib.layout_map_scope(self._layout_map):"," # We ignore the result here."," super().__call__(inputs, *copied_args, **copied_kwargs)",""," layout_map_lib._map_subclass_model_variable(self, self._layout_map)",""," return super().__call__(*args, **kwargs)",""," @doc_controls.doc_in_current_and_subclasses"," def call(self, inputs, training=None, mask=None):"," \"\"\"Calls the model on new inputs and returns the outputs as tensors.",""," In this case `call()` just reapplies"," all ops in the graph to the new inputs"," (e.g. build a new computational graph from the provided inputs).",""," Note: This method should not be called directly. It is only meant to be"," overridden when subclassing `tf.keras.Model`."," To call a model on an input, always use the `__call__()` method,"," i.e. `model(inputs)`, which relies on the underlying `call()` method.",""," Args:"," inputs: Input tensor, or dict/list/tuple of input tensors."," training: Boolean or boolean scalar tensor, indicating whether to"," run the `Network` in training mode or inference mode."," mask: A mask or list of masks. A mask can be either a boolean tensor"," or None (no mask). For more details, check the guide"," [here](https://www.tensorflow.org/guide/keras/masking_and_padding).",""," Returns:"," A tensor if there is a single output, or"," a list of tensors if there are more than one outputs."," \"\"\""," raise NotImplementedError("," \"Unimplemented `tf.keras.Model.call()`: if you \""," \"intend to create a `Model` with the Functional \""," \"API, please provide `inputs` and `outputs` \""," \"arguments. Otherwise, subclass `Model` with an \""," \"overridden `call()` method.\""," )",""," @traceback_utils.filter_traceback"," def compile("," self,"," optimizer=\"rmsprop\","," loss=None,"," metrics=None,"," loss_weights=None,"," weighted_metrics=None,"," run_eagerly=None,"," steps_per_execution=None,"," jit_compile=None,"," pss_evaluation_shards=0,"," **kwargs,"," ):"," \"\"\"Configures the model for training.",""," Example:",""," ```python"," model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),"," loss=tf.keras.losses.BinaryCrossentropy(),"," metrics=[tf.keras.metrics.BinaryAccuracy(),"," tf.keras.metrics.FalseNegatives()])"," ```",""," Args:"," optimizer: String (name of optimizer) or optimizer instance. See"," `tf.keras.optimizers`."," loss: Loss function. May be a string (name of loss function), or"," a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss"," function is any callable with the signature `loss = fn(y_true,"," y_pred)`, where `y_true` are the ground truth values, and"," `y_pred` are the model's predictions."," `y_true` should have shape"," `(batch_size, d0, .. dN)` (except in the case of"," sparse loss functions such as"," sparse categorical crossentropy which expects integer arrays of"," shape `(batch_size, d0, .. dN-1)`)."," `y_pred` should have shape `(batch_size, d0, .. dN)`."," The loss function should return a float tensor."," If a custom `Loss` instance is"," used and reduction is set to `None`, return value has shape"," `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss"," values; otherwise, it is a scalar. If the model has multiple"," outputs, you can use a different loss on each output by passing a"," dictionary or a list of losses. The loss value that will be"," minimized by the model will then be the sum of all individual"," losses, unless `loss_weights` is specified."," metrics: List of metrics to be evaluated by the model during"," training and testing. Each of this can be a string (name of a"," built-in function), function or a `tf.keras.metrics.Metric`"," instance. See `tf.keras.metrics`. Typically you will use"," `metrics=['accuracy']`."," A function is any callable with the signature `result = fn(y_true,"," y_pred)`. To specify different metrics for different outputs of a"," multi-output model, you could also pass a dictionary, such as"," `metrics={'output_a':'accuracy', 'output_b':['accuracy', 'mse']}`."," You can also pass a list to specify a metric or a list of metrics"," for each output, such as"," `metrics=[['accuracy'], ['accuracy', 'mse']]`"," or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the"," strings 'accuracy' or 'acc', we convert this to one of"," `tf.keras.metrics.BinaryAccuracy`,"," `tf.keras.metrics.CategoricalAccuracy`,"," `tf.keras.metrics.SparseCategoricalAccuracy` based on the shapes"," of the targets and of the model output. We do a similar"," conversion for the strings 'crossentropy' and 'ce' as well."," The metrics passed here are evaluated without sample weighting; if"," you would like sample weighting to apply, you can specify your"," metrics via the `weighted_metrics` argument instead."," loss_weights: Optional list or dictionary specifying scalar"," coefficients (Python floats) to weight the loss contributions of"," different model outputs. The loss value that will be minimized by"," the model will then be the *weighted sum* of all individual"," losses, weighted by the `loss_weights` coefficients. If a list,"," it is expected to have a 1:1 mapping to the model's outputs. If a"," dict, it is expected to map output names (strings) to scalar"," coefficients."," weighted_metrics: List of metrics to be evaluated and weighted by"," `sample_weight` or `class_weight` during training and testing."," run_eagerly: Bool. If `True`, this `Model`'s logic will not be"," wrapped in a `tf.function`. Recommended to leave this as `None`"," unless your `Model` cannot be run inside a `tf.function`."," `run_eagerly=True` is not supported when using"," `tf.distribute.experimental.ParameterServerStrategy`. Defaults to"," `False`."," steps_per_execution: Int or `'auto'`. The number of batches to"," run during each `tf.function` call. If set to \"auto\", keras will"," automatically tune `steps_per_execution` during runtime. Running"," multiple batches inside a single `tf.function` call can greatly"," improve performance on TPUs, when used with distributed strategies"," such as `ParameterServerStrategy`, or with small models with a"," large Python overhead. At most, one full epoch will be run each"," execution. If a number larger than the size of the epoch is"," passed, the execution will be truncated to the size of the epoch."," Note that if `steps_per_execution` is set to `N`,"," `Callback.on_batch_begin` and `Callback.on_batch_end` methods will"," only be called every `N` batches (i.e. before/after each"," `tf.function` execution). Defaults to `1`."," jit_compile: If `True`, compile the model training step with XLA."," [XLA](https://www.tensorflow.org/xla) is an optimizing compiler"," for machine learning."," `jit_compile` is not enabled for by default."," Note that `jit_compile=True`"," may not necessarily work for all models."," For more information on supported operations please refer to the"," [XLA documentation](https://www.tensorflow.org/xla)."," Also refer to"," [known XLA issues](https://www.tensorflow.org/xla/known_issues)"," for more details."," pss_evaluation_shards: Integer or 'auto'. Used for"," `tf.distribute.ParameterServerStrategy` training only. This arg"," sets the number of shards to split the dataset into, to enable an"," exact visitation guarantee for evaluation, meaning the model will"," be applied to each dataset element exactly once, even if workers"," fail. The dataset must be sharded to ensure separate workers do"," not process the same data. The number of shards should be at least"," the number of workers for good performance. A value of 'auto'"," turns on exact evaluation and uses a heuristic for the number of"," shards based on the number of workers. 0, meaning no"," visitation guarantee is provided. NOTE: Custom implementations of"," `Model.test_step` will be ignored when doing exact evaluation."," Defaults to `0`."," **kwargs: Arguments supported for backwards compatibility only."," \"\"\""," if jit_compile and not tf_utils.can_jit_compile(warn=True):"," jit_compile = False"," self._compile_config = serialization_lib.Config("," optimizer=optimizer,"," loss=loss,"," metrics=metrics,"," loss_weights=loss_weights,"," weighted_metrics=weighted_metrics,"," run_eagerly=run_eagerly,"," steps_per_execution=steps_per_execution,"," jit_compile=jit_compile,"," )"," with self.distribute_strategy.scope():"," if \"experimental_steps_per_execution\" in kwargs:"," logging.warning("," \"The argument `steps_per_execution` is no longer \""," \"experimental. Pass `steps_per_execution` instead of \""," \"`experimental_steps_per_execution`.\""," )"," if not steps_per_execution:"," steps_per_execution = kwargs.pop("," \"experimental_steps_per_execution\""," )",""," # When compiling from an already-serialized model, we do not want to"," # reapply some processing steps (e.g. metric renaming for"," # multi-output models, which have prefixes added for each"," # corresponding output name)."," from_serialized = kwargs.pop(\"from_serialized\", False)",""," self._validate_compile(optimizer, metrics, **kwargs)"," self._run_eagerly = run_eagerly",""," self.optimizer = self._get_optimizer(optimizer)",""," mesh = None"," if self._layout_map is not None:"," mesh = self._layout_map.get_default_mesh()",""," if isinstance(loss, compile_utils.LossesContainer):"," self.compiled_loss = loss"," else:"," self.compiled_loss = compile_utils.LossesContainer("," loss,"," loss_weights,"," output_names=self.output_names,"," mesh=mesh,"," )"," self.compiled_metrics = compile_utils.MetricsContainer("," metrics,"," weighted_metrics,"," output_names=self.output_names,"," from_serialized=from_serialized,"," mesh=mesh,"," )",""," if steps_per_execution == \"auto\":"," if self._steps_per_execution is None:"," self._configure_steps_per_execution(1)"," self._steps_per_execution_tuner = ("," steps_per_execution_tuning.StepsPerExecutionTuner("," self.optimizer, self._steps_per_execution"," )"," )"," self._autotune_steps_per_execution = True"," else:"," self._configure_steps_per_execution(steps_per_execution or 1)",""," self._pss_evaluation_shards = self._infer_exact_eval_shards("," pss_evaluation_shards"," )",""," # Initializes attrs that are reset each time `compile` is called."," self._reset_compile_cache()"," self._is_compiled = True"," self.loss = loss or {}"," if (self._run_eagerly or self.dynamic) and jit_compile:"," raise ValueError("," \"You cannot enable `run_eagerly` and `jit_compile` \""," \"at the same time.\""," )"," else:"," self._jit_compile = jit_compile",""," def _get_optimizer(self, optimizer):"," \"\"\"Wraps `optimizer` in `LossScaleOptimizer` if necessary.\"\"\"",""," def _get_single_optimizer(opt):"," opt = optimizers.get(opt)"," if self.dtype_policy.name == \"mixed_float16\" and not isinstance("," opt, lso.BaseLossScaleOptimizer"," ):"," # Loss scaling is necessary with mixed_float16 for models to"," # converge to the same accuracy as with float32."," opt = lso.BaseLossScaleOptimizer(opt)"," return opt",""," return tf.nest.map_structure(_get_single_optimizer, optimizer)",""," @tf.__internal__.tracking.no_automatic_dependency_tracking"," def _reset_compile_cache(self):"," self.train_function = None"," self.test_function = None"," self.predict_function = None"," # Used to cache the `tf.function`'ed `train_function` to be logged in"," # TensorBoard, since the original `train_function` is not necessarily"," # a `tf.function` (e.g., with ParameterServerStrategy, the"," # `train_function` is a scheduling of the actual training function to a"," # remote worker)."," self.train_tf_function = None",""," # Used to cache `trainable` attr of `Layer`s for `fit`."," self._compiled_trainable_state = self._get_trainable_state()",""," @tf.__internal__.tracking.no_automatic_dependency_tracking"," def _configure_steps_per_execution(self, steps_per_execution):"," self._steps_per_execution = self._create_counter_variable("," steps_per_execution"," )",""," @property"," def _should_compute_mask(self):"," return False",""," @property"," def metrics(self):"," \"\"\"Return metrics added using `compile()` or `add_metric()`.",""," Note: Metrics passed to `compile()` are available only after a"," `keras.Model` has been trained/evaluated on actual data.",""," Examples:",""," \u003e\u003e\u003e inputs = tf.keras.layers.Input(shape=(3,))"," \u003e\u003e\u003e outputs = tf.keras.layers.Dense(2)(inputs)"," \u003e\u003e\u003e model = tf.keras.models.Model(inputs=inputs, outputs=outputs)"," \u003e\u003e\u003e model.compile(optimizer=\"Adam\", loss=\"mse\", metrics=[\"mae\"])"," \u003e\u003e\u003e [m.name for m in model.metrics]"," []",""," \u003e\u003e\u003e x = np.random.random((2, 3))"," \u003e\u003e\u003e y = np.random.randint(0, 2, (2, 2))"," \u003e\u003e\u003e model.fit(x, y)"," \u003e\u003e\u003e [m.name for m in model.metrics]"," ['loss', 'mae']",""," \u003e\u003e\u003e inputs = tf.keras.layers.Input(shape=(3,))"," \u003e\u003e\u003e d = tf.keras.layers.Dense(2, name='out')"," \u003e\u003e\u003e output_1 = d(inputs)"," \u003e\u003e\u003e output_2 = d(inputs)"," \u003e\u003e\u003e model = tf.keras.models.Model("," ... inputs=inputs, outputs=[output_1, output_2])"," \u003e\u003e\u003e model.add_metric("," ... tf.reduce_sum(output_2), name='mean', aggregation='mean')"," \u003e\u003e\u003e model.compile(optimizer=\"Adam\", loss=\"mse\", metrics=[\"mae\", \"acc\"])"," \u003e\u003e\u003e model.fit(x, (y, y))"," \u003e\u003e\u003e [m.name for m in model.metrics]"," ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',"," 'out_1_acc', 'mean']",""," \"\"\""," metrics = []"," if self._is_compiled:"," if self.compiled_loss is not None:"," metrics += self.compiled_loss.metrics"," if self.compiled_metrics is not None:"," metrics += self.compiled_metrics.metrics",""," for l in self._flatten_layers():"," metrics.extend(l._metrics)"," return metrics",""," @property"," def metrics_names(self):"," \"\"\"Returns the model's display labels for all outputs.",""," Note: `metrics_names` are available only after a `keras.Model` has been"," trained/evaluated on actual data.",""," Examples:",""," \u003e\u003e\u003e inputs = tf.keras.layers.Input(shape=(3,))"," \u003e\u003e\u003e outputs = tf.keras.layers.Dense(2)(inputs)"," \u003e\u003e\u003e model = tf.keras.models.Model(inputs=inputs, outputs=outputs)"," \u003e\u003e\u003e model.compile(optimizer=\"Adam\", loss=\"mse\", metrics=[\"mae\"])"," \u003e\u003e\u003e model.metrics_names"," []",""," \u003e\u003e\u003e x = np.random.random((2, 3))"," \u003e\u003e\u003e y = np.random.randint(0, 2, (2, 2))"," \u003e\u003e\u003e model.fit(x, y)"," \u003e\u003e\u003e model.metrics_names"," ['loss', 'mae']",""," \u003e\u003e\u003e inputs = tf.keras.layers.Input(shape=(3,))"," \u003e\u003e\u003e d = tf.keras.layers.Dense(2, name='out')"," \u003e\u003e\u003e output_1 = d(inputs)"," \u003e\u003e\u003e output_2 = d(inputs)"," \u003e\u003e\u003e model = tf.keras.models.Model("," ... inputs=inputs, outputs=[output_1, output_2])"," \u003e\u003e\u003e model.compile(optimizer=\"Adam\", loss=\"mse\", metrics=[\"mae\", \"acc\"])"," \u003e\u003e\u003e model.fit(x, (y, y))"," \u003e\u003e\u003e model.metrics_names"," ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',"," 'out_1_acc']",""," \"\"\"",""," # This property includes all output names including `loss` and"," # per-output losses for backward compatibility."," return [m.name for m in self.metrics]",""," @property"," def distribute_strategy(self):"," \"\"\"The `tf.distribute.Strategy` this model was created under.\"\"\""," return self._distribution_strategy or tf.distribute.get_strategy()",""," @property"," def run_eagerly(self):"," \"\"\"Settable attribute indicating whether the model should run eagerly.",""," Running eagerly means that your model will be run step by step,"," like Python code. Your model might run slower, but it should become"," easier for you to debug it by stepping into individual layer calls.",""," By default, we will attempt to compile your model to a static graph to"," deliver the best execution performance.",""," Returns:"," Boolean, whether the model should run eagerly."," \"\"\""," if self.dynamic and self._run_eagerly == False:"," # TODO(fchollet): consider using py_func to enable this."," raise ValueError("," \"Your model contains layers that can only be \""," \"successfully run in eager execution (layers \""," \"constructed with `dynamic=True`). \""," \"You cannot set `run_eagerly=False`.\""," )",""," if self._cluster_coordinator and self._run_eagerly:"," raise ValueError("," \"When using `Model` with `ParameterServerStrategy`, \""," \"`run_eagerly` is not supported.\""," )",""," # Run eagerly logic, by priority:"," # (1) Dynamic models must be run eagerly."," # (2) Explicitly setting run_eagerly causes a Model to be run eagerly."," # (3) Not explicitly setting run_eagerly defaults to TF's global"," # setting."," return ("," self.dynamic"," or self._run_eagerly"," or (tf.config.functions_run_eagerly() and self._run_eagerly is None)"," )",""," @run_eagerly.setter"," def run_eagerly(self, value):"," self._run_eagerly = value",""," @property"," def autotune_steps_per_execution(self):"," \"\"\"Settable property to enable tuning for steps_per_execution\"\"\""," return self._autotune_steps_per_execution",""," @autotune_steps_per_execution.setter"," def autotune_steps_per_execution(self, value):"," self._autotune_steps_per_execution = value"," if value and self._steps_per_execution_tuner is None:"," if self._steps_per_execution is None:"," self._configure_steps_per_execution(1)"," self._steps_per_execution_tuner = ("," steps_per_execution_tuning.StepsPerExecutionTuner("," self.optimizer, self._steps_per_execution"," )"," )",""," @property"," def steps_per_execution(self):"," \"\"\"Settable `steps_per_execution variable. Requires a compiled model.\"\"\""," return self._steps_per_execution",""," @steps_per_execution.setter"," def steps_per_execution(self, value):"," if self._steps_per_execution is None:"," self._configure_steps_per_execution(value)"," else:"," self._steps_per_execution.assign(value)",""," @property"," def jit_compile(self):"," \"\"\"Specify whether to compile the model with XLA.",""," [XLA](https://www.tensorflow.org/xla) is an optimizing compiler"," for machine learning. `jit_compile` is not enabled by default."," Note that `jit_compile=True` may not necessarily work for all models.",""," For more information on supported operations please refer to the"," [XLA documentation](https://www.tensorflow.org/xla). Also refer to"," [known XLA issues](https://www.tensorflow.org/xla/known_issues)"," for more details."," \"\"\""," return self._jit_compile",""," @jit_compile.setter"," def jit_compile(self, value):"," # Function remains cached with previous jit_compile settings"," if self._jit_compile == value:"," # Avoid resetting compiler cache if possible if the value is the"," # same"," return"," # Check if TensorFlow is compiled with XLA before setting the value"," if value and not tf_utils.can_jit_compile(warn=True):"," self._jit_compile = False"," return",""," self._jit_compile = value"," # Setting `jit_compile` should invalidate previously cached functions."," self._reset_compile_cache()",""," @property"," def distribute_reduction_method(self):"," \"\"\"The method employed to reduce per-replica values during training.",""," Unless specified, the value \"auto\" will be assumed, indicating that"," the reduction strategy should be chosen based on the current"," running environment."," See `reduce_per_replica` function for more details.",""," \"\"\""," return self._distribute_reduction_method or \"auto\"",""," @distribute_reduction_method.setter"," def distribute_reduction_method(self, value):"," self._distribute_reduction_method = value",""," def _validate_target_and_loss(self, y, loss):"," \"\"\"Raises error if target or loss is not found.",""," This method verifies that the target and loss are properly populated"," when applicable, or raises errors.",""," Args:"," y: the target for training."," loss: the total loss tensor including loss added via `compile` and"," `add_loss`."," \"\"\"",""," # `self.loss` references the loss added via `compile` call. If users"," # have provided such, the target must be provided; otherwise it's a user"," # error. Note that `self.loss` does not include losses added via"," # `add_loss`, and it is a valid use when such loss from `add_loss`"," # exists and target does not."," if self.loss and y is None:"," raise ValueError("," \"Target data is missing. Your model was compiled with \""," f\"loss={self.loss}, \""," \"and therefore expects target data to be provided in `fit()`.\""," )",""," # For training, there must be compiled loss or regularization loss to"," # exist in order to apply the gradients. If one is not found, it means"," # no loss was supplied via `compile` or `add_loss`."," elif loss is None:"," raise ValueError("," \"No loss found. You may have forgotten to provide a `loss` \""," \"argument in the `compile()` method.\""," )",""," def train_step(self, data):"," \"\"\"The logic for one training step.",""," This method can be overridden to support custom training logic."," For concrete examples of how to override this method see"," [Customizing what happens in fit]("," https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)."," This method is called by `Model.make_train_function`.",""," This method should contain the mathematical logic for one step of"," training. This typically includes the forward pass, loss calculation,"," backpropagation, and metric updates.",""," Configuration details for *how* this logic is run (e.g. `tf.function`"," and `tf.distribute.Strategy` settings), should be left to"," `Model.make_train_function`, which can also be overridden.",""," Args:"," data: A nested structure of `Tensor`s.",""," Returns:"," A `dict` containing values that will be passed to"," `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the"," values of the `Model`'s metrics are returned. Example:"," `{'loss': 0.2, 'accuracy': 0.7}`."," \"\"\""," x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)"," # Run forward pass."," with tf.GradientTape() as tape:"," y_pred = self(x, training=True)"," loss = self.compute_loss(x, y, y_pred, sample_weight)"," self._validate_target_and_loss(y, loss)"," # Run backwards pass."," self.optimizer.minimize(loss, self.trainable_variables, tape=tape)"," return self.compute_metrics(x, y, y_pred, sample_weight)",""," def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None):"," \"\"\"Compute the total loss, validate it, and return it.",""," Subclasses can optionally override this method to provide custom loss"," computation logic.",""," Example:"," ```python"," class MyModel(tf.keras.Model):",""," def __init__(self, *args, **kwargs):"," super(MyModel, self).__init__(*args, **kwargs)"," self.loss_tracker = tf.keras.metrics.Mean(name='loss')",""," def compute_loss(self, x, y, y_pred, sample_weight):"," loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y))"," loss += tf.add_n(self.losses)"," self.loss_tracker.update_state(loss)"," return loss",""," def reset_metrics(self):"," self.loss_tracker.reset_states()",""," @property"," def metrics(self):"," return [self.loss_tracker]",""," tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,))"," dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)",""," inputs = tf.keras.layers.Input(shape=(10,), name='my_input')"," outputs = tf.keras.layers.Dense(10)(inputs)"," model = MyModel(inputs, outputs)"," model.add_loss(tf.reduce_sum(outputs))",""," optimizer = tf.keras.optimizers.SGD()"," model.compile(optimizer, loss='mse', steps_per_execution=10)"," model.fit(dataset, epochs=2, steps_per_epoch=10)"," print('My custom loss: ', model.loss_tracker.result().numpy())"," ```",""," Args:"," x: Input data."," y: Target data."," y_pred: Predictions returned by the model (output of `model(x)`)"," sample_weight: Sample weights for weighting the loss function.",""," Returns:"," The total loss as a `tf.Tensor`, or `None` if no loss results (which"," is the case when called by `Model.test_step`)."," \"\"\""," del x # The default implementation does not use `x`."," return self.compiled_loss("," y, y_pred, sample_weight, regularization_losses=self.losses"," )",""," def compute_metrics(self, x, y, y_pred, sample_weight):"," \"\"\"Update metric states and collect all metrics to be returned.",""," Subclasses can optionally override this method to provide custom metric"," updating and collection logic.",""," Example:"," ```python"," class MyModel(tf.keras.Sequential):",""," def compute_metrics(self, x, y, y_pred, sample_weight):",""," # This super call updates `self.compiled_metrics` and returns"," # results for all metrics listed in `self.metrics`."," metric_results = super(MyModel, self).compute_metrics("," x, y, y_pred, sample_weight)",""," # Note that `self.custom_metric` is not listed in `self.metrics`."," self.custom_metric.update_state(x, y, y_pred, sample_weight)"," metric_results['custom_metric_name'] = self.custom_metric.result()"," return metric_results"," ```",""," Args:"," x: Input data."," y: Target data."," y_pred: Predictions returned by the model (output of `model.call(x)`)"," sample_weight: Sample weights for weighting the loss function.",""," Returns:"," A `dict` containing values that will be passed to"," `tf.keras.callbacks.CallbackList.on_train_batch_end()`. Typically, the"," values of the metrics listed in `self.metrics` are returned. Example:"," `{'loss': 0.2, 'accuracy': 0.7}`."," \"\"\""," del x # The default implementation does not use `x`."," self.compiled_metrics.update_state(y, y_pred, sample_weight)"," return self.get_metrics_result()",""," def get_metrics_result(self):"," \"\"\"Returns the model's metrics values as a dict.",""," If any of the metric result is a dict (containing multiple metrics),"," each of them gets added to the top level returned dict of this method.",""," Returns:"," A `dict` containing values of the metrics listed in `self.metrics`."," Example:"," `{'loss': 0.2, 'accuracy': 0.7}`."," \"\"\""," # Collect metrics to return"," return_metrics = {}"," for metric in self.metrics:"," result = metric.result()"," if isinstance(result, dict):"," return_metrics.update(result)"," else:"," return_metrics[metric.name] = result"," return return_metrics",""," def _validate_and_get_metrics_result(self, logs):"," \"\"\"Returns model metrics as a dict if the keys match with input logs.",""," When the training / evalution is performed with asynchronous steps, such"," as the case with `tf.distribute.ParameterServerStrategy`, the last"," scheduled `train / test_step` may not give the latest metrics because it"," is not guaranteed to be executed the last. This method gets metrics from"," the model directly instead of relying on the return from last step"," function.",""," It logs a warning if the metric results could not be overridden when"," used with `tf.distribute.ParameterServerStrategy`.",""," When the user has custom train / test step functions, the metrics"," returned may be different from `Model.metrics`. In those instances,"," this function will be no-op and return the logs.",""," Args:"," logs: A `dict` of metrics returned by train / test step function.",""," Returns:"," A `dict` containing values of the metrics listed in `self.metrics`"," when logs and model metrics keys match. Otherwise it returns input"," `logs`."," \"\"\""," PSS_WARN_MSG = \"Could not get Model metric results. \\"," Using the results of last step function could lead to incorrect \\"," results when used with ParameterServerStrategy\""," try:"," metric_logs = self.get_metrics_result()"," except TypeError:"," if self._cluster_coordinator:"," logging.warning(PSS_WARN_MSG)"," else:"," # Verify that train / test step logs passed and metric logs have"," # matching keys. Could be different when using custom step functions"," if isinstance(logs, dict) and set(logs.keys()) == set("," metric_logs.keys()"," ):"," logs = tf_utils.sync_to_numpy_or_python_type(metric_logs)"," elif self._cluster_coordinator:"," logging.warning(PSS_WARN_MSG)"," return logs",""," def _aggregate_exact_metrics(self, logs):"," # When doing exact evaluation, `logs` is a list of each data shard's"," # metric variables, which will be used to update the metrics."," for shard_result in logs:"," for metric in self.metrics:"," if metric.name not in shard_result.keys():"," logging.log_first_n("," logging.WARN,"," f\"No matching result found for metric {metric.name}. \""," \"This metric's computed result may be incorrect.\","," 3,"," )"," continue"," metric_result = shard_result[metric.name]"," if len(metric_result) != len(metric.weights):"," raise ValueError("," f\"Expected {len(metric.weights)} variables in result \""," f\"for metric {metric.name}, but found \""," f\"{len(metric_result)}.\""," )"," for weight, val in zip(metric.weights, metric_result):"," weight.assign_add(val)"," return self.get_metrics_result()",""," def make_train_function(self, force=False):"," \"\"\"Creates a function that executes one step of training.",""," This method can be overridden to support custom training logic."," This method is called by `Model.fit` and `Model.train_on_batch`.",""," Typically, this method directly controls `tf.function` and"," `tf.distribute.Strategy` settings, and delegates the actual training"," logic to `Model.train_step`.",""," This function is cached the first time `Model.fit` or"," `Model.train_on_batch` is called. The cache is cleared whenever"," `Model.compile` is called. You can skip the cache and generate again the"," function with `force=True`.",""," Args:"," force: Whether to regenerate the train function and skip the cached"," function if available.",""," Returns:"," Function. The function created by this method should accept a"," `tf.data.Iterator`, and return a `dict` containing values that will"," be passed to `tf.keras.Callbacks.on_train_batch_end`, such as"," `{'loss': 0.2, 'accuracy': 0.7}`."," \"\"\""," if self.train_function is not None and not force:"," return self.train_function",""," def step_function(model, iterator):"," \"\"\"Runs a single training step.\"\"\"",""," def run_step(data):"," outputs = model.train_step(data)"," # Ensure counter is updated only if `train_step` succeeds."," with tf.control_dependencies(_minimum_control_deps(outputs)):"," model._train_counter.assign_add(1)"," return outputs",""," if self.jit_compile:"," run_step = tf.function("," run_step, jit_compile=True, reduce_retracing=True"," )"," data = next(iterator)"," outputs = model.distribute_strategy.run(run_step, args=(data,))"," outputs = reduce_per_replica("," outputs,"," self.distribute_strategy,"," reduction=self.distribute_reduction_method,"," )"," return outputs",""," # Special case if steps_per_execution is one."," if ("," self._steps_per_execution is None"," or self._steps_per_execution.numpy().item() == 1"," and not self.autotune_steps_per_execution"," ):",""," def train_function(iterator):"," \"\"\"Runs a training execution with a single step.\"\"\""," return step_function(self, iterator)",""," if not self.run_eagerly:"," train_function = tf.function("," train_function, reduce_retracing=True"," )"," self.train_tf_function = train_function",""," if self._cluster_coordinator:"," self.train_function = ("," lambda it: self._cluster_coordinator.schedule("," train_function, args=(it,)"," )"," )"," else:"," self.train_function = train_function",""," # If we're using a coordinator, use the value of"," # self._steps_per_execution at the time the function is"," # called/scheduled, and not when it is actually executed."," elif self._cluster_coordinator:",""," def train_function(iterator, steps_per_execution):"," \"\"\"Runs a training execution with multiple steps.\"\"\""," for _ in tf.range(steps_per_execution):"," outputs = step_function(self, iterator)"," return outputs",""," if not self.run_eagerly:"," train_function = tf.function("," train_function, reduce_retracing=True"," )"," self.train_tf_function = train_function",""," self.train_function = lambda it: self._cluster_coordinator.schedule("," train_function, args=(it, self._steps_per_execution.value())"," )"," else:",""," def train_function(iterator):"," \"\"\"Runs a training execution with multiple steps.\"\"\""," for _ in tf.range(self._steps_per_execution):"," outputs = step_function(self, iterator)"," return outputs",""," if not self.run_eagerly:"," train_function = tf.function("," train_function, reduce_retracing=True"," )"," self.train_tf_function = train_function"," self.train_function = train_function",""," return self.train_function",""," @traceback_utils.filter_traceback"," def fit("," self,"," x=None,"," y=None,"," batch_size=None,"," epochs=1,"," verbose=\"auto\","," callbacks=None,"," validation_split=0.0,"," validation_data=None,"," shuffle=True,"," class_weight=None,"," sample_weight=None,"," initial_epoch=0,"," steps_per_epoch=None,"," validation_steps=None,"," validation_batch_size=None,"," validation_freq=1,"," max_queue_size=10,"," workers=1,"," use_multiprocessing=False,"," ):"," \"\"\"Trains the model for a fixed number of epochs (dataset iterations).",""," Args:"," x: Input data. It could be:"," - A Numpy array (or array-like), or a list of arrays"," (in case the model has multiple inputs)."," - A TensorFlow tensor, or a list of tensors"," (in case the model has multiple inputs)."," - A dict mapping input names to the corresponding array/tensors,"," if the model has named inputs."," - A `tf.data` dataset. Should return a tuple"," of either `(inputs, targets)` or"," `(inputs, targets, sample_weights)`."," - A generator or `keras.utils.Sequence` returning `(inputs,"," targets)` or `(inputs, targets, sample_weights)`."," - A `tf.keras.utils.experimental.DatasetCreator`, which wraps a"," callable that takes a single argument of type"," `tf.distribute.InputContext`, and returns a `tf.data.Dataset`."," `DatasetCreator` should be used when users prefer to specify the"," per-replica batching and sharding logic for the `Dataset`."," See `tf.keras.utils.experimental.DatasetCreator` doc for more"," information."," A more detailed description of unpacking behavior for iterator"," types (Dataset, generator, Sequence) is given below. If these"," include `sample_weights` as a third component, note that sample"," weighting applies to the `weighted_metrics` argument but not the"," `metrics` argument in `compile()`. If using"," `tf.distribute.experimental.ParameterServerStrategy`, only"," `DatasetCreator` type is supported for `x`."," y: Target data. Like the input data `x`,"," it could be either Numpy array(s) or TensorFlow tensor(s)."," It should be consistent with `x` (you cannot have Numpy inputs and"," tensor targets, or inversely). If `x` is a dataset, generator,"," or `keras.utils.Sequence` instance, `y` should"," not be specified (since targets will be obtained from `x`)."," batch_size: Integer or `None`."," Number of samples per gradient update."," If unspecified, `batch_size` will default to 32."," Do not specify the `batch_size` if your data is in the"," form of datasets, generators, or `keras.utils.Sequence`"," instances (since they generate batches)."," epochs: Integer. Number of epochs to train the model."," An epoch is an iteration over the entire `x` and `y`"," data provided"," (unless the `steps_per_epoch` flag is set to"," something other than None)."," Note that in conjunction with `initial_epoch`,"," `epochs` is to be understood as \"final epoch\"."," The model is not trained for a number of iterations"," given by `epochs`, but merely until the epoch"," of index `epochs` is reached."," verbose: 'auto', 0, 1, or 2. Verbosity mode."," 0 = silent, 1 = progress bar, 2 = one line per epoch."," 'auto' becomes 1 for most cases, but 2 when used with"," `ParameterServerStrategy`. Note that the progress bar is not"," particularly useful when logged to a file, so verbose=2 is"," recommended when not running interactively (eg, in a production"," environment). Defaults to 'auto'."," callbacks: List of `keras.callbacks.Callback` instances."," List of callbacks to apply during training."," See `tf.keras.callbacks`. Note"," `tf.keras.callbacks.ProgbarLogger` and"," `tf.keras.callbacks.History` callbacks are created automatically"," and need not be passed into `model.fit`."," `tf.keras.callbacks.ProgbarLogger` is created or not based on"," `verbose` argument to `model.fit`."," Callbacks with batch-level calls are currently unsupported with"," `tf.distribute.experimental.ParameterServerStrategy`, and users"," are advised to implement epoch-level calls instead with an"," appropriate `steps_per_epoch` value."," validation_split: Float between 0 and 1."," Fraction of the training data to be used as validation data."," The model will set apart this fraction of the training data,"," will not train on it, and will evaluate"," the loss and any model metrics"," on this data at the end of each epoch."," The validation data is selected from the last samples"," in the `x` and `y` data provided, before shuffling. This"," argument is not supported when `x` is a dataset, generator or"," `keras.utils.Sequence` instance."," If both `validation_data` and `validation_split` are provided,"," `validation_data` will override `validation_split`."," `validation_split` is not yet supported with"," `tf.distribute.experimental.ParameterServerStrategy`."," validation_data: Data on which to evaluate"," the loss and any model metrics at the end of each epoch."," The model will not be trained on this data. Thus, note the fact"," that the validation loss of data provided using"," `validation_split` or `validation_data` is not affected by"," regularization layers like noise and dropout."," `validation_data` will override `validation_split`."," `validation_data` could be:"," - A tuple `(x_val, y_val)` of Numpy arrays or tensors."," - A tuple `(x_val, y_val, val_sample_weights)` of NumPy"," arrays."," - A `tf.data.Dataset`."," - A Python generator or `keras.utils.Sequence` returning"," `(inputs, targets)` or `(inputs, targets, sample_weights)`."," `validation_data` is not yet supported with"," `tf.distribute.experimental.ParameterServerStrategy`."," shuffle: Boolean (whether to shuffle the training data"," before each epoch) or str (for 'batch'). This argument is"," ignored when `x` is a generator or an object of tf.data.Dataset."," 'batch' is a special option for dealing"," with the limitations of HDF5 data; it shuffles in batch-sized"," chunks. Has no effect when `steps_per_epoch` is not `None`."," class_weight: Optional dictionary mapping class indices (integers)"," to a weight (float) value, used for weighting the loss function"," (during training only)."," This can be useful to tell the model to"," \"pay more attention\" to samples from"," an under-represented class. When `class_weight` is specified"," and targets have a rank of 2 or greater, either `y` must be"," one-hot encoded, or an explicit final dimension of `1` must"," be included for sparse class labels."," sample_weight: Optional Numpy array of weights for"," the training samples, used for weighting the loss function"," (during training only). You can either pass a flat (1D)"," Numpy array with the same length as the input samples"," (1:1 mapping between weights and samples),"," or in the case of temporal data,"," you can pass a 2D array with shape"," `(samples, sequence_length)`,"," to apply a different weight to every timestep of every sample."," This argument is not supported when `x` is a dataset, generator,"," or `keras.utils.Sequence` instance, instead provide the"," sample_weights as the third element of `x`."," Note that sample weighting does not apply to metrics specified"," via the `metrics` argument in `compile()`. To apply sample"," weighting to your metrics, you can specify them via the"," `weighted_metrics` in `compile()` instead."," initial_epoch: Integer."," Epoch at which to start training"," (useful for resuming a previous training run)."," steps_per_epoch: Integer or `None`."," Total number of steps (batches of samples)"," before declaring one epoch finished and starting the"," next epoch. When training with input tensors such as"," TensorFlow data tensors, the default `None` is equal to"," the number of samples in your dataset divided by"," the batch size, or 1 if that cannot be determined. If x is a"," `tf.data` dataset, and 'steps_per_epoch'"," is None, the epoch will run until the input dataset is"," exhausted. When passing an infinitely repeating dataset, you"," must specify the `steps_per_epoch` argument. If"," `steps_per_epoch=-1` the training will run indefinitely with an"," infinitely repeating dataset. This argument is not supported"," with array inputs."," When using `tf.distribute.experimental.ParameterServerStrategy`:"," * `steps_per_epoch=None` is not supported."," validation_steps: Only relevant if `validation_data` is provided and"," is a `tf.data` dataset. Total number of steps (batches of"," samples) to draw before stopping when performing validation"," at the end of every epoch. If 'validation_steps' is None,"," validation will run until the `validation_data` dataset is"," exhausted. In the case of an infinitely repeated dataset, it"," will run into an infinite loop. If 'validation_steps' is"," specified and only part of the dataset will be consumed, the"," evaluation will start from the beginning of the dataset at each"," epoch. This ensures that the same validation samples are used"," every time."," validation_batch_size: Integer or `None`."," Number of samples per validation batch."," If unspecified, will default to `batch_size`."," Do not specify the `validation_batch_size` if your data is in"," the form of datasets, generators, or `keras.utils.Sequence`"," instances (since they generate batches)."," validation_freq: Only relevant if validation data is provided."," Integer or `collections.abc.Container` instance (e.g. list, tuple,"," etc.). If an integer, specifies how many training epochs to run"," before a new validation run is performed, e.g. `validation_freq=2`"," runs validation every 2 epochs. If a Container, specifies the"," epochs on which to run validation, e.g."," `validation_freq=[1, 2, 10]` runs validation at the end of the"," 1st, 2nd, and 10th epochs."," max_queue_size: Integer. Used for generator or"," `keras.utils.Sequence` input only. Maximum size for the generator"," queue. If unspecified, `max_queue_size` will default to 10."," workers: Integer. Used for generator or `keras.utils.Sequence` input"," only. Maximum number of processes to spin up"," when using process-based threading. If unspecified, `workers`"," will default to 1."," use_multiprocessing: Boolean. Used for generator or"," `keras.utils.Sequence` input only. If `True`, use process-based"," threading. If unspecified, `use_multiprocessing` will default to"," `False`. Note that because this implementation relies on"," multiprocessing, you should not pass non-pickleable arguments to"," the generator as they can't be passed easily to children"," processes.",""," Unpacking behavior for iterator-like inputs:"," A common pattern is to pass a tf.data.Dataset, generator, or"," tf.keras.utils.Sequence to the `x` argument of fit, which will in fact"," yield not only features (x) but optionally targets (y) and sample"," weights. TF-Keras requires that the output of such iterator-likes be"," unambiguous. The iterator should return a tuple of length 1, 2, or 3,"," where the optional second and third elements will be used for y and"," sample_weight respectively. Any other type provided will be wrapped in"," a length one tuple, effectively treating everything as 'x'. When"," yielding dicts, they should still adhere to the top-level tuple"," structure."," e.g. `({\"x0\": x0, \"x1\": x1}, y)`. TF-Keras will not attempt to"," separate features, targets, and weights from the keys of a single"," dict."," A notable unsupported data type is the namedtuple. The reason is"," that it behaves like both an ordered datatype (tuple) and a mapping"," datatype (dict). So given a namedtuple of the form:"," `namedtuple(\"example_tuple\", [\"y\", \"x\"])`"," it is ambiguous whether to reverse the order of the elements when"," interpreting the value. Even worse is a tuple of the form:"," `namedtuple(\"other_tuple\", [\"x\", \"y\", \"z\"])`"," where it is unclear if the tuple was intended to be unpacked into x,"," y, and sample_weight or passed through as a single element to `x`. As"," a result the data processing code will simply raise a ValueError if it"," encounters a namedtuple. (Along with instructions to remedy the"," issue.)",""," Returns:"," A `History` object. Its `History.history` attribute is"," a record of training loss values and metrics values"," at successive epochs, as well as validation loss values"," and validation metrics values (if applicable).",""," Raises:"," RuntimeError: 1. If the model was never compiled or,"," 2. If `model.fit` is wrapped in `tf.function`.",""," ValueError: In case of mismatch between the provided input data"," and what the model expects or when the input data is empty."," \"\"\""," # Legacy graph support is contained in `training_v1.Model`."," version_utils.disallow_legacy_graph(\"Model\", \"fit\")"," self._assert_compile_was_called()"," self._check_call_args(\"fit\")"," _disallow_inside_tf_function(\"fit\")",""," verbose = _get_verbosity(verbose, self.distribute_strategy)",""," if validation_split and validation_data is None:"," # Create the validation data using the training data. Only supported"," # for `Tensor` and `NumPy` input."," ("," x,"," y,"," sample_weight,"," ), validation_data = data_adapter.train_validation_split("," (x, y, sample_weight), validation_split=validation_split"," )",""," if validation_data:"," ("," val_x,"," val_y,"," val_sample_weight,"," ) = data_adapter.unpack_x_y_sample_weight(validation_data)",""," if self.distribute_strategy._should_use_with_coordinator:"," self._cluster_coordinator = ("," tf.distribute.experimental.coordinator.ClusterCoordinator("," self.distribute_strategy"," )"," )",""," with self.distribute_strategy.scope(), training_utils.RespectCompiledTrainableState( # noqa: E501"," self"," ):"," # Creates a `tf.data.Dataset` and handles batch and epoch iteration."," data_handler = data_adapter.get_data_handler("," x=x,"," y=y,"," sample_weight=sample_weight,"," batch_size=batch_size,"," steps_per_epoch=steps_per_epoch,"," initial_epoch=initial_epoch,"," epochs=epochs,"," shuffle=shuffle,"," class_weight=class_weight,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," model=self,"," steps_per_execution=self._steps_per_execution,"," )",""," # Container that configures and calls `tf.keras.Callback`s."," if not isinstance(callbacks, callbacks_module.CallbackList):"," callbacks = callbacks_module.CallbackList("," callbacks,"," add_history=True,"," add_progbar=verbose != 0,"," model=self,"," verbose=verbose,"," epochs=epochs,"," steps=data_handler.inferred_steps,"," )",""," self.stop_training = False"," self.train_function = self.make_train_function()"," self._train_counter.assign(0)"," callbacks.on_train_begin()"," training_logs = None"," if self.autotune_steps_per_execution:"," self._steps_per_execution_tuner.start()"," # Handle fault-tolerance for multi-worker."," # TODO(omalleyt): Fix the ordering issues that mean this has to"," # happen after `callbacks.on_train_begin`."," steps_per_epoch_inferred = ("," steps_per_epoch or data_handler.inferred_steps"," )"," ("," data_handler._initial_epoch,"," data_handler._initial_step,"," ) = self._maybe_load_initial_counters_from_ckpt("," steps_per_epoch_inferred, initial_epoch"," )"," logs = None"," for epoch, iterator in data_handler.enumerate_epochs():"," self.reset_metrics()"," callbacks.on_epoch_begin(epoch)"," with data_handler.catch_stop_iteration():"," for step in data_handler.steps():"," with tf.profiler.experimental.Trace("," \"train\","," epoch_num=epoch,"," step_num=step,"," batch_size=batch_size,"," _r=1,"," ):"," callbacks.on_train_batch_begin(step)"," tmp_logs = self.train_function(iterator)"," if data_handler.should_sync:"," context.async_wait()"," # No error, now safe to assign to logs."," logs = tmp_logs"," end_step = step + data_handler.step_increment"," callbacks.on_train_batch_end(end_step, logs)"," if self.stop_training:"," break",""," logs = tf_utils.sync_to_numpy_or_python_type(logs)"," if logs is None:"," raise ValueError("," \"Unexpected result of `train_function` \""," \"(Empty logs). This could be due to issues in input \""," \"pipeline that resulted in an empty dataset. \""," \"Otherwise, please use \""," \"`Model.compile(..., run_eagerly=True)`, or \""," \"`tf.config.run_functions_eagerly(True)` for more \""," \"information of where went wrong, or file a \""," \"issue/bug to `tf.keras`.\""," )"," # Override with model metrics instead of last step logs"," logs = self._validate_and_get_metrics_result(logs)"," epoch_logs = copy.copy(logs)",""," # Run validation."," if validation_data and self._should_eval("," epoch, validation_freq"," ):"," if self._pss_evaluation_shards:"," self._disallow_exact_eval_with_add_metrics()"," # Create data_handler for evaluation and cache it."," if getattr(self, \"_eval_data_handler\", None) is None:"," self._eval_data_handler = data_adapter.get_data_handler("," x=val_x,"," y=val_y,"," sample_weight=val_sample_weight,"," batch_size=validation_batch_size or batch_size,"," steps_per_epoch=validation_steps,"," initial_epoch=0,"," epochs=1,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," model=self,"," steps_per_execution=self._steps_per_execution,"," pss_evaluation_shards=self._pss_evaluation_shards,"," )"," val_logs = self.evaluate("," x=val_x,"," y=val_y,"," sample_weight=val_sample_weight,"," batch_size=validation_batch_size or batch_size,"," steps=validation_steps,"," callbacks=callbacks,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," return_dict=True,"," _use_cached_eval_dataset=True,"," )"," val_logs = {"," \"val_\" + name: val for name, val in val_logs.items()"," }"," epoch_logs.update(val_logs)",""," callbacks.on_epoch_end(epoch, epoch_logs)"," training_logs = epoch_logs"," if self.stop_training:"," break",""," if isinstance(self.optimizer, optimizer.Optimizer) and epochs \u003e 0:"," self.optimizer.finalize_variable_values("," self.trainable_variables"," )",""," # If eval data_handler exists, delete it after all epochs are done."," if getattr(self, \"_eval_data_handler\", None) is not None:"," del self._eval_data_handler"," if self.autotune_steps_per_execution:"," self._steps_per_execution_tuner.stop()"," callbacks.on_train_end(logs=training_logs)"," return self.history",""," def test_step(self, data):"," \"\"\"The logic for one evaluation step.",""," This method can be overridden to support custom evaluation logic."," This method is called by `Model.make_test_function`.",""," This function should contain the mathematical logic for one step of"," evaluation."," This typically includes the forward pass, loss calculation, and metrics"," updates.",""," Configuration details for *how* this logic is run (e.g. `tf.function`"," and `tf.distribute.Strategy` settings), should be left to"," `Model.make_test_function`, which can also be overridden.",""," Args:"," data: A nested structure of `Tensor`s.",""," Returns:"," A `dict` containing values that will be passed to"," `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the"," values of the `Model`'s metrics are returned."," \"\"\""," x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)",""," y_pred = self(x, training=False)"," # Updates stateful loss metrics."," self.compute_loss(x, y, y_pred, sample_weight)"," return self.compute_metrics(x, y, y_pred, sample_weight)",""," def _make_test_function_exact(self):"," if getattr(self, \"_shard_test_function\", None):"," return self._shard_test_function",""," def step_function(batch):"," def run_step(data):"," # TODO(b/272050910): Use sample_weight for weighted metrics."," x, y, sample_weight = data_adapter.unpack_x_y_sample_weight("," data"," )"," y_pred = self(x, training=False)"," return x, y, y_pred, sample_weight",""," if self._jit_compile:"," run_step = tf.function("," run_step, jit_compile=True, reduce_retracing=True"," )",""," outputs = self.distribute_strategy.run(run_step, args=(batch,))"," outputs = reduce_per_replica("," outputs,"," self.distribute_strategy,"," reduction=self.distribute_reduction_method,"," )"," return outputs",""," def shard_test_function(dataset, total_shards, shard_idx):"," # Copy loss and metric variables to the worker and work with them"," # locally. This ensures each shard function is atomic: if a worker"," # is preempted, the intermediate progress is discarded and that"," # shard is retried. This in turn guarantees exactly-once visitation."," local_unweighted_metrics, local_weighted_metrics = [], []"," with tf_utils.with_metric_local_vars_scope():"," # TODO(jmullenbach): implement and use a clone for"," # `MetricsContainer` and use its `update_state` method directly."," for metric in self.compiled_metrics.unweighted_metrics:"," if metric is not None:"," local_unweighted_metrics.append("," base_metric.clone_metric(metric)"," )"," for metric in self.compiled_metrics.weighted_metrics:"," if metric is not None:"," local_weighted_metrics.append("," base_metric.clone_metric(metric)"," )"," local_loss = compile_utils.LossesContainer.from_config("," self.compiled_loss.get_config()"," )",""," dataset = input_ops.auto_shard_dataset("," dataset, total_shards, shard_idx"," )"," iterator = iter(dataset)"," with distribute_utils.cache_variable_reads():"," for batch in iterator:"," x, y, y_pred, sample_weight = step_function(batch)"," for weighted_metric in local_weighted_metrics:"," weighted_metric.update_state(y, y_pred, sample_weight)"," for unweighted_metric in local_unweighted_metrics:"," unweighted_metric.update_state(y, y_pred)"," local_loss(y, y_pred, sample_weight)"," local_metrics = ("," local_unweighted_metrics"," + local_weighted_metrics"," + local_loss.metrics"," )"," outputs = {metric.name: metric.weights for metric in local_metrics}"," with tf.control_dependencies(_minimum_control_deps(outputs)):"," self._test_counter.assign_add(1)"," return outputs",""," if not self.run_eagerly:"," shard_test_function = tf.function("," shard_test_function, reduce_retracing=True"," )",""," self._shard_test_function = ("," lambda *args: self._cluster_coordinator.schedule("," shard_test_function,"," args=args,"," )"," )"," return self._shard_test_function",""," def make_test_function(self, force=False):"," \"\"\"Creates a function that executes one step of evaluation.",""," This method can be overridden to support custom evaluation logic."," This method is called by `Model.evaluate` and `Model.test_on_batch`.",""," Typically, this method directly controls `tf.function` and"," `tf.distribute.Strategy` settings, and delegates the actual evaluation"," logic to `Model.test_step`.",""," This function is cached the first time `Model.evaluate` or"," `Model.test_on_batch` is called. The cache is cleared whenever"," `Model.compile` is called. You can skip the cache and generate again the"," function with `force=True`.",""," Args:"," force: Whether to regenerate the test function and skip the cached"," function if available.",""," Returns:"," Function. The function created by this method should accept a"," `tf.data.Iterator`, and return a `dict` containing values that will"," be passed to `tf.keras.Callbacks.on_test_batch_end`."," \"\"\""," if self.test_function is not None and not force:"," return self.test_function",""," def step_function(model, iterator):"," \"\"\"Runs a single evaluation step.\"\"\"",""," def run_step(data):"," outputs = model.test_step(data)"," # Ensure counter is updated only if `test_step` succeeds."," with tf.control_dependencies(_minimum_control_deps(outputs)):"," model._test_counter.assign_add(1)"," return outputs",""," if self.jit_compile:"," run_step = tf.function("," run_step, jit_compile=True, reduce_retracing=True"," )",""," data = next(iterator)"," outputs = model.distribute_strategy.run(run_step, args=(data,))"," outputs = reduce_per_replica("," outputs,"," self.distribute_strategy,"," reduction=self.distribute_reduction_method,"," )"," return outputs",""," # Special case if steps_per_execution is one."," if ("," self._steps_per_execution is None"," or self._steps_per_execution.numpy().item() == 1"," and not self.autotune_steps_per_execution"," ):",""," def test_function(iterator):"," \"\"\"Runs a test execution with a single step.\"\"\""," return step_function(self, iterator)",""," if not self.run_eagerly:"," test_function = tf.function("," test_function, reduce_retracing=True"," )",""," if self._cluster_coordinator:"," self.test_function = ("," lambda it: self._cluster_coordinator.schedule("," test_function, args=(it,)"," )"," )"," else:"," self.test_function = test_function",""," # If we're using a coordinator, use the value of"," # self._steps_per_execution at the time the function is"," # called/scheduled, and not when it is actually executed."," elif self._cluster_coordinator:",""," def test_function(iterator, steps_per_execution):"," \"\"\"Runs a test execution with multiple steps.\"\"\""," for _ in tf.range(steps_per_execution):"," outputs = step_function(self, iterator)"," return outputs",""," if not self.run_eagerly:"," test_function = tf.function("," test_function, reduce_retracing=True"," )",""," self.test_function = lambda it: self._cluster_coordinator.schedule("," test_function, args=(it, self._steps_per_execution.value())"," )"," else:",""," def test_function(iterator):"," \"\"\"Runs a test execution with multiple steps.\"\"\""," for _ in tf.range(self._steps_per_execution):"," outputs = step_function(self, iterator)"," return outputs",""," if not self.run_eagerly:"," test_function = tf.function("," test_function, reduce_retracing=True"," )"," self.test_function = test_function",""," return self.test_function",""," @traceback_utils.filter_traceback"," def evaluate("," self,"," x=None,"," y=None,"," batch_size=None,"," verbose=\"auto\","," sample_weight=None,"," steps=None,"," callbacks=None,"," max_queue_size=10,"," workers=1,"," use_multiprocessing=False,"," return_dict=False,"," **kwargs,"," ):"," \"\"\"Returns the loss value \u0026 metrics values for the model in test mode.",""," Computation is done in batches (see the `batch_size` arg.)",""," Args:"," x: Input data. It could be:"," - A Numpy array (or array-like), or a list of arrays"," (in case the model has multiple inputs)."," - A TensorFlow tensor, or a list of tensors"," (in case the model has multiple inputs)."," - A dict mapping input names to the corresponding array/tensors,"," if the model has named inputs."," - A `tf.data` dataset. Should return a tuple"," of either `(inputs, targets)` or"," `(inputs, targets, sample_weights)`."," - A generator or `keras.utils.Sequence` returning `(inputs,"," targets)` or `(inputs, targets, sample_weights)`."," A more detailed description of unpacking behavior for iterator"," types (Dataset, generator, Sequence) is given in the `Unpacking"," behavior for iterator-like inputs` section of `Model.fit`."," y: Target data. Like the input data `x`, it could be either Numpy"," array(s) or TensorFlow tensor(s). It should be consistent with `x`"," (you cannot have Numpy inputs and tensor targets, or inversely)."," If `x` is a dataset, generator or `keras.utils.Sequence` instance,"," `y` should not be specified (since targets will be obtained from"," the iterator/dataset)."," batch_size: Integer or `None`. Number of samples per batch of"," computation. If unspecified, `batch_size` will default to 32. Do"," not specify the `batch_size` if your data is in the form of a"," dataset, generators, or `keras.utils.Sequence` instances (since"," they generate batches)."," verbose: `\"auto\"`, 0, 1, or 2. Verbosity mode."," 0 = silent, 1 = progress bar, 2 = single line."," `\"auto\"` becomes 1 for most cases, and to 2 when used with"," `ParameterServerStrategy`. Note that the progress bar is not"," particularly useful when logged to a file, so `verbose=2` is"," recommended when not running interactively (e.g. in a production"," environment). Defaults to 'auto'."," sample_weight: Optional Numpy array of weights for the test samples,"," used for weighting the loss function. You can either pass a flat"," (1D) Numpy array with the same length as the input samples"," (1:1 mapping between weights and samples), or in the case of"," temporal data, you can pass a 2D array with shape `(samples,"," sequence_length)`, to apply a different weight to every"," timestep of every sample. This argument is not supported when"," `x` is a dataset, instead pass sample weights as the third"," element of `x`."," steps: Integer or `None`. Total number of steps (batches of samples)"," before declaring the evaluation round finished. Ignored with the"," default value of `None`. If x is a `tf.data` dataset and `steps`"," is None, 'evaluate' will run until the dataset is exhausted. This"," argument is not supported with array inputs."," callbacks: List of `keras.callbacks.Callback` instances. List of"," callbacks to apply during evaluation. See"," [callbacks](https://www.tensorflow.org/api_docs/python/tf/tf_keras/callbacks)."," max_queue_size: Integer. Used for generator or"," `keras.utils.Sequence` input only. Maximum size for the generator"," queue. If unspecified, `max_queue_size` will default to 10."," workers: Integer. Used for generator or `keras.utils.Sequence` input"," only. Maximum number of processes to spin up when using"," process-based threading. If unspecified, `workers` will default to"," 1."," use_multiprocessing: Boolean. Used for generator or"," `keras.utils.Sequence` input only. If `True`, use process-based"," threading. If unspecified, `use_multiprocessing` will default to"," `False`. Note that because this implementation relies on"," multiprocessing, you should not pass non-pickleable arguments to"," the generator as they can't be passed easily to children"," processes."," return_dict: If `True`, loss and metric results are returned as a"," dict, with each key being the name of the metric. If `False`, they"," are returned as a list."," **kwargs: Unused at this time.",""," See the discussion of `Unpacking behavior for iterator-like inputs` for"," `Model.fit`.",""," Returns:"," Scalar test loss (if the model has a single output and no metrics)"," or list of scalars (if the model has multiple outputs"," and/or metrics). The attribute `model.metrics_names` will give you"," the display labels for the scalar outputs.",""," Raises:"," RuntimeError: If `model.evaluate` is wrapped in a `tf.function`."," \"\"\""," version_utils.disallow_legacy_graph(\"Model\", \"evaluate\")"," self._assert_compile_was_called()"," self._check_call_args(\"evaluate\")"," self._check_sample_weight_warning(x, sample_weight)"," _disallow_inside_tf_function(\"evaluate\")"," use_cached_eval_dataset = kwargs.pop(\"_use_cached_eval_dataset\", False)"," if kwargs:"," raise TypeError(f\"Invalid keyword arguments: {list(kwargs.keys())}\")",""," if self.distribute_strategy._should_use_with_coordinator:"," self._cluster_coordinator = ("," tf.distribute.experimental.coordinator.ClusterCoordinator("," self.distribute_strategy"," )"," )",""," verbose = _get_verbosity(verbose, self.distribute_strategy)"," if self._pss_evaluation_shards:"," self._disallow_exact_eval_with_add_metrics()"," with self.distribute_strategy.scope():"," # Use cached evaluation data only when it's called in `Model.fit`"," if ("," use_cached_eval_dataset"," and getattr(self, \"_eval_data_handler\", None) is not None"," ):"," data_handler = self._eval_data_handler"," else:"," # Creates a `tf.data.Dataset` and handles batch and epoch"," # iteration."," data_handler = data_adapter.get_data_handler("," x=x,"," y=y,"," sample_weight=sample_weight,"," batch_size=batch_size,"," steps_per_epoch=steps,"," initial_epoch=0,"," epochs=1,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," model=self,"," steps_per_execution=self._steps_per_execution,"," pss_evaluation_shards=self._pss_evaluation_shards,"," )",""," # Container that configures and calls `tf.keras.Callback`s."," if not isinstance(callbacks, callbacks_module.CallbackList):"," callbacks = callbacks_module.CallbackList("," callbacks,"," add_history=True,"," add_progbar=verbose != 0,"," model=self,"," verbose=verbose,"," epochs=1,"," steps=data_handler.inferred_steps,"," )",""," # Initialize to prevent errors if 0 epochs are evaluated."," logs = {}",""," test_function_runner = self._get_test_function_runner(callbacks)"," self._test_counter.assign(0)"," callbacks.on_test_begin()"," if self.autotune_steps_per_execution:"," self._steps_per_execution_tuner.start()"," for ("," _,"," dataset_or_iterator,"," ) in data_handler.enumerate_epochs(): # Single epoch."," self.reset_metrics()"," with data_handler.catch_stop_iteration():"," for step in data_handler.steps():"," with tf.profiler.experimental.Trace("," \"test\", step_num=step, _r=1"," ):"," callbacks.on_test_batch_begin(step)"," logs = test_function_runner.run_step("," dataset_or_iterator,"," data_handler,"," step,"," self._pss_evaluation_shards,"," )",""," logs = tf_utils.sync_to_numpy_or_python_type(logs)"," # Override with model metrics instead of last step logs"," if self._pss_evaluation_shards:"," logs = self._aggregate_exact_metrics(logs)"," else:"," logs = self._validate_and_get_metrics_result(logs)"," if self.autotune_steps_per_execution:"," self._steps_per_execution_tuner.stop()"," callbacks.on_test_end(logs=logs)",""," if return_dict:"," return logs"," else:"," return flatten_metrics_in_order(logs, self.metrics_names)",""," def _disallow_exact_eval_with_add_metrics(self):"," metrics_from_add_metric = ["," metric"," for layer in self._flatten_layers()"," for metric in layer._metrics"," ]"," compiled_metrics = self.compiled_metrics.metrics"," if any("," ["," metric not in compiled_metrics"," for metric in metrics_from_add_metric"," ]"," ):"," raise ValueError("," \"Detected that a metric was added to this model \""," \"via `Model.add_metric`. This is not currently \""," \"supported when using exact evaluation with \""," \"`tf.distribute.ParameterServerStrategy`.\""," )",""," def _infer_exact_eval_shards(self, pss_evaluation_shards):"," if not self.distribute_strategy._should_use_with_coordinator:"," return 0"," if pss_evaluation_shards == \"auto\":"," # TODO(b/264265138) evaluate and improve this heuristic"," return self.distribute_strategy._num_workers * 5"," return pss_evaluation_shards",""," def _get_test_function_runner(self, callbacks):"," if ("," self._pss_evaluation_shards"," and self.distribute_strategy._should_use_with_coordinator"," ):"," self.test_function = self._make_test_function_exact()"," test_function_runner = _ExactTestFunction("," self.test_function, callbacks"," )"," else:"," self.test_function = self.make_test_function()"," test_function_runner = _TestFunction(self.test_function, callbacks)"," return test_function_runner",""," def predict_step(self, data):"," \"\"\"The logic for one inference step.",""," This method can be overridden to support custom inference logic."," This method is called by `Model.make_predict_function`.",""," This method should contain the mathematical logic for one step of"," inference. This typically includes the forward pass.",""," Configuration details for *how* this logic is run (e.g. `tf.function`"," and `tf.distribute.Strategy` settings), should be left to"," `Model.make_predict_function`, which can also be overridden.",""," Args:"," data: A nested structure of `Tensor`s.",""," Returns:"," The result of one inference step, typically the output of calling the"," `Model` on data."," \"\"\""," x, _, _ = data_adapter.unpack_x_y_sample_weight(data)"," return self(x, training=False)",""," def make_predict_function(self, force=False):"," \"\"\"Creates a function that executes one step of inference.",""," This method can be overridden to support custom inference logic."," This method is called by `Model.predict` and `Model.predict_on_batch`.",""," Typically, this method directly controls `tf.function` and"," `tf.distribute.Strategy` settings, and delegates the actual evaluation"," logic to `Model.predict_step`.",""," This function is cached the first time `Model.predict` or"," `Model.predict_on_batch` is called. The cache is cleared whenever"," `Model.compile` is called. You can skip the cache and generate again the"," function with `force=True`.",""," Args:"," force: Whether to regenerate the predict function and skip the cached"," function if available.",""," Returns:"," Function. The function created by this method should accept a"," `tf.data.Iterator`, and return the outputs of the `Model`."," \"\"\""," if self.predict_function is not None and not force:"," return self.predict_function",""," def step_function(model, iterator):"," \"\"\"Runs a single evaluation step.\"\"\"",""," def run_step(data):"," outputs = model.predict_step(data)"," # Ensure counter is updated only if `test_step` succeeds."," with tf.control_dependencies(_minimum_control_deps(outputs)):"," model._predict_counter.assign_add(1)"," return outputs",""," if self.jit_compile:"," run_step = tf.function("," run_step, jit_compile=True, reduce_retracing=True"," )",""," data = next(iterator)"," outputs = model.distribute_strategy.run(run_step, args=(data,))"," outputs = reduce_per_replica("," outputs, self.distribute_strategy, reduction=\"concat\""," )"," return outputs",""," # Special case if steps_per_execution is one."," if ("," self._steps_per_execution is None"," or self._steps_per_execution.numpy().item() == 1"," and not self.autotune_steps_per_execution"," ):",""," def predict_function(iterator):"," \"\"\"Runs an evaluation execution with a single step.\"\"\""," return step_function(self, iterator)",""," else:",""," def predict_function(iterator):"," \"\"\"Runs an evaluation execution with multiple steps.\"\"\""," outputs = step_function(self, iterator)"," for _ in tf.range(self._steps_per_execution - 1):"," tf.autograph.experimental.set_loop_options("," shape_invariants=["," ("," outputs,"," tf.nest.map_structure("," lambda t: tf_utils.get_tensor_spec("," t, dynamic_batch=True"," ).shape,"," outputs,"," ),"," )"," ]"," )"," step_outputs = step_function(self, iterator)"," outputs = tf.nest.map_structure("," lambda t1, t2: concat([t1, t2]), outputs, step_outputs"," )"," return outputs",""," if not self.run_eagerly:"," predict_function = tf.function("," predict_function, reduce_retracing=True"," )"," self.predict_function = predict_function",""," return self.predict_function",""," @traceback_utils.filter_traceback"," def predict("," self,"," x,"," batch_size=None,"," verbose=\"auto\","," steps=None,"," callbacks=None,"," max_queue_size=10,"," workers=1,"," use_multiprocessing=False,"," ):"," \"\"\"Generates output predictions for the input samples.",""," Computation is done in batches. This method is designed for batch"," processing of large numbers of inputs. It is not intended for use inside"," of loops that iterate over your data and process small numbers of inputs"," at a time.",""," For small numbers of inputs that fit in one batch,"," directly use `__call__()` for faster execution, e.g.,"," `model(x)`, or `model(x, training=False)` if you have layers such as"," `tf.keras.layers.BatchNormalization` that behave differently during"," inference. You may pair the individual model call with a `tf.function`"," for additional performance inside your inner loop."," If you need access to numpy array values instead of tensors after your"," model call, you can use `tensor.numpy()` to get the numpy array value of"," an eager tensor.",""," Also, note the fact that test loss is not affected by"," regularization layers like noise and dropout.",""," Note: See [this FAQ entry]("," https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call)"," for more details about the difference between `Model` methods"," `predict()` and `__call__()`.",""," Args:"," x: Input samples. It could be:"," - A Numpy array (or array-like), or a list of arrays"," (in case the model has multiple inputs)."," - A TensorFlow tensor, or a list of tensors"," (in case the model has multiple inputs)."," - A `tf.data` dataset."," - A generator or `keras.utils.Sequence` instance."," A more detailed description of unpacking behavior for iterator"," types (Dataset, generator, Sequence) is given in the `Unpacking"," behavior for iterator-like inputs` section of `Model.fit`."," batch_size: Integer or `None`."," Number of samples per batch."," If unspecified, `batch_size` will default to 32."," Do not specify the `batch_size` if your data is in the"," form of dataset, generators, or `keras.utils.Sequence` instances"," (since they generate batches)."," verbose: `\"auto\"`, 0, 1, or 2. Verbosity mode."," 0 = silent, 1 = progress bar, 2 = single line."," `\"auto\"` becomes 1 for most cases, and to 2 when used with"," `ParameterServerStrategy`. Note that the progress bar is not"," particularly useful when logged to a file, so `verbose=2` is"," recommended when not running interactively (e.g. in a production"," environment). Defaults to 'auto'."," steps: Total number of steps (batches of samples)"," before declaring the prediction round finished."," Ignored with the default value of `None`. If x is a `tf.data`"," dataset and `steps` is None, `predict()` will"," run until the input dataset is exhausted."," callbacks: List of `keras.callbacks.Callback` instances."," List of callbacks to apply during prediction."," See [callbacks]("," https://www.tensorflow.org/api_docs/python/tf/tf_keras/callbacks)."," max_queue_size: Integer. Used for generator or"," `keras.utils.Sequence` input only. Maximum size for the"," generator queue. If unspecified, `max_queue_size` will default"," to 10."," workers: Integer. Used for generator or `keras.utils.Sequence` input"," only. Maximum number of processes to spin up when using"," process-based threading. If unspecified, `workers` will default"," to 1."," use_multiprocessing: Boolean. Used for generator or"," `keras.utils.Sequence` input only. If `True`, use process-based"," threading. If unspecified, `use_multiprocessing` will default to"," `False`. Note that because this implementation relies on"," multiprocessing, you should not pass non-pickleable arguments to"," the generator as they can't be passed easily to children"," processes.",""," See the discussion of `Unpacking behavior for iterator-like inputs` for"," `Model.fit`. Note that Model.predict uses the same interpretation rules"," as `Model.fit` and `Model.evaluate`, so inputs must be unambiguous for"," all three methods.",""," Returns:"," Numpy array(s) of predictions.",""," Raises:"," RuntimeError: If `model.predict` is wrapped in a `tf.function`."," ValueError: In case of mismatch between the provided"," input data and the model's expectations,"," or in case a stateful model receives a number of samples"," that is not a multiple of the batch size."," \"\"\""," version_utils.disallow_legacy_graph(\"Model\", \"predict\")"," self._check_call_args(\"predict\")"," _disallow_inside_tf_function(\"predict\")",""," # TODO(yashkatariya): Cache model on the coordinator for faster"," # prediction. If running under PSS, then swap it with OneDeviceStrategy"," # so that execution will run on the coordinator."," original_pss_strategy = None"," if self.distribute_strategy._should_use_with_coordinator:"," original_pss_strategy = self.distribute_strategy"," self._distribution_strategy = None",""," # Cluster coordinator is set by `.fit()` and `.evaluate()` which is not"," # needed in `.predict()` because all the predictions happen on the"," # coordinator/locally."," if self._cluster_coordinator:"," self._cluster_coordinator = None",""," verbose = _get_verbosity(verbose, self.distribute_strategy)"," outputs = None"," with self.distribute_strategy.scope():"," # Creates a `tf.data.Dataset` and handles batch and epoch iteration."," dataset_types = (tf.compat.v1.data.Dataset, tf.data.Dataset)"," if ("," self._in_multi_worker_mode()"," or _is_tpu_multi_host(self.distribute_strategy)"," ) and isinstance(x, dataset_types):"," try:"," options = tf.data.Options()"," data_option = tf.data.experimental.AutoShardPolicy.DATA"," options.experimental_distribute.auto_shard_policy = ("," data_option"," )"," x = x.with_options(options)"," except ValueError:"," warnings.warn("," \"Using Model.predict with MultiWorkerMirroredStrategy \""," \"or TPUStrategy and AutoShardPolicy.FILE might lead to \""," \"out-of-order result. Consider setting it to \""," \"AutoShardPolicy.DATA.\","," stacklevel=2,"," )",""," data_handler = data_adapter.get_data_handler("," x=x,"," batch_size=batch_size,"," steps_per_epoch=steps,"," initial_epoch=0,"," epochs=1,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," model=self,"," steps_per_execution=self._steps_per_execution,"," )",""," # Container that configures and calls `tf.keras.Callback`s."," if not isinstance(callbacks, callbacks_module.CallbackList):"," callbacks = callbacks_module.CallbackList("," callbacks,"," add_history=True,"," add_progbar=verbose != 0,"," model=self,"," verbose=verbose,"," epochs=1,"," steps=data_handler.inferred_steps,"," )",""," self.predict_function = self.make_predict_function()"," self._predict_counter.assign(0)"," callbacks.on_predict_begin()"," if self.autotune_steps_per_execution:"," self._steps_per_execution_tuner.start()"," batch_outputs = None"," for _, iterator in data_handler.enumerate_epochs(): # Single epoch."," with data_handler.catch_stop_iteration():"," for step in data_handler.steps():"," callbacks.on_predict_batch_begin(step)"," tmp_batch_outputs = self.predict_function(iterator)"," if data_handler.should_sync:"," context.async_wait()"," batch_outputs = ("," tmp_batch_outputs # No error, now safe to assign."," )"," if outputs is None:"," outputs = tf.nest.map_structure("," lambda batch_output: [batch_output],"," batch_outputs,"," )"," else:"," tf.__internal__.nest.map_structure_up_to("," batch_outputs,"," lambda output, batch_output: output.append("," batch_output"," ),"," outputs,"," batch_outputs,"," )"," end_step = step + data_handler.step_increment"," callbacks.on_predict_batch_end("," end_step, {\"outputs\": batch_outputs}"," )"," if batch_outputs is None:"," raise ValueError("," \"Unexpected result of `predict_function` \""," \"(Empty batch_outputs). Please use \""," \"`Model.compile(..., run_eagerly=True)`, or \""," \"`tf.config.run_functions_eagerly(True)` for more \""," \"information of where went wrong, or file a \""," \"issue/bug to `tf.keras`.\""," )"," if self.autotune_steps_per_execution:"," self._steps_per_execution_tuner.stop()"," callbacks.on_predict_end()"," all_outputs = tf.__internal__.nest.map_structure_up_to("," batch_outputs, potentially_ragged_concat, outputs"," )",""," # If originally PSS strategy was used, then replace it back since"," # predict is running under `OneDeviceStrategy` after the swap and once"," # its done we need to replace it back to PSS again."," if original_pss_strategy is not None:"," self._distribution_strategy = original_pss_strategy",""," return tf_utils.sync_to_numpy_or_python_type(all_outputs)",""," def reset_metrics(self):"," \"\"\"Resets the state of all the metrics in the model.",""," Examples:",""," \u003e\u003e\u003e inputs = tf.keras.layers.Input(shape=(3,))"," \u003e\u003e\u003e outputs = tf.keras.layers.Dense(2)(inputs)"," \u003e\u003e\u003e model = tf.keras.models.Model(inputs=inputs, outputs=outputs)"," \u003e\u003e\u003e model.compile(optimizer=\"Adam\", loss=\"mse\", metrics=[\"mae\"])",""," \u003e\u003e\u003e x = np.random.random((2, 3))"," \u003e\u003e\u003e y = np.random.randint(0, 2, (2, 2))"," \u003e\u003e\u003e _ = model.fit(x, y, verbose=0)"," \u003e\u003e\u003e assert all(float(m.result()) for m in model.metrics)",""," \u003e\u003e\u003e model.reset_metrics()"," \u003e\u003e\u003e assert all(float(m.result()) == 0 for m in model.metrics)",""," \"\"\""," for m in self.metrics:"," m.reset_state()",""," def train_on_batch("," self,"," x,"," y=None,"," sample_weight=None,"," class_weight=None,"," reset_metrics=True,"," return_dict=False,"," ):"," \"\"\"Runs a single gradient update on a single batch of data.",""," Args:"," x: Input data. It could be:"," - A Numpy array (or array-like), or a list of arrays"," (in case the model has multiple inputs)."," - A TensorFlow tensor, or a list of tensors"," (in case the model has multiple inputs)."," - A dict mapping input names to the corresponding array/tensors,"," if the model has named inputs."," y: Target data. Like the input data `x`, it could be either Numpy"," array(s) or TensorFlow tensor(s)."," sample_weight: Optional array of the same length as x, containing"," weights to apply to the model's loss for each sample. In the case"," of temporal data, you can pass a 2D array with shape (samples,"," sequence_length), to apply a different weight to every timestep of"," every sample."," class_weight: Optional dictionary mapping class indices (integers)"," to a weight (float) to apply to the model's loss for the samples"," from this class during training. This can be useful to tell the"," model to \"pay more attention\" to samples from an under-represented"," class. When `class_weight` is specified and targets have a rank of"," 2 or greater, either `y` must be one-hot encoded, or an explicit"," final dimension of `1` must be included for sparse class labels."," reset_metrics: If `True`, the metrics returned will be only for this"," batch. If `False`, the metrics will be statefully accumulated"," across batches."," return_dict: If `True`, loss and metric results are returned as a"," dict, with each key being the name of the metric. If `False`, they"," are returned as a list.",""," Returns:"," Scalar training loss"," (if the model has a single output and no metrics)"," or list of scalars (if the model has multiple outputs"," and/or metrics). The attribute `model.metrics_names` will give you"," the display labels for the scalar outputs.",""," Raises:"," RuntimeError: If `model.train_on_batch` is wrapped in a `tf.function`."," \"\"\""," self._assert_compile_was_called()"," self._check_call_args(\"train_on_batch\")"," _disallow_inside_tf_function(\"train_on_batch\")"," if reset_metrics:"," self.reset_metrics()"," with self.distribute_strategy.scope(), training_utils.RespectCompiledTrainableState( # noqa: E501"," self"," ):"," iterator = data_adapter.single_batch_iterator("," self.distribute_strategy, x, y, sample_weight, class_weight"," )"," self.train_function = self.make_train_function()"," logs = self.train_function(iterator)",""," logs = tf_utils.sync_to_numpy_or_python_type(logs)"," if return_dict:"," return logs"," else:"," return flatten_metrics_in_order(logs, self.metrics_names)",""," def test_on_batch("," self,"," x,"," y=None,"," sample_weight=None,"," reset_metrics=True,"," return_dict=False,"," ):"," \"\"\"Test the model on a single batch of samples.",""," Args:"," x: Input data. It could be:"," - A Numpy array (or array-like), or a list of arrays (in case the"," model has multiple inputs)."," - A TensorFlow tensor, or a list of tensors (in case the model has"," multiple inputs)."," - A dict mapping input names to the corresponding array/tensors,"," if the model has named inputs."," y: Target data. Like the input data `x`, it could be either Numpy"," array(s) or TensorFlow tensor(s). It should be consistent with `x`"," (you cannot have Numpy inputs and tensor targets, or inversely)."," sample_weight: Optional array of the same length as x, containing"," weights to apply to the model's loss for each sample. In the case"," of temporal data, you can pass a 2D array with shape (samples,"," sequence_length), to apply a different weight to every timestep of"," every sample."," reset_metrics: If `True`, the metrics returned will be only for this"," batch. If `False`, the metrics will be statefully accumulated"," across batches."," return_dict: If `True`, loss and metric results are returned as a"," dict, with each key being the name of the metric. If `False`, they"," are returned as a list.",""," Returns:"," Scalar test loss (if the model has a single output and no metrics)"," or list of scalars (if the model has multiple outputs"," and/or metrics). The attribute `model.metrics_names` will give you"," the display labels for the scalar outputs.",""," Raises:"," RuntimeError: If `model.test_on_batch` is wrapped in a"," `tf.function`."," \"\"\""," self._assert_compile_was_called()"," self._check_call_args(\"test_on_batch\")"," _disallow_inside_tf_function(\"test_on_batch\")"," if reset_metrics:"," self.reset_metrics()"," with self.distribute_strategy.scope():"," iterator = data_adapter.single_batch_iterator("," self.distribute_strategy, x, y, sample_weight"," )"," self.test_function = self.make_test_function()"," logs = self.test_function(iterator)",""," logs = tf_utils.sync_to_numpy_or_python_type(logs)"," if return_dict:"," return logs"," else:"," return flatten_metrics_in_order(logs, self.metrics_names)",""," def predict_on_batch(self, x):"," \"\"\"Returns predictions for a single batch of samples.",""," Args:"," x: Input data. It could be:"," - A Numpy array (or array-like), or a list of arrays (in case the"," model has multiple inputs)."," - A TensorFlow tensor, or a list of tensors (in case the model has"," multiple inputs).",""," Returns:"," Numpy array(s) of predictions.",""," Raises:"," RuntimeError: If `model.predict_on_batch` is wrapped in a"," `tf.function`."," \"\"\""," self._check_call_args(\"predict_on_batch\")"," _disallow_inside_tf_function(\"predict_on_batch\")"," with self.distribute_strategy.scope():"," iterator = data_adapter.single_batch_iterator("," self.distribute_strategy, x"," )"," self.predict_function = self.make_predict_function()"," outputs = self.predict_function(iterator)"," return tf_utils.sync_to_numpy_or_python_type(outputs)",""," @doc_controls.do_not_generate_docs"," def fit_generator("," self,"," generator,"," steps_per_epoch=None,"," epochs=1,"," verbose=1,"," callbacks=None,"," validation_data=None,"," validation_steps=None,"," validation_freq=1,"," class_weight=None,"," max_queue_size=10,"," workers=1,"," use_multiprocessing=False,"," shuffle=True,"," initial_epoch=0,"," ):"," \"\"\"Fits the model on data yielded batch-by-batch by a Python generator.",""," DEPRECATED:"," `Model.fit` now supports generators, so there is no longer any need to"," use this endpoint."," \"\"\""," warnings.warn("," \"`Model.fit_generator` is deprecated and \""," \"will be removed in a future version. \""," \"Please use `Model.fit`, which supports generators.\","," stacklevel=2,"," )"," return self.fit("," generator,"," steps_per_epoch=steps_per_epoch,"," epochs=epochs,"," verbose=verbose,"," callbacks=callbacks,"," validation_data=validation_data,"," validation_steps=validation_steps,"," validation_freq=validation_freq,"," class_weight=class_weight,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," shuffle=shuffle,"," initial_epoch=initial_epoch,"," )",""," @doc_controls.do_not_generate_docs"," def evaluate_generator("," self,"," generator,"," steps=None,"," callbacks=None,"," max_queue_size=10,"," workers=1,"," use_multiprocessing=False,"," verbose=0,"," ):"," \"\"\"Evaluates the model on a data generator.",""," DEPRECATED:"," `Model.evaluate` now supports generators, so there is no longer any"," need to use this endpoint."," \"\"\""," warnings.warn("," \"`Model.evaluate_generator` is deprecated and \""," \"will be removed in a future version. \""," \"Please use `Model.evaluate`, which supports generators.\","," stacklevel=2,"," )"," self._check_call_args(\"evaluate_generator\")",""," return self.evaluate("," generator,"," steps=steps,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," verbose=verbose,"," callbacks=callbacks,"," )",""," @doc_controls.do_not_generate_docs"," def predict_generator("," self,"," generator,"," steps=None,"," callbacks=None,"," max_queue_size=10,"," workers=1,"," use_multiprocessing=False,"," verbose=0,"," ):"," \"\"\"Generates predictions for the input samples from a data generator.",""," DEPRECATED:"," `Model.predict` now supports generators, so there is no longer any"," need to use this endpoint."," \"\"\""," warnings.warn("," \"`Model.predict_generator` is deprecated and \""," \"will be removed in a future version. \""," \"Please use `Model.predict`, which supports generators.\","," stacklevel=2,"," )"," return self.predict("," generator,"," steps=steps,"," max_queue_size=max_queue_size,"," workers=workers,"," use_multiprocessing=use_multiprocessing,"," verbose=verbose,"," callbacks=callbacks,"," )",""," ######################################################################"," # Functions below are not training related. They are for model weights"," # tracking, save/load, serialization, etc."," ######################################################################",""," @property"," def trainable_weights(self):"," self._assert_weights_created()"," if not self._trainable:"," return []"," trainable_variables = []"," for trackable_obj in self._self_tracked_trackables:"," trainable_variables += trackable_obj.trainable_variables"," trainable_variables += self._trainable_weights"," return self._dedup_weights(trainable_variables)",""," @property"," def non_trainable_weights(self):"," self._assert_weights_created()"," non_trainable_variables = []"," for trackable_obj in self._self_tracked_trackables:"," non_trainable_variables += trackable_obj.non_trainable_variables",""," if not self._trainable:"," # Return order is all trainable vars, then all non-trainable vars."," trainable_variables = []"," for trackable_obj in self._self_tracked_trackables:"," trainable_variables += trackable_obj.trainable_variables",""," non_trainable_variables = ("," trainable_variables"," + self._trainable_weights"," + non_trainable_variables"," + self._non_trainable_weights"," )"," else:"," non_trainable_variables = ("," non_trainable_variables + self._non_trainable_weights"," )",""," return self._dedup_weights(non_trainable_variables)",""," def get_weights(self):"," \"\"\"Retrieves the weights of the model.",""," Returns:"," A flat list of Numpy arrays."," \"\"\""," with self.distribute_strategy.scope():"," return super().get_weights()",""," @traceback_utils.filter_traceback"," def save(self, filepath, overwrite=True, save_format=None, **kwargs):"," \"\"\"Saves a model as a TensorFlow SavedModel or HDF5 file.",""," See the [Serialization and Saving guide]("," https://keras.io/guides/serialization_and_saving/) for details.",""," Args:"," model: TF-Keras model instance to be saved."," filepath: `str` or `pathlib.Path` object. Path where to save the"," model."," overwrite: Whether we should overwrite any existing model at the"," target location, or instead ask the user via an interactive"," prompt."," save_format: Either `\"keras\"`, `\"tf\"`, `\"h5\"`,"," indicating whether to save the model"," in the native TF-Keras format (`.keras`),"," in the TensorFlow SavedModel format"," (referred to as \"SavedModel\" below),"," or in the legacy HDF5 format (`.h5`)."," Defaults to `\"tf\"` in TF 2.X, and `\"h5\"` in TF 1.X.",""," SavedModel format arguments:"," include_optimizer: Only applied to SavedModel and legacy HDF5"," formats. If False, do not save the optimizer state."," Defaults to `True`."," signatures: Only applies to SavedModel format. Signatures to save"," with the SavedModel. See the `signatures` argument in"," `tf.saved_model.save` for details."," options: Only applies to SavedModel format."," `tf.saved_model.SaveOptions` object that specifies SavedModel"," saving options."," save_traces: Only applies to SavedModel format. When enabled, the"," SavedModel will store the function traces for each layer. This"," can be disabled, so that only the configs of each layer are"," stored. Defaults to `True`."," Disabling this will decrease serialization time"," and reduce file size, but it requires that all custom"," layers/models implement a `get_config()` method.",""," Example:",""," ```python"," model = tf.keras.Sequential(["," tf.keras.layers.Dense(5, input_shape=(3,)),"," tf.keras.layers.Softmax()])"," model.save(\"model.keras\")"," loaded_model = tf.keras.models.load_model(\"model.keras\")"," x = tf.random.uniform((10, 3))"," assert np.allclose(model.predict(x), loaded_model.predict(x))"," ```",""," Note that `model.save()` is an alias for `tf.keras.models.save_model()`."," \"\"\""," saving_api.save_model("," self,"," filepath=filepath,"," overwrite=overwrite,"," save_format=save_format,"," **kwargs,"," )",""," @traceback_utils.filter_traceback"," def save_weights("," self, filepath, overwrite=True, save_format=None, options=None"," ):"," \"\"\"Saves all layer weights.",""," Either saves in HDF5 or in TensorFlow format based on the `save_format`"," argument.",""," When saving in HDF5 format, the weight file has:"," - `layer_names` (attribute), a list of strings"," (ordered names of model layers)."," - For every layer, a `group` named `layer.name`"," - For every such layer group, a group attribute `weight_names`,"," a list of strings"," (ordered names of weights tensor of the layer)."," - For every weight in the layer, a dataset"," storing the weight value, named after the weight tensor.",""," When saving in TensorFlow format, all objects referenced by the network"," are saved in the same format as `tf.train.Checkpoint`, including any"," `Layer` instances or `Optimizer` instances assigned to object"," attributes. For networks constructed from inputs and outputs using"," `tf.keras.Model(inputs, outputs)`, `Layer` instances used by the network"," are tracked/saved automatically. For user-defined classes which inherit"," from `tf.keras.Model`, `Layer` instances must be assigned to object"," attributes, typically in the constructor. See the documentation of"," `tf.train.Checkpoint` and `tf.keras.Model` for details.",""," While the formats are the same, do not mix `save_weights` and"," `tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should"," be loaded using `Model.load_weights`. Checkpoints saved using"," `tf.train.Checkpoint.save` should be restored using the corresponding"," `tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over"," `save_weights` for training checkpoints.",""," The TensorFlow format matches objects and variables by starting at a"," root object, `self` for `save_weights`, and greedily matching attribute"," names. For `Model.save` this is the `Model`, and for `Checkpoint.save`"," this is the `Checkpoint` even if the `Checkpoint` has a model attached."," This means saving a `tf.keras.Model` using `save_weights` and loading"," into a `tf.train.Checkpoint` with a `Model` attached (or vice versa)"," will not match the `Model`'s variables. See the"," [guide to training checkpoints]("," https://www.tensorflow.org/guide/checkpoint) for details on"," the TensorFlow format.",""," Args:"," filepath: String or PathLike, path to the file to save the weights"," to. When saving in TensorFlow format, this is the prefix used"," for checkpoint files (multiple files are generated). Note that"," the '.h5' suffix causes weights to be saved in HDF5 format."," overwrite: Whether to silently overwrite any existing file at the"," target location, or provide the user with a manual prompt."," save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or"," '.keras' will default to HDF5 if `save_format` is `None`."," Otherwise, `None` becomes 'tf'. Defaults to `None`."," options: Optional `tf.train.CheckpointOptions` object that specifies"," options for saving weights.",""," Raises:"," ImportError: If `h5py` is not available when attempting to save in"," HDF5 format."," \"\"\""," saving_api.save_weights("," self,"," filepath=filepath,"," overwrite=overwrite,"," save_format=save_format,"," options=options,"," )",""," @traceback_utils.filter_traceback"," def load_weights("," self, filepath, skip_mismatch=False, by_name=False, options=None"," ):"," \"\"\"Loads all layer weights from a saved files.",""," The saved file could be a SavedModel file, a `.keras` file (v3 saving"," format), or a file created via `model.save_weights()`.",""," By default, weights are loaded based on the network's"," topology. This means the architecture should be the same as when the"," weights were saved. Note that layers that don't have weights are not"," taken into account in the topological ordering, so adding or removing"," layers is fine as long as they don't have weights.",""," **Partial weight loading**",""," If you have modified your model, for instance by adding a new layer"," (with weights) or by changing the shape of the weights of a layer,"," you can choose to ignore errors and continue loading"," by setting `skip_mismatch=True`. In this case any layer with"," mismatching weights will be skipped. A warning will be displayed"," for each skipped layer.",""," **Weight loading by name**",""," If your weights are saved as a `.h5` file created"," via `model.save_weights()`, you can use the argument `by_name=True`.",""," In this case, weights are loaded into layers only if they share"," the same name. This is useful for fine-tuning or transfer-learning"," models where some of the layers have changed.",""," Note that only topological loading (`by_name=False`) is supported when"," loading weights from the `.keras` v3 format or from the TensorFlow"," SavedModel format.",""," Args:"," filepath: String, path to the weights file to load. For weight files"," in TensorFlow format, this is the file prefix (the same as was"," passed to `save_weights()`). This can also be a path to a"," SavedModel or a `.keras` file (v3 saving format) saved"," via `model.save()`."," skip_mismatch: Boolean, whether to skip loading of layers where"," there is a mismatch in the number of weights, or a mismatch in"," the shape of the weights."," by_name: Boolean, whether to load weights by name or by topological"," order. Only topological loading is supported for weight files in"," the `.keras` v3 format or in the TensorFlow SavedModel format."," options: Optional `tf.train.CheckpointOptions` object that specifies"," options for loading weights (only valid for a SavedModel file)."," \"\"\""," return saving_api.load_weights("," self,"," filepath=filepath,"," by_name=by_name,"," skip_mismatch=skip_mismatch,"," options=options,"," )",""," def _updated_config(self):"," \"\"\"Util shared between different serialization methods.",""," Returns:"," Model config with TF-Keras version information added."," \"\"\""," from tf_keras import __version__ as keras_version",""," config = self.get_config()"," model_config = {"," \"class_name\": self.__class__.__name__,"," \"config\": config,"," \"keras_version\": keras_version,"," \"backend\": backend.backend(),"," }"," return model_config",""," @generic_utils.default"," def get_config(self):"," \"\"\"Returns the config of the `Model`.",""," Config is a Python dictionary (serializable) containing the"," configuration of an object, which in this case is a `Model`. This allows"," the `Model` to be be reinstantiated later (without its trained weights)"," from this configuration.",""," Note that `get_config()` does not guarantee to return a fresh copy of"," dict every time it is called. The callers should make a copy of the"," returned dict if they want to modify it.",""," Developers of subclassed `Model` are advised to override this method,"," and continue to update the dict from `super(MyModel, self).get_config()`"," to provide the proper configuration of this `Model`. The default config"," will return config dict for init parameters if they are basic types."," Raises `NotImplementedError` when in cases where a custom"," `get_config()` implementation is required for the subclassed model.",""," Returns:"," Python dictionary containing the configuration of this `Model`."," \"\"\""," # If sublcass doesn't implement `get_config()` parse from init args"," # otherwise default to empty dict"," if generic_utils.is_default(self.get_config):"," try:"," config = base_layer.Layer.get_config(self)"," except NotImplementedError:"," config = {}"," logging.warning("," \"Model's `__init__()` arguments contain non-serializable \""," \"objects. Please implement a `get_config()` method in the \""," \"subclassed Model for proper saving and loading. \""," \"Defaulting to empty config.\""," )"," else:"," config = {}"," return config",""," @classmethod"," def from_config(cls, config, custom_objects=None):"," # `from_config` assumes `cls` is either `Functional` or a child class of"," # `Functional`. In the case that `cls` is meant to behave like a child"," # class of `Functional` but only inherits from the `Model` class, we"," # have to call `cls(...)` instead of `Functional.from_config`."," from tf_keras.engine import functional",""," with serialization.SharedObjectLoadingScope():"," functional_config_keys = ["," \"name\","," \"layers\","," \"input_layers\","," \"output_layers\","," ]"," is_functional_config = all("," key in config for key in functional_config_keys"," )"," argspec = tf_inspect.getfullargspec(cls.__init__)"," functional_init_args = tf_inspect.getfullargspec("," functional.Functional.__init__"," ).args[1:]"," revivable_as_functional = ("," cls in {functional.Functional, Model}"," or argspec.args[1:] == functional_init_args"," or (argspec.varargs == \"args\" and argspec.varkw == \"kwargs\")"," )"," if is_functional_config and revivable_as_functional:"," # Revive Functional model"," # (but not Functional subclasses with a custom __init__)"," inputs, outputs, layers = functional.reconstruct_from_config("," config, custom_objects"," )"," model = cls("," inputs=inputs, outputs=outputs, name=config.get(\"name\")"," )"," functional.connect_ancillary_layers(model, layers)",""," else:"," # Either the model has a custom __init__, or the config"," # does not contain all the information necessary to"," # revive a Functional model. This happens when the user creates"," # subclassed models where `get_config()` is returning"," # insufficient information to be considered a Functional model."," # In this case, we fall back to provide all config into the"," # constructor of the class."," try:"," model = cls(**config)"," except TypeError as e:"," raise TypeError("," \"Unable to revive model from config. When overriding \""," \"the `get_config()` method, make sure that the \""," \"returned config contains all items used as arguments \""," f\"in the constructor to {cls}, \""," \"which is the default behavior. \""," \"You can override this default behavior by defining a \""," \"`from_config(cls, config)` class method to specify \""," \"how to create an \""," f\"instance of {cls.__name__} from its config.\\n\\n\""," f\"Received config={config}\\n\\n\""," f\"Error encountered during deserialization: {e}\""," )"," return model",""," def to_json(self, **kwargs):"," \"\"\"Returns a JSON string containing the network configuration.",""," To load a network from a JSON save file, use"," `keras.models.model_from_json(json_string, custom_objects={})`.",""," Args:"," **kwargs: Additional keyword arguments to be passed to"," *`json.dumps()`.",""," Returns:"," A JSON string."," \"\"\""," model_config = self._updated_config()"," return json.dumps("," model_config, default=json_utils.get_json_type, **kwargs"," )",""," def to_yaml(self, **kwargs):"," \"\"\"Returns a yaml string containing the network configuration.",""," Note: Since TF 2.6, this method is no longer supported and will raise a"," RuntimeError.",""," To load a network from a yaml save file, use"," `keras.models.model_from_yaml(yaml_string, custom_objects={})`.",""," `custom_objects` should be a dictionary mapping"," the names of custom losses / layers / etc to the corresponding"," functions / classes.",""," Args:"," **kwargs: Additional keyword arguments"," to be passed to `yaml.dump()`.",""," Returns:"," A YAML string.",""," Raises:"," RuntimeError: announces that the method poses a security risk"," \"\"\""," raise RuntimeError("," \"Method `model.to_yaml()` has been removed due to security risk of \""," \"arbitrary code execution. Please use `model.to_json()` instead.\""," )",""," def reset_states(self):"," for layer in self.layers:"," if hasattr(layer, \"reset_states\") and getattr("," layer, \"stateful\", False"," ):"," layer.reset_states()",""," @property"," @doc_controls.do_not_generate_docs"," def state_updates(self):"," \"\"\"Deprecated, do NOT use!",""," Returns the `updates` from all layers that are stateful.",""," This is useful for separating training updates and"," state updates, e.g. when we need to update a layer's internal state"," during prediction.",""," Returns:"," A list of update ops."," \"\"\""," warnings.warn("," \"`Model.state_updates` will be removed in a future version. \""," \"This property should not be used in TensorFlow 2.0, \""," \"as `updates` are applied automatically.\","," stacklevel=2,"," )"," state_updates = []"," for layer in self.layers:"," if getattr(layer, \"stateful\", False):"," if hasattr(layer, \"updates\"):"," state_updates += layer.updates"," return state_updates",""," @property"," def weights(self):"," \"\"\"Returns the list of all layer variables/weights.",""," Note: This will not track the weights of nested `tf.Modules` that are"," not themselves TF-Keras layers.",""," Returns:"," A list of variables."," \"\"\""," return self._dedup_weights(self._undeduplicated_weights)",""," @property"," def _undeduplicated_weights(self):"," \"\"\"Returns the undeduplicated list of all layer variables/weights.\"\"\""," self._assert_weights_created()"," weights = []"," for layer in self._self_tracked_trackables:"," weights += layer.variables"," weights += self._trainable_weights + self._non_trainable_weights"," return weights",""," def summary("," self,"," line_length=None,"," positions=None,"," print_fn=None,"," expand_nested=False,"," show_trainable=False,"," layer_range=None,"," ):"," \"\"\"Prints a string summary of the network.",""," Args:"," line_length: Total length of printed lines"," (e.g. set this to adapt the display to different"," terminal window sizes)."," positions: Relative or absolute positions of log elements"," in each line. If not provided, becomes"," `[0.3, 0.6, 0.70, 1.]`. Defaults to `None`."," print_fn: Print function to use. By default, prints to `stdout`."," If `stdout` doesn't work in your environment, change to `print`."," It will be called on each line of the summary."," You can set it to a custom function"," in order to capture the string summary."," expand_nested: Whether to expand the nested models."," Defaults to `False`."," show_trainable: Whether to show if a layer is trainable."," Defaults to `False`."," layer_range: a list or tuple of 2 strings,"," which is the starting layer name and ending layer name"," (both inclusive) indicating the range of layers to be printed"," in summary. It also accepts regex patterns instead of exact"," name. In such case, start predicate will be the first element"," it matches to `layer_range[0]` and the end predicate will be"," the last element it matches to `layer_range[1]`."," By default `None` which considers all layers of model.",""," Raises:"," ValueError: if `summary()` is called before the model is built."," \"\"\""," if not self.built:"," raise ValueError("," \"This model has not yet been built. \""," \"Build the model first by calling `build()` or by calling \""," \"the model on a batch of data.\""," )"," layer_utils.print_summary("," self,"," line_length=line_length,"," positions=positions,"," print_fn=print_fn,"," expand_nested=expand_nested,"," show_trainable=show_trainable,"," layer_range=layer_range,"," )",""," @property"," def layers(self):"," return list(self._flatten_layers(include_self=False, recursive=False))",""," @layers.setter"," def layers(self, _):"," raise AttributeError("," \"`Model.layers` attribute is reserved and should not be used. \""," \"Please use another name.\""," )",""," def get_layer(self, name=None, index=None):"," \"\"\"Retrieves a layer based on either its name (unique) or index.",""," If `name` and `index` are both provided, `index` will take precedence."," Indices are based on order of horizontal graph traversal (bottom-up).",""," Args:"," name: String, name of layer."," index: Integer, index of layer.",""," Returns:"," A layer instance."," \"\"\""," # TODO(fchollet): We could build a dictionary based on layer names"," # since they are constant, but we have not done that yet."," if index is not None and name is not None:"," raise ValueError("," \"Provide only a layer name or a layer index. Received: \""," f\"index={index}, name={name}.\""," )",""," if index is not None:"," if len(self.layers) \u003c= index:"," raise ValueError("," f\"Was asked to retrieve layer at index {index}\""," f\" but model only has {len(self.layers)}\""," \" layers.\""," )"," else:"," return self.layers[index]",""," if name is not None:"," for layer in self.layers:"," if layer.name == name:"," return layer"," raise ValueError("," f\"No such layer: {name}. Existing layers are: \""," f\"{list(layer.name for layer in self.layers)}.\""," )"," raise ValueError("," \"Provide either a layer name or layer index at `get_layer`.\""," )",""," def get_weight_paths(self):"," \"\"\"Retrieve all the variables and their paths for the model.",""," The variable path (string) is a stable key to identify a `tf.Variable`"," instance owned by the model. It can be used to specify variable-specific"," configurations (e.g. DTensor, quantization) from a global view.",""," This method returns a dict with weight object paths as keys"," and the corresponding `tf.Variable` instances as values.",""," Note that if the model is a subclassed model and the weights haven't"," been initialized, an empty dict will be returned.",""," Returns:"," A dict where keys are variable paths and values are `tf.Variable`"," instances.",""," Example:",""," ```python"," class SubclassModel(tf.keras.Model):",""," def __init__(self, name=None):"," super().__init__(name=name)"," self.d1 = tf.keras.layers.Dense(10)"," self.d2 = tf.keras.layers.Dense(20)",""," def call(self, inputs):"," x = self.d1(inputs)"," return self.d2(x)",""," model = SubclassModel()"," model(tf.zeros((10, 10)))"," weight_paths = model.get_weight_paths()"," # weight_paths:"," # {"," # 'd1.kernel': model.d1.kernel,"," # 'd1.bias': model.d1.bias,"," # 'd2.kernel': model.d2.kernel,"," # 'd2.bias': model.d2.bias,"," # }",""," # Functional model"," inputs = tf.keras.Input((10,), batch_size=10)"," x = tf.keras.layers.Dense(20, name='d1')(inputs)"," output = tf.keras.layers.Dense(30, name='d2')(x)"," model = tf.keras.Model(inputs, output)"," d1 = model.layers[1]"," d2 = model.layers[2]"," weight_paths = model.get_weight_paths()"," # weight_paths:"," # {"," # 'd1.kernel': d1.kernel,"," # 'd1.bias': d1.bias,"," # 'd2.kernel': d2.kernel,"," # 'd2.bias': d2.bias,"," # }"," ```"," \"\"\""," result = {}"," ("," descendants,"," object_paths_dict,"," ) = tf.__internal__.tracking.ObjectGraphView("," self"," ).breadth_first_traversal()"," for descendant in descendants:"," if isinstance(descendant, tf.Variable):"," trackable_references = object_paths_dict[descendant]"," object_path = \".\".join([t.name for t in trackable_references])"," result[object_path] = descendant"," return result",""," def get_compile_config(self):"," \"\"\"Returns a serialized config with information for compiling the model.",""," This method returns a config dictionary containing all the information"," (optimizer, loss, metrics, etc.) with which the model was compiled.",""," Returns:"," A dict containing information for compiling the model."," \"\"\""," if self._is_compiled and hasattr(self, \"_compile_config\"):"," return self._compile_config.serialize()",""," def compile_from_config(self, config):"," \"\"\"Compiles the model with the information given in config.",""," This method uses the information in the config (optimizer, loss,"," metrics, etc.) to compile the model.",""," Args:"," config: Dict containing information for compiling the model."," \"\"\""," has_overridden_compile = self.__class__.compile != Model.compile"," if has_overridden_compile:"," logging.warning("," \"`compile()` was not called as part of model loading \""," \"because the model's `compile()` method is custom. \""," \"All subclassed Models that have `compile()` \""," \"overridden should also override \""," \"`get_compile_config()` and `compile_from_config(config)`. \""," \"Alternatively, you can \""," \"call `compile()` manually after loading.\""," )"," return"," config = saving_lib.deserialize_keras_object(config)"," self.compile(**config)"," if ("," hasattr(self, \"optimizer\")"," # Exempt legacy optimizers."," and isinstance(self.optimizer, optimizer.Optimizer)"," and self.built"," ):"," # Create optimizer variables."," self.optimizer.build(self.trainable_variables)",""," def export(self, filepath):"," \"\"\"Create a SavedModel artifact for inference (e.g. via TF-Serving).",""," This method lets you export a model to a lightweight SavedModel artifact"," that contains the model's forward pass only (its `call()` method)"," and can be served via e.g. TF-Serving. The forward pass is registered"," under the name `serve()` (see example below).",""," The original code of the model (including any custom layers you may"," have used) is *no longer* necessary to reload the artifact -- it is"," entirely standalone.",""," Args:"," filepath: `str` or `pathlib.Path` object. Path where to save"," the artifact.",""," Example:",""," ```python"," # Create the artifact"," model.export(\"path/to/location\")",""," # Later, in a different process / environment..."," reloaded_artifact = tf.saved_model.load(\"path/to/location\")"," predictions = reloaded_artifact.serve(input_data)"," ```",""," If you would like to customize your serving endpoints, you can"," use the lower-level `keras.export.ExportArchive` class. The `export()`"," method relies on `ExportArchive` internally."," \"\"\""," from tf_keras.export import export_lib",""," export_lib.export_model(self, filepath)",""," @tf.__internal__.tracking.no_automatic_dependency_tracking"," def _set_save_spec(self, inputs, args=None, kwargs=None):"," \"\"\"Defines the save spec so that serialization can trace `call()`.",""," The TensorSpecs of the call function `inputs`, `args`, and `kwargs` are"," saved into a tuple of `([inputs] + args, kwargs)`. The input"," `TensorSpec` names are updated to match the built `input_names`.",""," The specs can be retrieved with the `save_spec` property.",""," Args:"," inputs: possibly nested inputs passed into the call function."," args: a list of positional arguments passed into call."," kwargs: a dictionary of keyword arguments passed into call."," \"\"\""," if self._saved_model_inputs_spec is not None:"," return # Already set."," args = args or []"," kwargs = kwargs or {}",""," input_names = self.input_names"," if not input_names:"," input_names = compile_utils.create_pseudo_input_names(inputs)",""," flat_inputs = tf.nest.flatten(inputs)"," inputs_spec = []"," for name, tensor in zip(input_names, flat_inputs):"," inputs_spec.append("," tf_utils.get_tensor_spec(tensor, dynamic_batch=False, name=name)"," )"," inputs_spec = tf.nest.pack_sequence_as(inputs, inputs_spec)"," super()._set_save_spec(inputs_spec, args, kwargs)",""," # Store the input shapes"," if ("," self.__class__.__name__ == \"Sequential\""," and self._build_input_shape is None"," ):"," self._build_input_shape = tf.nest.map_structure("," lambda x: None if x is None else x.shape, inputs_spec"," )",""," def save_spec(self, dynamic_batch=True):"," \"\"\"Returns the `tf.TensorSpec` of call args as a tuple `(args, kwargs)`.",""," This value is automatically defined after calling the model for the"," first time. Afterwards, you can use it when exporting the model for"," serving:",""," ```python"," model = tf.keras.Model(...)",""," @tf.function"," def serve(*args, **kwargs):"," outputs = model(*args, **kwargs)"," # Apply postprocessing steps, or add additional outputs."," ..."," return outputs",""," # arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this"," # example, is an empty dict since functional models do not use keyword"," # arguments."," arg_specs, kwarg_specs = model.save_spec()",""," model.save(path, signatures={"," 'serving_default': serve.get_concrete_function(*arg_specs,"," **kwarg_specs)"," })"," ```",""," Args:"," dynamic_batch: Whether to set the batch sizes of all the returned"," `tf.TensorSpec` to `None`. (Note that when defining functional or"," Sequential models with `tf.keras.Input([...], batch_size=X)`, the"," batch size will always be preserved). Defaults to `True`."," Returns:"," If the model inputs are defined, returns a tuple `(args, kwargs)`. All"," elements in `args` and `kwargs` are `tf.TensorSpec`."," If the model inputs are not defined, returns `None`."," The model inputs are automatically set when calling the model,"," `model.fit`, `model.evaluate` or `model.predict`."," \"\"\""," return self._get_save_spec(dynamic_batch, inputs_only=False)",""," def _assert_weights_created(self):"," \"\"\"Asserts that all the weights for the model have been created.",""," For a non-dynamic model, the weights must already be created after the"," layer has been called. For a dynamic model, the exact list of weights"," can never be known for certain since it may change at any time during"," execution.",""," We run this check right before accessing weights or getting the Numpy"," value for the current weights. Otherwise, if the layer has never been"," called, the user would just get an empty list, which is misleading.",""," Raises:"," ValueError: if the weights of the network have not yet been created."," \"\"\""," if self.dynamic:"," return",""," if ("," \"build\" in self.__class__.__dict__"," and self.__class__ != Model"," and not self.built"," ):"," # For any model that has customized build() method but hasn't been"," # invoked yet, this will cover both sequential and subclass model."," # Also make sure to exclude Model class itself which has build()"," # defined."," raise ValueError("," f\"Weights for model '{self.name}' have not yet been \""," \"created. \""," \"Weights are created when the model is first called on \""," \"inputs or `build()` is called with an `input_shape`.\""," )",""," def _check_call_args(self, method_name):"," \"\"\"Check that `call()` has only one positional arg.\"\"\""," # Always allow first arg, regardless of arg name."," fullargspec = self._call_spec.full_argspec"," if fullargspec.defaults:"," positional_args = fullargspec.args[: -len(fullargspec.defaults)]"," else:"," positional_args = fullargspec.args"," if \"training\" in positional_args:"," positional_args.remove(\"training\")",""," # self and first arg can be positional."," if len(positional_args) \u003e 2:"," extra_args = positional_args[2:]"," raise ValueError("," f\"Models passed to `{method_name}` can only have `training` \""," \"and the first argument in `call()` as positional arguments, \""," f\"found: {extra_args}.\""," )",""," def _validate_compile(self, optimizer, metrics, **kwargs):"," \"\"\"Performs validation checks for the default `compile()`.\"\"\""," if any("," isinstance(opt, optimizer_v1.Optimizer)"," for opt in tf.nest.flatten(optimizer)"," ):"," raise ValueError("," f\"`tf.compat.v1.keras` Optimizer ({optimizer}) is \""," \"not supported when eager execution is enabled. Use a \""," \"`tf.keras` Optimizer instead, or disable eager \""," \"execution.\""," )",""," kwargs.pop(\"cloning\", None) # Legacy DistStrat argument, never used."," kwargs.pop(\"experimental_run_tf_function\", None) # Always `True`."," distribute_arg = kwargs.pop(\"distribute\", None)"," if distribute_arg is not None:"," raise ValueError("," \"`distribute` argument in compile is not available in TF 2.0. \""," \"Please create the model under the `strategy.scope()`. \""," f\"Received: {distribute_arg}.\""," )"," target_tensor_arg = kwargs.pop(\"target_tensors\", None)"," if target_tensor_arg is not None:"," raise ValueError("," \"`target_tensors` argument is not supported when executing \""," f\"eagerly. Received: {target_tensor_arg}.\""," )"," invalid_kwargs = set(kwargs) - {\"sample_weight_mode\"}"," if invalid_kwargs:"," raise TypeError("," \"Invalid keyword argument(s) in `compile()`: \""," f\"{(invalid_kwargs,)}. Valid keyword arguments include \""," '\"cloning\", \"experimental_run_tf_function\", \"distribute\",'"," ' \"target_tensors\", or \"sample_weight_mode\".'"," )",""," # Model must be created and compiled with the same DistStrat."," if self.built and tf.distribute.has_strategy():"," strategy = tf.distribute.get_strategy()"," for v in self.variables:"," if not strategy.extended.variable_created_in_scope(v):"," raise ValueError("," f\"Variable ({v}) was not created in the distribution \""," f\"strategy scope of ({strategy}). It is most likely \""," \"because some layers, model, or optimizer was being \""," \"created outside the distribution strategy scope. Try \""," \"to make sure your code looks similar \""," \"to the following.\\nwith strategy.scope():\\n\""," \" model=_create_model()\\n\""," \" model.compile(...)\""," )",""," # Model metrics must be created in the same distribution strategy scope"," # as the model."," strategy = self.distribute_strategy"," for metric in tf.nest.flatten(metrics):"," for v in getattr(metric, \"variables\", []):"," if not strategy.extended.variable_created_in_scope(v):"," raise ValueError("," f\"Metric ({metric}) passed to `model.compile` was \""," \"created inside a different distribution strategy \""," \"scope than the model. All metrics must be created \""," \"in the same distribution strategy \""," f\"scope as the model (in this case {strategy}). \""," \"If you pass in a string identifier for a metric to \""," \"compile, the metric will automatically be created \""," \"in the correct distribution strategy scope.\""," )",""," # Model metrics must be created in the same distribution strategy scope"," # as the model."," for opt in tf.nest.flatten(optimizer):"," for v in getattr(opt, \"_weights\", []):"," if not strategy.extended.variable_created_in_scope(v):"," raise ValueError("," f\"Optimizer ({optimizer}) passed to `model.compile` \""," \"was created inside a different distribution strategy \""," \"scope than the model. All optimizers must be created \""," \"in the same distribution strategy scope as the model \""," f\"(in this case {strategy}). If you pass in a string \""," \"identifier for an optimizer to compile, the optimizer \""," \"will automatically be created in the correct \""," \"distribution strategy scope.\""," )",""," def _maybe_load_initial_counters_from_ckpt("," self, steps_per_epoch, initial_epoch"," ):"," \"\"\"Maybe load initial epoch from ckpt, considering worker recovery.",""," Refer to tensorflow/python/tf_keras/distribute/worker_training_state.py"," for more information.",""," Args:"," steps_per_epoch: The number of step per epoch."," initial_epoch: The original initial_epoch user passes in `fit()`."," mode: The mode for running `model.fit()`.",""," Returns:"," If the training is recovering from previous failure under multi-worker"," training setting, return the (epoch, step) the training is supposed to"," continue at. Otherwise, return the `initial_epoch, initial_step` the"," user passes in."," \"\"\""," initial_step = 0"," if self._training_state is not None:"," return self._training_state.maybe_load_initial_counters_from_ckpt("," steps_per_epoch, initial_epoch, mode=ModeKeys.TRAIN"," )"," return (initial_epoch, initial_step)",""," def _assert_compile_was_called(self):"," # Checks whether `compile` has been called. If it has been called,"," # then the optimizer is set. This is different from whether the"," # model is compiled"," # (i.e. whether the model is built and its inputs/outputs are set)."," if not self._is_compiled:"," raise RuntimeError("," \"You must compile your model before \""," \"training/testing. \""," \"Use `model.compile(optimizer, loss)`.\""," )",""," def _check_sample_weight_warning(self, x, sample_weight):"," # Datasets can include sample weight, by returning a tuple with the"," # structure of `(x, y, sample_weight)`."," sample_weight_present = sample_weight is not None or ("," isinstance(x, tf.data.Dataset)"," and isinstance(x.element_spec, tuple)"," and len(x.element_spec) == 3"," )",""," if ("," sample_weight_present"," and self.compiled_metrics._user_weighted_metrics is None"," ):"," logging.warning("," \"`evaluate()` received a value for `sample_weight`, but \""," \"`weighted_metrics` were not provided. Did you mean to pass \""," \"metrics to `weighted_metrics` in `compile()`? If this is \""," \"intentional you can pass `weighted_metrics=[]` to `compile()` \""," \"in order to silence this warning.\""," )",""," def _set_inputs(self, inputs, outputs=None, training=None):"," \"\"\"This method is for compat with Modelv1. Only inputs are needed"," here.\"\"\""," self._set_save_spec(inputs)",""," @property"," def _trackable_saved_model_saver(self):"," return model_serialization.ModelSavedModelSaver(self)",""," def _trackable_children(self, save_type=\"checkpoint\", **kwargs):"," if save_type == \"savedmodel\":"," # SavedModel needs to ignore the execution functions."," train_function = self.train_function"," test_function = self.test_function"," predict_function = self.predict_function"," train_tf_function = self.train_tf_function"," self.train_function = None"," self.test_function = None"," self.predict_function = None"," self.train_tf_function = None",""," children = super()._trackable_children(save_type, **kwargs)",""," if save_type == \"savedmodel\":"," self.train_function = train_function"," self.test_function = test_function"," self.predict_function = predict_function"," self.train_tf_function = train_tf_function",""," return children",""," def _should_eval(self, epoch, validation_freq):"," epoch = epoch + 1 # one-index the user-facing epoch."," if isinstance(validation_freq, int):"," return epoch % validation_freq == 0"," elif isinstance(validation_freq, list):"," return epoch in validation_freq"," else:"," raise ValueError("," \"Expected `validation_freq` to be a list or int. \""," f\"Received: validation_freq={validation_freq} of the \""," f\"type {type(validation_freq)}.\""," )",""," ######################################################################"," # Functions below exist only as v1 / v2 compatibility shims."," ######################################################################",""," def _get_compile_args(self, user_metrics=True):"," \"\"\"Used for saving or cloning a Model.",""," Args:"," user_metrics: Whether to return user-supplied metrics or `Metric`"," objects. If True, returns the user-supplied metrics."," Defaults to `True`.",""," Returns:"," Dictionary of arguments that were used when compiling the model."," \"\"\""," self._assert_compile_was_called()"," saved_metrics = self.compiled_metrics._user_metrics"," saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics",""," if not user_metrics:"," if saved_metrics is not None:"," saved_metrics = self.compiled_metrics._metrics"," if saved_weighted_metrics is not None:"," saved_weighted_metrics = self.compiled_metrics._weighted_metrics",""," compile_args = {"," \"optimizer\": self.optimizer,"," \"loss\": self.compiled_loss._user_losses,"," \"metrics\": saved_metrics,"," \"weighted_metrics\": saved_weighted_metrics,"," \"loss_weights\": self.compiled_loss._user_loss_weights,"," }"," return compile_args",""," def _get_callback_model(self):"," return self",""," def _in_multi_worker_mode(self):"," return self.distribute_strategy.extended._in_multi_worker_mode()",""," @property"," def _compile_was_called(self):"," return self._is_compiled","","","class _TestFunction:"," def __init__(self, function, callbacks):"," self._function = function"," self._callbacks = callbacks",""," def run_step(self, dataset_or_iterator, data_handler, step, unused_shards):"," tmp_logs = self._function(dataset_or_iterator)"," if data_handler.should_sync:"," context.async_wait()"," logs = tmp_logs"," end_step = step + data_handler.step_increment"," self._callbacks.on_test_batch_end(end_step, logs)"," return logs","","","class _ExactTestFunction(_TestFunction):"," def __init__(self, function, callbacks):"," super().__init__(function, callbacks)"," self._logs = []",""," def run_step(self, dataset_or_iterator, data_handler, step, shards):"," tmp_logs = self._function("," dataset_or_iterator,"," tf.constant(shards, dtype=tf.int64),"," tf.constant(step, dtype=tf.int64),"," )"," if data_handler.should_sync:"," context.async_wait()"," self._logs.append(tmp_logs)"," return self._logs","","","def reduce_per_replica(values, strategy, reduction):"," \"\"\"Attempt to reduce the structure `values` to single values.",""," Given `values` (a `tf.Tensor` or a `PerReplica` structure),"," which represents the values across all the replicas, `reduce_per_replica`"," attempts to \"reduce\" those values and returns the corresponding structure"," that represents only single values.",""," Currently, `reduce_per_replica` is only used for reducing the metric results"," from `tf.distribute.Strategy.run()`. Depending on the underlying"," `Strategy` implementation, `values` may be a `PerReplica` object,"," which can be thought of as a collection of values across the replicas,"," or a `tf.Tensor`, if the strategy has already conducted the reduction"," for the downstream library.",""," There are five possible outcomes of reduction:",""," 1) if the `values` is a structure of simple `tf.Tensor`s, meaning that"," reduction is not actually needed, `reduce_per_replica` returns the"," structure as-is."," 2) else, if `reduction=\"auto\"`, then the best reduction strategy is"," chosen based on the current environment. This should only be used"," for training cases (`fit()`)."," 3) else, if `reduction=\"first\"`, then `reduce_per_replica`"," returns the values of the first replica. This is used in the case of"," training and evaluation, where `values` is expected to hold the same"," value across the replicas as a result of `Strategy`'s synchronization"," across the replicas."," `reduce_per_replica` does not synchronize the values."," 4) else, if `reduction=\"sum\"`, then `reduce_per_replica` returns the sum"," of values for all replicas. This may be used in the custom training loop"," case, where each replica contain different values which are not"," synchronized."," 5) else, if `reduction=\"concat\"`, then `reduce_per_replica`"," returns the concatenation of the values across the replicas, along the"," axis of dimension 0. This is used in the inference case (`predict()`).",""," Args:"," values: Structure of `PerReplica` objects or `tf.Tensor`s. `tf.Tensor`s"," are returned as-is."," strategy: `tf.distribute.Strategy` object."," reduction: One of `\"auto\"`, `\"first\"`, `\"concat\"`, or `\"sum\"`."," `\"auto\"` will select `\"first\"` when used under a TPUStrategy, or"," `\"sum\"` otherwise.",""," Returns:"," Structure of `Tensor`s, representing the result of reduction.",""," Raises:"," ValueError: if the reduction method is not supported."," \"\"\"",""," if reduction == \"auto\":"," reduction = \"first\" if backend.is_tpu_strategy(strategy) else \"sum\"",""," def _reduce(v):"," \"\"\"Reduce a single `PerReplica` object.\"\"\""," if _collective_all_reduce_multi_worker(strategy):"," if reduction == \"concat\":"," return _multi_worker_concat(v, strategy)"," elif reduction == \"sum\":"," return strategy.reduce(\"SUM\", v, axis=None)",""," if _is_dtensor_per_replica_instance(v):"," return _reduce_dtensor_per_replica(v, strategy, reduction)"," elif not _is_per_replica_instance(v):"," return v"," elif reduction == \"first\":"," return strategy.experimental_local_results(v)[0]"," elif reduction == \"concat\":"," if _is_tpu_multi_host(strategy):"," return _tpu_multi_host_concat(v, strategy)"," else:"," return concat(strategy.experimental_local_results(v))"," elif reduction == \"sum\":"," return tf.reduce_sum(strategy.experimental_local_results(v))"," else:"," raise ValueError("," '`reduction` must be \"first\", \"concat\", \"sum\", or \"auto\". '"," f\"Received: reduction={reduction}.\""," )",""," return tf.nest.map_structure(_reduce, values)","","","def concat(tensors, axis=0):"," \"\"\"Concats `tensor`s along `axis`.\"\"\""," if isinstance(tensors[0], tf.SparseTensor):"," return tf.sparse.concat(axis=axis, sp_inputs=tensors)"," elif _is_scalar(tensors[0]):"," return tf.stack(tensors, axis=axis)"," else:"," return tf.concat(tensors, axis=axis)","","","def potentially_ragged_concat(tensors):"," \"\"\"Concats `Tensor`s along their first dimension.",""," Args:"," tensors: List of `Tensor`s.",""," Returns:"," Concatenation of the inputs along the first dimension -- of type `Tensor`"," if all input shapes are compatible, or `RaggedTensor` if not."," \"\"\""," if len(tensors) == 1:"," return tensors[0]"," if isinstance(tensors[0], tf.SparseTensor):"," return tf.sparse.concat(axis=0, sp_inputs=tensors)"," elif isinstance(tensors[0], tf.RaggedTensor):"," return tf.concat(tensors, axis=0)"," elif not tf.__internal__.tf2.enabled():"," return tf.concat(tensors, axis=0)",""," non_batch_shapes = tf.stack([tf.shape(tensor)[1:] for tensor in tensors])"," constant_dims = tf.math.reduce_all("," non_batch_shapes == non_batch_shapes[:1], axis=0"," )"," if tf.math.reduce_all(constant_dims).numpy().item():"," # All non-batch dims are constant"," if _is_scalar(tensors[0]):"," return tf.stack(tensors, axis=0)"," else:"," return tf.concat(tensors, axis=0)",""," # First, identify constant inner dimensions by finding the"," # rightmost dimension that is not constant"," constant_inner_dimensions = ("," constant_dims.numpy().tolist()[::-1].index(False)"," )"," # If there are constant inner dimensions, define a constant inner shape"," if constant_inner_dimensions == 0:"," constant_inner_shape = None"," else:"," constant_inner_shape = tensors[0].shape[-constant_inner_dimensions:]"," return tf.ragged.constant("," [tensor.numpy() for tensor in tensors], inner_shape=constant_inner_shape"," ).merge_dims(0, 1)","","","def _reduce_dtensor_per_replica(value, strategy, reduction):"," # Note that this function could happen in graph, so we can't just access"," # the per-replica.values(), which will trigger unpack in graph and result"," # into error."," # For now we will perform ops on dtensor instance directly on a global"," # context."," dtensor = value._dtensor"," if reduction == \"first\":"," num_replica = strategy.num_replicas_in_sync"," return tf.split(dtensor, num_replica, axis=0)[0]"," elif reduction == \"concat\":"," # Since dtensor is already in global context, the concat is a no-op"," return dtensor"," elif reduction == \"sum\":"," return tf.reduce_sum(dtensor)"," else:"," raise ValueError("," '`reduction` must be one of \"first\", \"concat\", \"sum\", or \"auto\". '"," f\"Received: reduction={reduction}.\""," )","","","def _get_verbosity(verbose, distribute_strategy):"," \"\"\"Find the right verbosity value for 'auto'.\"\"\""," if verbose == 1 and distribute_strategy._should_use_with_coordinator:"," raise ValueError("," \"`verbose=1` is not allowed with `ParameterServerStrategy` for \""," f\"performance reasons. Received: verbose={verbose}\""," )"," if verbose == \"auto\":"," if ("," distribute_strategy._should_use_with_coordinator"," or not io_utils.is_interactive_logging_enabled()"," ):"," # Defaults to epoch-level logging for PSStrategy or using absl"," # logging."," return 2"," else:"," return 1 # Defaults to batch-level logging otherwise."," return verbose","","","def _is_tpu_multi_host(strategy):"," return backend.is_tpu_strategy(strategy) and strategy.extended.num_hosts \u003e 1","","","def _tpu_multi_host_concat(v, strategy):"," \"\"\"Correctly order TPU PerReplica objects.\"\"\""," replicas = strategy.experimental_local_results(v)"," # When distributed datasets are created from Tensors / NumPy,"," # TPUStrategy.experimental_distribute_dataset shards data in"," # (Replica, Host) order, and TPUStrategy.experimental_local_results returns"," # it in (Host, Replica) order."," # TODO(b/150317897): Figure out long-term plan here."," num_replicas_per_host = strategy.extended.num_replicas_per_host"," ordered_replicas = []"," for replica_id in range(num_replicas_per_host):"," ordered_replicas += replicas[replica_id::num_replicas_per_host]"," return concat(ordered_replicas)","","","def _collective_all_reduce_multi_worker(strategy):"," return ("," isinstance(strategy, tf.distribute.MultiWorkerMirroredStrategy)"," ) and strategy.extended._in_multi_worker_mode()","","","# TODO(wxinyi): merge this with _tpu_multi_host_concat once we have all_gather","# for all strategies","def _multi_worker_concat(v, strategy):"," \"\"\"Order PerReplica objects for CollectiveAllReduceStrategy and concat.\"\"\""," replicas = strategy.gather(v, axis=0)"," # v might not have the same shape on different replicas"," if _is_per_replica_instance(v):"," shapes = tf.concat("," ["," tf.expand_dims(tf.shape(single_value)[0], axis=0)"," for single_value in v.values"," ],"," axis=0,"," )"," all_shapes = strategy.gather(shapes, axis=0)"," else:"," # v is a tensor. This may happen when, say, we have 2x1 multi-worker."," all_shapes = strategy.gather("," tf.expand_dims(tf.shape(v)[0], axis=0), axis=0"," )",""," replicas = tf.split("," replicas,"," num_or_size_splits=all_shapes,"," num=strategy.num_replicas_in_sync,"," )"," ordered_replicas = []"," num_replicas_per_worker = len(strategy.extended.worker_devices)"," for replica_id in range(num_replicas_per_worker):"," ordered_replicas += replicas[replica_id::num_replicas_per_worker]"," return concat(ordered_replicas)","","","def _is_scalar(x):"," return isinstance(x, (tf.Tensor, tf.Variable)) and x.shape.rank == 0","","","def _minimum_control_deps(outputs):"," \"\"\"Returns the minimum control dependencies to ensure step succeeded.\"\"\""," if tf.executing_eagerly():"," return [] # Control dependencies not needed."," outputs = tf.nest.flatten(outputs, expand_composites=True)"," for out in outputs:"," # Variables can't be control dependencies."," if not isinstance(out, tf.Variable):"," return [out] # Return first Tensor or Op from outputs."," return [] # No viable Tensor or Op to use for control deps.","","","def _disallow_inside_tf_function(method_name):"," if tf.inside_function():"," error_msg = ("," \"Detected a call to `Model.{method_name}` inside a `tf.function`. \""," \"`Model.{method_name} is a high-level endpoint that manages its \""," \"own `tf.function`. Please move the call to `Model.{method_name}` \""," \"outside of all enclosing `tf.function`s. Note that you can call a \""," \"`Model` directly on `Tensor`s inside a `tf.function` like: \""," \"`model(x)`.\""," ).format(method_name=method_name)"," raise RuntimeError(error_msg)","","","def flatten_metrics_in_order(logs, metrics_names):"," \"\"\"Turns the `logs` dict into a list as per key order of `metrics_names`.\"\"\""," results = []"," for name in metrics_names:"," if name in logs:"," results.append(logs[name])"," for key in sorted(logs.keys()):"," if key not in metrics_names:"," results.append(logs[key])"," if len(results) == 1:"," return results[0]"," return results","","","def _is_per_replica_instance(obj):"," return isinstance(obj, tf.distribute.DistributedValues) and isinstance("," obj, tf.__internal__.CompositeTensor"," )","","","def _is_dtensor_per_replica_instance(obj):"," # This is a temp check for DTensorDistributedValue, which is not public API"," # yet."," # TODO(scottzhu): Move to more stable API when dtensor based strategy is"," # ready."," return isinstance(obj, tf.distribute.DistributedValues) and hasattr("," obj, \"_dtensor\""," )","","","def disable_multi_worker(method):"," \"\"\"Decorator that disallows multi-worker use of `method`.\"\"\"",""," def _method_wrapper(self, *args, **kwargs):"," if self._in_multi_worker_mode():"," raise ValueError("," f\"{method.__name__} is not supported in multi-worker \""," \"mode. Please use a non-multi-worker \""," \"`tf.distribute.Strategy` such as \""," \"`tf.distribute.MirroredStrategy`.\""," )"," return method(self, *args, **kwargs)",""," return tf.__internal__.decorator.make_decorator("," target=method, decorator_func=_method_wrapper"," )","","","def inject_functional_model_class(cls):"," \"\"\"Inject `Functional` into the hierarchy of this class if needed.\"\"\""," from tf_keras.engine import functional"," from tf_keras.engine import training_v1",""," if cls == Model or cls == training_v1.Model:"," return functional.Functional"," # In case there is any multiple inheritance, we stop injecting the"," # class if keras model is not in its class hierarchy."," if cls == object:"," return object",""," cls.__bases__ = tuple("," inject_functional_model_class(base) for base in cls.__bases__"," )"," # Trigger any `__new__` class swapping that needed to happen on `Functional`"," # but did not because functional was not in the class hierarchy."," cls.__new__(cls)",""," return cls","","","def is_functional_model_init_params(args, kwargs):"," # Both inputs and outputs in args"," if len(args) == 2:"," return True"," # Both inputs in args, outputs in kwargs"," if len(args) == 1 and \"outputs\" in kwargs:"," return True"," # Both in kwargs"," if \"inputs\" in kwargs and \"outputs\" in kwargs:"," return True"," return 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react-line-number react-code-text" style="padding-right:16px">987</div><div data-line-number="988" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">988</div><div data-line-number="989" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">989</div><div data-line-number="990" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">990</div><div data-line-number="991" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">991</div><div data-line-number="992" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">992</div><div data-line-number="993" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">993</div><div data-line-number="994" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">994</div><div data-line-number="995" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">995</div><div data-line-number="996" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">996</div><div data-line-number="997" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">997</div><div data-line-number="998" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">998</div><div data-line-number="999" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">999</div><div data-line-number="1000" class="child-of-line-70 child-of-line-965 react-line-number react-code-text" style="padding-right:16px">1000</div></div><div class="react-code-lines"><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC1" class="react-file-line html-div" data-testid="code-cell" data-line-number="1" style="position:relative"><span class="pl-c"># Copyright 2015 The TensorFlow Authors. All Rights Reserved.</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC2" class="react-file-line html-div" data-testid="code-cell" data-line-number="2" style="position:relative"><span class="pl-c">#</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC3" class="react-file-line html-div" data-testid="code-cell" data-line-number="3" style="position:relative"><span class="pl-c"># Licensed under the Apache License, Version 2.0 (the "License");</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC4" class="react-file-line html-div" data-testid="code-cell" data-line-number="4" style="position:relative"><span class="pl-c"># you may not use this file except in compliance with the License.</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC5" class="react-file-line html-div" data-testid="code-cell" data-line-number="5" style="position:relative"><span class="pl-c"># You may obtain a copy of the License at</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC6" class="react-file-line html-div" data-testid="code-cell" data-line-number="6" style="position:relative"><span class="pl-c">#</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC7" class="react-file-line html-div" data-testid="code-cell" data-line-number="7" style="position:relative"><span class="pl-c"># http://www.apache.org/licenses/LICENSE-2.0</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC8" class="react-file-line html-div" data-testid="code-cell" data-line-number="8" style="position:relative"><span class="pl-c">#</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC9" class="react-file-line html-div" data-testid="code-cell" data-line-number="9" style="position:relative"><span class="pl-c"># Unless required by applicable law or agreed to in writing, software</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC10" class="react-file-line html-div" data-testid="code-cell" data-line-number="10" style="position:relative"><span class="pl-c"># distributed under the License is distributed on an "AS IS" BASIS,</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC11" class="react-file-line html-div" data-testid="code-cell" data-line-number="11" style="position:relative"><span class="pl-c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC12" class="react-file-line html-div" data-testid="code-cell" data-line-number="12" style="position:relative"><span class="pl-c"># See the License for the specific language governing permissions and</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC13" class="react-file-line html-div" data-testid="code-cell" data-line-number="13" style="position:relative"><span class="pl-c"># limitations under the License.</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC14" class="react-file-line html-div" data-testid="code-cell" data-line-number="14" style="position:relative"><span class="pl-c"># ==============================================================================</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC15" class="react-file-line html-div" data-testid="code-cell" data-line-number="15" style="position:relative"><span class="pl-s">"""Training-related part of the TF-Keras engine."""</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC16" class="react-file-line html-div" data-testid="code-cell" data-line-number="16" style="position:relative"> </div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC17" class="react-file-line html-div" data-testid="code-cell" data-line-number="17" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">copy</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC18" class="react-file-line html-div" data-testid="code-cell" data-line-number="18" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">itertools</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC19" class="react-file-line html-div" data-testid="code-cell" data-line-number="19" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">json</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC20" class="react-file-line html-div" data-testid="code-cell" data-line-number="20" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">warnings</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC21" class="react-file-line html-div" data-testid="code-cell" data-line-number="21" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">weakref</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC22" class="react-file-line html-div" data-testid="code-cell" data-line-number="22" style="position:relative"> </div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC23" class="react-file-line html-div" data-testid="code-cell" data-line-number="23" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">np</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC24" class="react-file-line html-div" data-testid="code-cell" data-line-number="24" style="position:relative"><span class="pl-k">import</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">compat</span>.<span class="pl-s1">v2</span> <span class="pl-k">as</span> <span class="pl-s1">tf</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC25" class="react-file-line html-div" data-testid="code-cell" data-line-number="25" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">distribute</span> <span class="pl-k">import</span> <span class="pl-s1">distribute_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC26" class="react-file-line html-div" data-testid="code-cell" data-line-number="26" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">distribute</span> <span class="pl-k">import</span> <span class="pl-s1">input_ops</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC27" class="react-file-line html-div" data-testid="code-cell" data-line-number="27" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">eager</span> <span class="pl-k">import</span> <span class="pl-s1">context</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC28" class="react-file-line html-div" data-testid="code-cell" data-line-number="28" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">platform</span> <span class="pl-k">import</span> <span class="pl-s1">tf_logging</span> <span class="pl-k">as</span> <span class="pl-s1">logging</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC29" class="react-file-line html-div" data-testid="code-cell" data-line-number="29" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">util</span>.<span class="pl-s1">tf_export</span> <span class="pl-k">import</span> <span class="pl-s1">keras_export</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC30" class="react-file-line html-div" data-testid="code-cell" data-line-number="30" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">tools</span>.<span class="pl-s1">docs</span> <span class="pl-k">import</span> <span class="pl-s1">doc_controls</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC31" class="react-file-line html-div" data-testid="code-cell" data-line-number="31" style="position:relative"> </div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC32" class="react-file-line html-div" data-testid="code-cell" data-line-number="32" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span> <span class="pl-k">import</span> <span class="pl-s1">backend</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC33" class="react-file-line html-div" data-testid="code-cell" data-line-number="33" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span> <span class="pl-k">import</span> <span class="pl-s1">callbacks</span> <span class="pl-k">as</span> <span class="pl-s1">callbacks_module</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC34" class="react-file-line html-div" data-testid="code-cell" data-line-number="34" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span> <span class="pl-k">import</span> <span class="pl-s1">optimizers</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC35" class="react-file-line html-div" data-testid="code-cell" data-line-number="35" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">dtensor</span> <span class="pl-k">import</span> <span class="pl-s1">dtensor_api</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC36" class="react-file-line html-div" data-testid="code-cell" data-line-number="36" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">dtensor</span> <span class="pl-k">import</span> <span class="pl-s1">layout_map</span> <span class="pl-k">as</span> <span class="pl-s1">layout_map_lib</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC37" class="react-file-line html-div" data-testid="code-cell" data-line-number="37" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">base_layer</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC38" class="react-file-line html-div" data-testid="code-cell" data-line-number="38" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">base_layer_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC39" class="react-file-line html-div" data-testid="code-cell" data-line-number="39" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">compile_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC40" class="react-file-line html-div" data-testid="code-cell" data-line-number="40" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">data_adapter</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC41" class="react-file-line html-div" data-testid="code-cell" data-line-number="41" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">input_layer</span> <span class="pl-k">as</span> <span class="pl-s1">input_layer_module</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC42" class="react-file-line html-div" data-testid="code-cell" data-line-number="42" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">training_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC43" class="react-file-line html-div" data-testid="code-cell" data-line-number="43" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">metrics</span> <span class="pl-k">import</span> <span class="pl-s1">base_metric</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC44" class="react-file-line html-div" data-testid="code-cell" data-line-number="44" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">mixed_precision</span> <span class="pl-k">import</span> <span class="pl-s1">loss_scale_optimizer</span> <span class="pl-k">as</span> <span class="pl-s1">lso</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC45" class="react-file-line html-div" data-testid="code-cell" data-line-number="45" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">optimizers</span> <span class="pl-k">import</span> <span class="pl-s1">optimizer</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC46" class="react-file-line html-div" data-testid="code-cell" data-line-number="46" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">optimizers</span> <span class="pl-k">import</span> <span class="pl-s1">optimizer_v1</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC47" class="react-file-line html-div" data-testid="code-cell" data-line-number="47" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span> <span class="pl-k">import</span> <span class="pl-s1">pickle_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC48" class="react-file-line html-div" data-testid="code-cell" data-line-number="48" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span> <span class="pl-k">import</span> <span class="pl-s1">saving_api</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC49" class="react-file-line html-div" data-testid="code-cell" data-line-number="49" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span> <span class="pl-k">import</span> <span class="pl-s1">saving_lib</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC50" class="react-file-line html-div" data-testid="code-cell" data-line-number="50" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span> <span class="pl-k">import</span> <span class="pl-s1">serialization_lib</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC51" class="react-file-line html-div" data-testid="code-cell" data-line-number="51" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span>.<span class="pl-s1">legacy</span> <span class="pl-k">import</span> <span class="pl-s1">serialization</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC52" class="react-file-line html-div" data-testid="code-cell" data-line-number="52" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span>.<span class="pl-s1">legacy</span>.<span class="pl-s1">saved_model</span> <span class="pl-k">import</span> <span class="pl-s1">json_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC53" class="react-file-line html-div" data-testid="code-cell" data-line-number="53" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">saving</span>.<span class="pl-s1">legacy</span>.<span class="pl-s1">saved_model</span> <span class="pl-k">import</span> <span class="pl-s1">model_serialization</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC54" class="react-file-line html-div" data-testid="code-cell" data-line-number="54" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">generic_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC55" class="react-file-line html-div" data-testid="code-cell" data-line-number="55" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">io_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC56" class="react-file-line html-div" data-testid="code-cell" data-line-number="56" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">layer_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC57" class="react-file-line html-div" data-testid="code-cell" data-line-number="57" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">steps_per_execution_tuning</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC58" class="react-file-line html-div" data-testid="code-cell" data-line-number="58" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">tf_inspect</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC59" class="react-file-line html-div" data-testid="code-cell" data-line-number="59" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">tf_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC60" class="react-file-line html-div" data-testid="code-cell" data-line-number="60" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">traceback_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC61" class="react-file-line html-div" data-testid="code-cell" data-line-number="61" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">version_utils</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC62" class="react-file-line html-div" data-testid="code-cell" data-line-number="62" style="position:relative"><span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">utils</span>.<span class="pl-s1">mode_keys</span> <span class="pl-k">import</span> <span class="pl-v">ModeKeys</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC63" class="react-file-line html-div" data-testid="code-cell" data-line-number="63" style="position:relative"> </div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC64" class="react-file-line html-div" data-testid="code-cell" data-line-number="64" style="position:relative"><span class="pl-k">try</span>:</div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC65" class="react-file-line html-div" data-testid="code-cell" data-line-number="65" style="position:relative"> <span class="pl-k">import</span> <span class="pl-s1">h5py</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC66" class="react-file-line html-div" data-testid="code-cell" data-line-number="66" style="position:relative"><span class="pl-k">except</span> <span class="pl-v">ImportError</span>:</div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC67" class="react-file-line html-div" data-testid="code-cell" data-line-number="67" style="position:relative"> <span class="pl-s1">h5py</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC68" class="react-file-line html-div" data-testid="code-cell" data-line-number="68" style="position:relative"> </div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC69" class="react-file-line html-div" data-testid="code-cell" data-line-number="69" style="position:relative"> </div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC70" class="react-file-line html-div" data-testid="code-cell" data-line-number="70" style="position:relative"><span class="pl-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.Model"</span>, <span class="pl-s">"keras.models.Model"</span>)</span></div></div></div><div class="react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC71" class="react-file-line html-div" data-testid="code-cell" data-line-number="71" style="position:relative"><span class="pl-k">class</span> <span class="pl-v">Model</span>(<span class="pl-s1">base_layer</span>.<span class="pl-c1">Layer</span>, <span class="pl-s1">version_utils</span>.<span class="pl-c1">ModelVersionSelector</span>):</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC72" class="react-file-line html-div" data-testid="code-cell" data-line-number="72" style="position:relative"> <span class="pl-s">"""A model grouping layers into an object with training/inference features.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC73" class="react-file-line html-div" data-testid="code-cell" data-line-number="73" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC74" class="react-file-line html-div" data-testid="code-cell" data-line-number="74" style="position:relative"><span class="pl-s"> Args:</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC75" class="react-file-line html-div" data-testid="code-cell" data-line-number="75" style="position:relative"><span class="pl-s"> inputs: The input(s) of the model: a `keras.Input` object or a</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC76" class="react-file-line html-div" data-testid="code-cell" data-line-number="76" style="position:relative"><span class="pl-s"> combination of `keras.Input` objects in a dict, list or tuple.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC77" class="react-file-line html-div" data-testid="code-cell" data-line-number="77" style="position:relative"><span class="pl-s"> outputs: The output(s) of the model: a tensor that originated from</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC78" class="react-file-line html-div" data-testid="code-cell" data-line-number="78" style="position:relative"><span class="pl-s"> `keras.Input` objects or a combination of such tensors in a dict,</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC79" class="react-file-line html-div" data-testid="code-cell" data-line-number="79" style="position:relative"><span class="pl-s"> list or tuple. See Functional API example below.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC80" class="react-file-line html-div" data-testid="code-cell" data-line-number="80" style="position:relative"><span class="pl-s"> name: String, the name of the model.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC81" class="react-file-line html-div" data-testid="code-cell" data-line-number="81" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC82" class="react-file-line html-div" data-testid="code-cell" data-line-number="82" style="position:relative"><span class="pl-s"> There are two ways to instantiate a `Model`:</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC83" class="react-file-line html-div" data-testid="code-cell" data-line-number="83" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC84" class="react-file-line html-div" data-testid="code-cell" data-line-number="84" style="position:relative"><span class="pl-s"> 1 - With the "Functional API", where you start from `Input`,</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC85" class="react-file-line html-div" data-testid="code-cell" data-line-number="85" style="position:relative"><span class="pl-s"> you chain layer calls to specify the model's forward pass,</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC86" class="react-file-line html-div" data-testid="code-cell" data-line-number="86" style="position:relative"><span class="pl-s"> and finally you create your model from inputs and outputs:</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC87" class="react-file-line html-div" data-testid="code-cell" data-line-number="87" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC88" class="react-file-line html-div" data-testid="code-cell" data-line-number="88" style="position:relative"><span class="pl-s"> ```python</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC89" class="react-file-line html-div" data-testid="code-cell" data-line-number="89" style="position:relative"><span class="pl-s"> import tensorflow as tf</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC90" class="react-file-line html-div" data-testid="code-cell" data-line-number="90" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC91" class="react-file-line html-div" data-testid="code-cell" data-line-number="91" style="position:relative"><span class="pl-s"> inputs = tf.keras.Input(shape=(3,))</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC92" class="react-file-line html-div" data-testid="code-cell" data-line-number="92" style="position:relative"><span class="pl-s"> x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC93" class="react-file-line html-div" data-testid="code-cell" data-line-number="93" style="position:relative"><span class="pl-s"> outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC94" class="react-file-line html-div" data-testid="code-cell" data-line-number="94" style="position:relative"><span class="pl-s"> model = tf.keras.Model(inputs=inputs, outputs=outputs)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC95" class="react-file-line html-div" data-testid="code-cell" data-line-number="95" style="position:relative"><span class="pl-s"> ```</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC96" class="react-file-line html-div" data-testid="code-cell" data-line-number="96" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC97" class="react-file-line html-div" data-testid="code-cell" data-line-number="97" style="position:relative"><span class="pl-s"> Note: Only dicts, lists, and tuples of input tensors are supported. Nested</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC98" class="react-file-line html-div" data-testid="code-cell" data-line-number="98" style="position:relative"><span class="pl-s"> inputs are not supported (e.g. lists of list or dicts of dict).</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC99" class="react-file-line html-div" data-testid="code-cell" data-line-number="99" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC100" class="react-file-line html-div" data-testid="code-cell" data-line-number="100" style="position:relative"><span class="pl-s"> A new Functional API model can also be created by using the</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC101" class="react-file-line html-div" data-testid="code-cell" data-line-number="101" style="position:relative"><span class="pl-s"> intermediate tensors. This enables you to quickly extract sub-components</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC102" class="react-file-line html-div" data-testid="code-cell" data-line-number="102" style="position:relative"><span class="pl-s"> of the model.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC103" class="react-file-line html-div" data-testid="code-cell" data-line-number="103" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC104" class="react-file-line html-div" data-testid="code-cell" data-line-number="104" style="position:relative"><span class="pl-s"> Example:</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC105" class="react-file-line html-div" data-testid="code-cell" data-line-number="105" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC106" class="react-file-line html-div" data-testid="code-cell" data-line-number="106" style="position:relative"><span class="pl-s"> ```python</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC107" class="react-file-line html-div" data-testid="code-cell" data-line-number="107" style="position:relative"><span class="pl-s"> inputs = keras.Input(shape=(None, None, 3))</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC108" class="react-file-line html-div" data-testid="code-cell" data-line-number="108" style="position:relative"><span class="pl-s"> processed = keras.layers.RandomCrop(width=32, height=32)(inputs)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC109" class="react-file-line html-div" data-testid="code-cell" data-line-number="109" style="position:relative"><span class="pl-s"> conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC110" class="react-file-line html-div" data-testid="code-cell" data-line-number="110" style="position:relative"><span class="pl-s"> pooling = keras.layers.GlobalAveragePooling2D()(conv)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC111" class="react-file-line html-div" data-testid="code-cell" data-line-number="111" style="position:relative"><span class="pl-s"> feature = keras.layers.Dense(10)(pooling)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC112" class="react-file-line html-div" data-testid="code-cell" data-line-number="112" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC113" class="react-file-line html-div" data-testid="code-cell" data-line-number="113" style="position:relative"><span class="pl-s"> full_model = keras.Model(inputs, feature)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC114" class="react-file-line html-div" data-testid="code-cell" data-line-number="114" style="position:relative"><span class="pl-s"> backbone = keras.Model(processed, conv)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC115" class="react-file-line html-div" data-testid="code-cell" data-line-number="115" style="position:relative"><span class="pl-s"> activations = keras.Model(conv, feature)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC116" class="react-file-line html-div" data-testid="code-cell" data-line-number="116" style="position:relative"><span class="pl-s"> ```</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC117" class="react-file-line html-div" data-testid="code-cell" data-line-number="117" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC118" class="react-file-line html-div" data-testid="code-cell" data-line-number="118" style="position:relative"><span class="pl-s"> Note that the `backbone` and `activations` models are not</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC119" class="react-file-line html-div" data-testid="code-cell" data-line-number="119" style="position:relative"><span class="pl-s"> created with `keras.Input` objects, but with the tensors that are originated</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC120" class="react-file-line html-div" data-testid="code-cell" data-line-number="120" style="position:relative"><span class="pl-s"> from `keras.Input` objects. Under the hood, the layers and weights will</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC121" class="react-file-line html-div" data-testid="code-cell" data-line-number="121" style="position:relative"><span class="pl-s"> be shared across these models, so that user can train the `full_model`, and</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC122" class="react-file-line html-div" data-testid="code-cell" data-line-number="122" style="position:relative"><span class="pl-s"> use `backbone` or `activations` to do feature extraction.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC123" class="react-file-line html-div" data-testid="code-cell" data-line-number="123" style="position:relative"><span class="pl-s"> The inputs and outputs of the model can be nested structures of tensors as</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC124" class="react-file-line html-div" data-testid="code-cell" data-line-number="124" style="position:relative"><span class="pl-s"> well, and the created models are standard Functional API models that support</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC125" class="react-file-line html-div" data-testid="code-cell" data-line-number="125" style="position:relative"><span class="pl-s"> all the existing APIs.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC126" class="react-file-line html-div" data-testid="code-cell" data-line-number="126" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC127" class="react-file-line html-div" data-testid="code-cell" data-line-number="127" style="position:relative"><span class="pl-s"> 2 - By subclassing the `Model` class: in that case, you should define your</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC128" class="react-file-line html-div" data-testid="code-cell" data-line-number="128" style="position:relative"><span class="pl-s"> layers in `__init__()` and you should implement the model's forward pass</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC129" class="react-file-line html-div" data-testid="code-cell" data-line-number="129" style="position:relative"><span class="pl-s"> in `call()`.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC130" class="react-file-line html-div" data-testid="code-cell" data-line-number="130" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC131" class="react-file-line html-div" data-testid="code-cell" data-line-number="131" style="position:relative"><span class="pl-s"> ```python</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC132" class="react-file-line html-div" data-testid="code-cell" data-line-number="132" style="position:relative"><span class="pl-s"> import tensorflow as tf</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC133" class="react-file-line html-div" data-testid="code-cell" data-line-number="133" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC134" class="react-file-line html-div" data-testid="code-cell" data-line-number="134" style="position:relative"><span class="pl-s"> class MyModel(tf.keras.Model):</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC135" class="react-file-line html-div" data-testid="code-cell" data-line-number="135" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC136" class="react-file-line html-div" data-testid="code-cell" data-line-number="136" style="position:relative"><span class="pl-s"> def __init__(self):</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC137" class="react-file-line html-div" data-testid="code-cell" data-line-number="137" style="position:relative"><span class="pl-s"> super().__init__()</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC138" class="react-file-line html-div" data-testid="code-cell" data-line-number="138" style="position:relative"><span class="pl-s"> self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC139" class="react-file-line html-div" data-testid="code-cell" data-line-number="139" style="position:relative"><span class="pl-s"> self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC140" class="react-file-line html-div" data-testid="code-cell" data-line-number="140" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC141" class="react-file-line html-div" data-testid="code-cell" data-line-number="141" style="position:relative"><span class="pl-s"> def call(self, inputs):</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC142" class="react-file-line html-div" data-testid="code-cell" data-line-number="142" style="position:relative"><span class="pl-s"> x = self.dense1(inputs)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC143" class="react-file-line html-div" data-testid="code-cell" data-line-number="143" style="position:relative"><span class="pl-s"> return self.dense2(x)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC144" class="react-file-line html-div" data-testid="code-cell" data-line-number="144" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC145" class="react-file-line html-div" data-testid="code-cell" data-line-number="145" style="position:relative"><span class="pl-s"> model = MyModel()</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC146" class="react-file-line html-div" data-testid="code-cell" data-line-number="146" style="position:relative"><span class="pl-s"> ```</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC147" class="react-file-line html-div" data-testid="code-cell" data-line-number="147" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC148" class="react-file-line html-div" data-testid="code-cell" data-line-number="148" style="position:relative"><span class="pl-s"> If you subclass `Model`, you can optionally have</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC149" class="react-file-line html-div" data-testid="code-cell" data-line-number="149" style="position:relative"><span class="pl-s"> a `training` argument (boolean) in `call()`, which you can use to specify</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC150" class="react-file-line html-div" data-testid="code-cell" data-line-number="150" style="position:relative"><span class="pl-s"> a different behavior in training and inference:</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC151" class="react-file-line html-div" data-testid="code-cell" data-line-number="151" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC152" class="react-file-line html-div" data-testid="code-cell" data-line-number="152" style="position:relative"><span class="pl-s"> ```python</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC153" class="react-file-line html-div" data-testid="code-cell" data-line-number="153" style="position:relative"><span class="pl-s"> import tensorflow as tf</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC154" class="react-file-line html-div" data-testid="code-cell" data-line-number="154" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC155" class="react-file-line html-div" data-testid="code-cell" data-line-number="155" style="position:relative"><span class="pl-s"> class MyModel(tf.keras.Model):</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC156" class="react-file-line html-div" data-testid="code-cell" data-line-number="156" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC157" class="react-file-line html-div" data-testid="code-cell" data-line-number="157" style="position:relative"><span class="pl-s"> def __init__(self):</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC158" class="react-file-line html-div" data-testid="code-cell" data-line-number="158" style="position:relative"><span class="pl-s"> super().__init__()</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC159" class="react-file-line html-div" data-testid="code-cell" data-line-number="159" style="position:relative"><span class="pl-s"> self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC160" class="react-file-line html-div" data-testid="code-cell" data-line-number="160" style="position:relative"><span class="pl-s"> self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC161" class="react-file-line html-div" data-testid="code-cell" data-line-number="161" style="position:relative"><span class="pl-s"> self.dropout = tf.keras.layers.Dropout(0.5)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC162" class="react-file-line html-div" data-testid="code-cell" data-line-number="162" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC163" class="react-file-line html-div" data-testid="code-cell" data-line-number="163" style="position:relative"><span class="pl-s"> def call(self, inputs, training=False):</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC164" class="react-file-line html-div" data-testid="code-cell" data-line-number="164" style="position:relative"><span class="pl-s"> x = self.dense1(inputs)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC165" class="react-file-line html-div" data-testid="code-cell" data-line-number="165" style="position:relative"><span class="pl-s"> if training:</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC166" class="react-file-line html-div" data-testid="code-cell" data-line-number="166" style="position:relative"><span class="pl-s"> x = self.dropout(x, training=training)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC167" class="react-file-line html-div" data-testid="code-cell" data-line-number="167" style="position:relative"><span class="pl-s"> return self.dense2(x)</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC168" class="react-file-line html-div" data-testid="code-cell" data-line-number="168" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC169" class="react-file-line html-div" data-testid="code-cell" data-line-number="169" style="position:relative"><span class="pl-s"> model = MyModel()</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC170" class="react-file-line html-div" data-testid="code-cell" data-line-number="170" style="position:relative"><span class="pl-s"> ```</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC171" class="react-file-line html-div" data-testid="code-cell" data-line-number="171" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC172" class="react-file-line html-div" data-testid="code-cell" data-line-number="172" style="position:relative"><span class="pl-s"> Once the model is created, you can config the model with losses and metrics</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC173" class="react-file-line html-div" data-testid="code-cell" data-line-number="173" style="position:relative"><span class="pl-s"> with `model.compile()`, train the model with `model.fit()`, or use the model</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC174" class="react-file-line html-div" data-testid="code-cell" data-line-number="174" style="position:relative"><span class="pl-s"> to do prediction with `model.predict()`.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC175" class="react-file-line html-div" data-testid="code-cell" data-line-number="175" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC176" class="react-file-line html-div" data-testid="code-cell" data-line-number="176" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC177" class="react-file-line html-div" data-testid="code-cell" data-line-number="177" style="position:relative"> <span class="pl-s1">_TF_MODULE_IGNORED_PROPERTIES</span> <span class="pl-c1">=</span> <span class="pl-en">frozenset</span>(</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC178" class="react-file-line html-div" data-testid="code-cell" data-line-number="178" style="position:relative"> <span class="pl-s1">itertools</span>.<span class="pl-c1">chain</span>(</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC179" class="react-file-line html-div" data-testid="code-cell" data-line-number="179" style="position:relative"> (</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC180" class="react-file-line html-div" data-testid="code-cell" data-line-number="180" style="position:relative"> <span class="pl-s">"_train_counter"</span>,</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC181" class="react-file-line html-div" data-testid="code-cell" data-line-number="181" style="position:relative"> <span class="pl-s">"_test_counter"</span>,</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC182" class="react-file-line html-div" data-testid="code-cell" data-line-number="182" style="position:relative"> <span class="pl-s">"_predict_counter"</span>,</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC183" class="react-file-line html-div" data-testid="code-cell" data-line-number="183" style="position:relative"> <span class="pl-s">"_steps_per_execution"</span>,</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC184" class="react-file-line html-div" data-testid="code-cell" data-line-number="184" style="position:relative"> <span class="pl-s">"_compiled_trainable_state"</span>,</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC185" class="react-file-line html-div" data-testid="code-cell" data-line-number="185" style="position:relative"> ),</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC186" class="react-file-line html-div" data-testid="code-cell" data-line-number="186" style="position:relative"> <span class="pl-s1">base_layer</span>.<span class="pl-c1">Layer</span>.<span class="pl-c1">_TF_MODULE_IGNORED_PROPERTIES</span>,</div></div></div><div class="child-of-line-70 child-of-line-176 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC187" class="react-file-line html-div" data-testid="code-cell" data-line-number="187" style="position:relative"> )</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC188" class="react-file-line html-div" data-testid="code-cell" data-line-number="188" style="position:relative"> )</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC189" class="react-file-line html-div" data-testid="code-cell" data-line-number="189" style="position:relative"> <span class="pl-s1">_SCALAR_UPRANKING_ON</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC190" class="react-file-line html-div" data-testid="code-cell" data-line-number="190" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC191" class="react-file-line html-div" data-testid="code-cell" data-line-number="191" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__new__</span>(<span class="pl-s1">cls</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>):</div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC192" class="react-file-line html-div" data-testid="code-cell" data-line-number="192" style="position:relative"> <span class="pl-c"># Signature detection</span></div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC193" class="react-file-line html-div" data-testid="code-cell" data-line-number="193" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">is_functional_model_init_params</span>(<span class="pl-s1">args</span>, <span class="pl-s1">kwargs</span>) <span class="pl-c1">and</span> <span class="pl-s1">cls</span> <span class="pl-c1">==</span> <span class="pl-v">Model</span>:</div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC194" class="react-file-line html-div" data-testid="code-cell" data-line-number="194" style="position:relative"> <span class="pl-c"># Functional model</span></div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC195" class="react-file-line html-div" data-testid="code-cell" data-line-number="195" style="position:relative"> <span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">functional</span></div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC196" class="react-file-line html-div" data-testid="code-cell" data-line-number="196" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC197" class="react-file-line html-div" data-testid="code-cell" data-line-number="197" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">functional</span>.<span class="pl-c1">Functional</span>(<span class="pl-s1">skip_init</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-190 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC198" class="react-file-line html-div" data-testid="code-cell" data-line-number="198" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC199" class="react-file-line html-div" data-testid="code-cell" data-line-number="199" style="position:relative"> <span class="pl-k">return</span> <span class="pl-en">super</span>(<span class="pl-v">Model</span>, <span class="pl-s1">cls</span>).<span class="pl-c1">__new__</span>(<span class="pl-s1">cls</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC200" class="react-file-line html-div" data-testid="code-cell" data-line-number="200" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC201" class="react-file-line html-div" data-testid="code-cell" data-line-number="201" style="position:relative"> <span class="pl-en">@<span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">tracking</span>.<span class="pl-c1">no_automatic_dependency_tracking</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC202" class="react-file-line html-div" data-testid="code-cell" data-line-number="202" style="position:relative"> <span class="pl-en">@<span class="pl-s1">traceback_utils</span>.<span class="pl-c1">filter_traceback</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC203" class="react-file-line html-div" data-testid="code-cell" data-line-number="203" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>):</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC204" class="react-file-line html-div" data-testid="code-cell" data-line-number="204" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_is_model_for_instrumentation</span> <span class="pl-c1">=</span> <span class="pl-c1">True</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC205" class="react-file-line html-div" data-testid="code-cell" data-line-number="205" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC206" class="react-file-line html-div" data-testid="code-cell" data-line-number="206" style="position:relative"> <span class="pl-c"># Special case for Subclassed Functional Model, which we couldn't detect</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC207" class="react-file-line html-div" data-testid="code-cell" data-line-number="207" style="position:relative"> <span class="pl-c"># when __new__ is called. We only realize it is a functional model when</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC208" class="react-file-line html-div" data-testid="code-cell" data-line-number="208" style="position:relative"> <span class="pl-c"># it calls super.__init__ with input and output tensor.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC209" class="react-file-line html-div" data-testid="code-cell" data-line-number="209" style="position:relative"> <span class="pl-k">from</span> <span class="pl-s1">tf_keras</span>.<span class="pl-s1">engine</span> <span class="pl-k">import</span> <span class="pl-s1">functional</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC210" class="react-file-line html-div" data-testid="code-cell" data-line-number="210" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC211" class="react-file-line html-div" data-testid="code-cell" data-line-number="211" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">is_functional_model_init_params</span>(<span class="pl-s1">args</span>, <span class="pl-s1">kwargs</span>) <span class="pl-c1">and</span> <span class="pl-c1">not</span> <span class="pl-en">isinstance</span>(</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC212" class="react-file-line html-div" data-testid="code-cell" data-line-number="212" style="position:relative"> <span class="pl-s1">self</span>, <span class="pl-s1">functional</span>.<span class="pl-c1">Functional</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC213" class="react-file-line html-div" data-testid="code-cell" data-line-number="213" style="position:relative"> ):</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC214" class="react-file-line html-div" data-testid="code-cell" data-line-number="214" style="position:relative"> <span class="pl-c"># Filter the kwargs for multiple inheritance.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC215" class="react-file-line html-div" data-testid="code-cell" data-line-number="215" style="position:relative"> <span class="pl-s1">supported_kwargs</span> <span class="pl-c1">=</span> [</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC216" class="react-file-line html-div" data-testid="code-cell" data-line-number="216" style="position:relative"> <span class="pl-s">"inputs"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC217" class="react-file-line html-div" data-testid="code-cell" data-line-number="217" style="position:relative"> <span class="pl-s">"outputs"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC218" class="react-file-line html-div" data-testid="code-cell" data-line-number="218" style="position:relative"> <span class="pl-s">"name"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC219" class="react-file-line html-div" data-testid="code-cell" data-line-number="219" style="position:relative"> <span class="pl-s">"trainable"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC220" class="react-file-line html-div" data-testid="code-cell" data-line-number="220" style="position:relative"> <span class="pl-s">"skip_init"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC221" class="react-file-line html-div" data-testid="code-cell" data-line-number="221" style="position:relative"> ]</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC222" class="react-file-line html-div" data-testid="code-cell" data-line-number="222" style="position:relative"> <span class="pl-s1">model_kwargs</span> <span class="pl-c1">=</span> {</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC223" class="react-file-line html-div" data-testid="code-cell" data-line-number="223" style="position:relative"> <span class="pl-s1">k</span>: <span class="pl-s1">kwargs</span>[<span class="pl-s1">k</span>] <span class="pl-k">for</span> <span class="pl-s1">k</span> <span class="pl-c1">in</span> <span class="pl-s1">kwargs</span> <span class="pl-k">if</span> <span class="pl-s1">k</span> <span class="pl-c1">in</span> <span class="pl-s1">supported_kwargs</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC224" class="react-file-line html-div" data-testid="code-cell" data-line-number="224" style="position:relative"> }</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC225" class="react-file-line html-div" data-testid="code-cell" data-line-number="225" style="position:relative"> <span class="pl-s1">other_kwargs</span> <span class="pl-c1">=</span> {</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC226" class="react-file-line html-div" data-testid="code-cell" data-line-number="226" style="position:relative"> <span class="pl-s1">k</span>: <span class="pl-s1">kwargs</span>[<span class="pl-s1">k</span>] <span class="pl-k">for</span> <span class="pl-s1">k</span> <span class="pl-c1">in</span> <span class="pl-s1">kwargs</span> <span class="pl-k">if</span> <span class="pl-s1">k</span> <span class="pl-c1"><span class="pl-c1">not</span> <span class="pl-c1">in</span></span> <span class="pl-s1">supported_kwargs</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC227" class="react-file-line html-div" data-testid="code-cell" data-line-number="227" style="position:relative"> }</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC228" class="react-file-line html-div" data-testid="code-cell" data-line-number="228" style="position:relative"> <span class="pl-en">inject_functional_model_class</span>(<span class="pl-s1">self</span>.<span class="pl-c1">__class__</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC229" class="react-file-line html-div" data-testid="code-cell" data-line-number="229" style="position:relative"> <span class="pl-s1">functional</span>.<span class="pl-c1">Functional</span>.<span class="pl-c1">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">model_kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC230" class="react-file-line html-div" data-testid="code-cell" data-line-number="230" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC231" class="react-file-line html-div" data-testid="code-cell" data-line-number="231" style="position:relative"> <span class="pl-c"># In case there is any multiple inheritance here, we need to call</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC232" class="react-file-line html-div" data-testid="code-cell" data-line-number="232" style="position:relative"> <span class="pl-c"># the __init__ for any class that appears after the Functional</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC233" class="react-file-line html-div" data-testid="code-cell" data-line-number="233" style="position:relative"> <span class="pl-c"># class.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC234" class="react-file-line html-div" data-testid="code-cell" data-line-number="234" style="position:relative"> <span class="pl-s1">clz_to_init</span> <span class="pl-c1">=</span> []</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC235" class="react-file-line html-div" data-testid="code-cell" data-line-number="235" style="position:relative"> <span class="pl-s1">found_functional_class</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC236" class="react-file-line html-div" data-testid="code-cell" data-line-number="236" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">clz</span> <span class="pl-c1">in</span> <span class="pl-s1">self</span>.<span class="pl-c1">__class__</span>.<span class="pl-c1">__bases__</span>:</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC237" class="react-file-line html-div" data-testid="code-cell" data-line-number="237" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">issubclass</span>(<span class="pl-s1">clz</span>, <span class="pl-s1">functional</span>.<span class="pl-c1">Functional</span>):</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC238" class="react-file-line html-div" data-testid="code-cell" data-line-number="238" style="position:relative"> <span class="pl-s1">found_functional_class</span> <span class="pl-c1">=</span> <span class="pl-c1">True</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC239" class="react-file-line html-div" data-testid="code-cell" data-line-number="239" style="position:relative"> <span class="pl-k">continue</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC240" class="react-file-line html-div" data-testid="code-cell" data-line-number="240" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">found_functional_class</span>:</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC241" class="react-file-line html-div" data-testid="code-cell" data-line-number="241" style="position:relative"> <span class="pl-s1">clz_to_init</span>.<span class="pl-c1">append</span>(<span class="pl-s1">clz</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC242" class="react-file-line html-div" data-testid="code-cell" data-line-number="242" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC243" class="react-file-line html-div" data-testid="code-cell" data-line-number="243" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">clz_to_init</span>:</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC244" class="react-file-line html-div" data-testid="code-cell" data-line-number="244" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">clz</span> <span class="pl-c1">in</span> <span class="pl-s1">clz_to_init</span>:</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC245" class="react-file-line html-div" data-testid="code-cell" data-line-number="245" style="position:relative"> <span class="pl-s1">clz</span>.<span class="pl-c1">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">other_kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC246" class="react-file-line html-div" data-testid="code-cell" data-line-number="246" style="position:relative"> <span class="pl-k">elif</span> <span class="pl-s1">other_kwargs</span>:</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC247" class="react-file-line html-div" data-testid="code-cell" data-line-number="247" style="position:relative"> <span class="pl-c"># In case there are unused kwargs, we should raise an error to</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC248" class="react-file-line html-div" data-testid="code-cell" data-line-number="248" style="position:relative"> <span class="pl-c"># user, in case they have a typo in the param name.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC249" class="react-file-line html-div" data-testid="code-cell" data-line-number="249" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">TypeError</span>(</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC250" class="react-file-line html-div" data-testid="code-cell" data-line-number="250" style="position:relative"> <span class="pl-s">"The following keyword arguments passed to `Model` aren't "</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC251" class="react-file-line html-div" data-testid="code-cell" data-line-number="251" style="position:relative"> <span class="pl-s">"supported: {}."</span>.<span class="pl-c1">format</span>(<span class="pl-s1">other_kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC252" class="react-file-line html-div" data-testid="code-cell" data-line-number="252" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC253" class="react-file-line html-div" data-testid="code-cell" data-line-number="253" style="position:relative"> <span class="pl-k">return</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC254" class="react-file-line html-div" data-testid="code-cell" data-line-number="254" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC255" class="react-file-line html-div" data-testid="code-cell" data-line-number="255" style="position:relative"> <span class="pl-c"># The following are implemented as property functions:</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC256" class="react-file-line html-div" data-testid="code-cell" data-line-number="256" style="position:relative"> <span class="pl-c"># self.trainable_weights</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC257" class="react-file-line html-div" data-testid="code-cell" data-line-number="257" style="position:relative"> <span class="pl-c"># self.non_trainable_weights</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC258" class="react-file-line html-div" data-testid="code-cell" data-line-number="258" style="position:relative"> <span class="pl-c"># `inputs` / `outputs` will only appear in kwargs if either are</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC259" class="react-file-line html-div" data-testid="code-cell" data-line-number="259" style="position:relative"> <span class="pl-c"># misspelled.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC260" class="react-file-line html-div" data-testid="code-cell" data-line-number="260" style="position:relative"> <span class="pl-s1">generic_utils</span>.<span class="pl-c1">validate_kwargs</span>(</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC261" class="react-file-line html-div" data-testid="code-cell" data-line-number="261" style="position:relative"> <span class="pl-s1">kwargs</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC262" class="react-file-line html-div" data-testid="code-cell" data-line-number="262" style="position:relative"> {</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC263" class="react-file-line html-div" data-testid="code-cell" data-line-number="263" style="position:relative"> <span class="pl-s">"trainable"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC264" class="react-file-line html-div" data-testid="code-cell" data-line-number="264" style="position:relative"> <span class="pl-s">"dtype"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC265" class="react-file-line html-div" data-testid="code-cell" data-line-number="265" style="position:relative"> <span class="pl-s">"dynamic"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC266" class="react-file-line html-div" data-testid="code-cell" data-line-number="266" style="position:relative"> <span class="pl-s">"name"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC267" class="react-file-line html-div" data-testid="code-cell" data-line-number="267" style="position:relative"> <span class="pl-s">"autocast"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC268" class="react-file-line html-div" data-testid="code-cell" data-line-number="268" style="position:relative"> <span class="pl-s">"inputs"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC269" class="react-file-line html-div" data-testid="code-cell" data-line-number="269" style="position:relative"> <span class="pl-s">"outputs"</span>,</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC270" class="react-file-line html-div" data-testid="code-cell" data-line-number="270" style="position:relative"> },</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC271" class="react-file-line html-div" data-testid="code-cell" data-line-number="271" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC272" class="react-file-line html-div" data-testid="code-cell" data-line-number="272" style="position:relative"> <span class="pl-en">super</span>().<span class="pl-c1">__init__</span>(<span class="pl-c1">**</span><span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC273" class="react-file-line html-div" data-testid="code-cell" data-line-number="273" style="position:relative"> <span class="pl-c"># By default, Model is a subclass model, which is not in graph network.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC274" class="react-file-line html-div" data-testid="code-cell" data-line-number="274" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_is_graph_network</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC275" class="react-file-line html-div" data-testid="code-cell" data-line-number="275" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC276" class="react-file-line html-div" data-testid="code-cell" data-line-number="276" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">inputs</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC277" class="react-file-line html-div" data-testid="code-cell" data-line-number="277" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">outputs</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC278" class="react-file-line html-div" data-testid="code-cell" data-line-number="278" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">input_names</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC279" class="react-file-line html-div" data-testid="code-cell" data-line-number="279" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">output_names</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC280" class="react-file-line html-div" data-testid="code-cell" data-line-number="280" style="position:relative"> <span class="pl-c"># stop_training is used by callback to stop training when error happens</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC281" class="react-file-line html-div" data-testid="code-cell" data-line-number="281" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">stop_training</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC282" class="react-file-line html-div" data-testid="code-cell" data-line-number="282" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">history</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC283" class="react-file-line html-div" data-testid="code-cell" data-line-number="283" style="position:relative"> <span class="pl-c"># These objects are used in the default `Model.compile`. They are not</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC284" class="react-file-line html-div" data-testid="code-cell" data-line-number="284" style="position:relative"> <span class="pl-c"># guaranteed to be set after `Model.compile` is called, as users can</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC285" class="react-file-line html-div" data-testid="code-cell" data-line-number="285" style="position:relative"> <span class="pl-c"># override compile with custom logic.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC286" class="react-file-line html-div" data-testid="code-cell" data-line-number="286" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_loss</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC287" class="react-file-line html-div" data-testid="code-cell" data-line-number="287" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_metrics</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC288" class="react-file-line html-div" data-testid="code-cell" data-line-number="288" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC289" class="react-file-line html-div" data-testid="code-cell" data-line-number="289" style="position:relative"> <span class="pl-c"># This is True for Sequential networks and Functional networks.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC290" class="react-file-line html-div" data-testid="code-cell" data-line-number="290" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_compute_output_and_mask_jointly</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC291" class="react-file-line html-div" data-testid="code-cell" data-line-number="291" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC292" class="react-file-line html-div" data-testid="code-cell" data-line-number="292" style="position:relative"> <span class="pl-c"># Don't reset compilation if already done. This may occur if calling</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC293" class="react-file-line html-div" data-testid="code-cell" data-line-number="293" style="position:relative"> <span class="pl-c"># `__init__` (or `_init_graph_network`) on an already-compiled model</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC294" class="react-file-line html-div" data-testid="code-cell" data-line-number="294" style="position:relative"> <span class="pl-c"># such as a Sequential model. Sequential models may need to rebuild</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC295" class="react-file-line html-div" data-testid="code-cell" data-line-number="295" style="position:relative"> <span class="pl-c"># themselves after compilation.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC296" class="react-file-line html-div" data-testid="code-cell" data-line-number="296" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_maybe_create_attribute</span>(<span class="pl-s">"_is_compiled"</span>, <span class="pl-c1">False</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC297" class="react-file-line html-div" data-testid="code-cell" data-line-number="297" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_maybe_create_attribute</span>(<span class="pl-s">"optimizer"</span>, <span class="pl-c1">None</span>)</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC298" class="react-file-line html-div" data-testid="code-cell" data-line-number="298" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC299" class="react-file-line html-div" data-testid="code-cell" data-line-number="299" style="position:relative"> <span class="pl-c"># Model must be created under scope of DistStrat it will be trained</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC300" class="react-file-line html-div" data-testid="code-cell" data-line-number="300" style="position:relative"> <span class="pl-c"># with.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC301" class="react-file-line html-div" data-testid="code-cell" data-line-number="301" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">tf</span>.<span class="pl-c1">distribute</span>.<span class="pl-c1">has_strategy</span>():</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC302" class="react-file-line html-div" data-testid="code-cell" data-line-number="302" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_distribution_strategy</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">distribute</span>.<span class="pl-c1">get_strategy</span>()</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC303" class="react-file-line html-div" data-testid="code-cell" data-line-number="303" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC304" class="react-file-line html-div" data-testid="code-cell" data-line-number="304" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_distribution_strategy</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC305" class="react-file-line html-div" data-testid="code-cell" data-line-number="305" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_distribute_reduction_method</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC306" class="react-file-line html-div" data-testid="code-cell" data-line-number="306" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC307" class="react-file-line html-div" data-testid="code-cell" data-line-number="307" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_cluster_coordinator</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC308" class="react-file-line html-div" data-testid="code-cell" data-line-number="308" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC309" class="react-file-line html-div" data-testid="code-cell" data-line-number="309" style="position:relative"> <span class="pl-c"># Defaults to value of `tf.config.experimental_functions_run_eagerly`.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC310" class="react-file-line html-div" data-testid="code-cell" data-line-number="310" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_run_eagerly</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC311" class="react-file-line html-div" data-testid="code-cell" data-line-number="311" style="position:relative"> <span class="pl-c"># Initialize cache attrs.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC312" class="react-file-line html-div" data-testid="code-cell" data-line-number="312" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_reset_compile_cache</span>()</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC313" class="react-file-line html-div" data-testid="code-cell" data-line-number="313" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC314" class="react-file-line html-div" data-testid="code-cell" data-line-number="314" style="position:relative"> <span class="pl-c"># Fault-tolerance handler. Set in `ModelCheckpoint`.</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC315" class="react-file-line html-div" data-testid="code-cell" data-line-number="315" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_training_state</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC316" class="react-file-line html-div" data-testid="code-cell" data-line-number="316" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_saved_model_inputs_spec</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC317" class="react-file-line html-div" data-testid="code-cell" data-line-number="317" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_saved_model_arg_spec</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC318" class="react-file-line html-div" data-testid="code-cell" data-line-number="318" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_checkpoint</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">train</span>.<span class="pl-c1">Checkpoint</span>(<span class="pl-s1">root</span><span class="pl-c1">=</span><span class="pl-s1">weakref</span>.<span class="pl-c1">ref</span>(<span class="pl-s1">self</span>))</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC319" class="react-file-line html-div" data-testid="code-cell" data-line-number="319" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC320" class="react-file-line html-div" data-testid="code-cell" data-line-number="320" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_steps_per_execution</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC321" class="react-file-line html-div" data-testid="code-cell" data-line-number="321" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_steps_per_execution_tuner</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC322" class="react-file-line html-div" data-testid="code-cell" data-line-number="322" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_autotune_steps_per_execution</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC323" class="react-file-line html-div" data-testid="code-cell" data-line-number="323" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC324" class="react-file-line html-div" data-testid="code-cell" data-line-number="324" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span> <span class="pl-c1">=</span> <span class="pl-s1">layout_map_lib</span>.<span class="pl-c1">get_current_layout_map</span>()</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC325" class="react-file-line html-div" data-testid="code-cell" data-line-number="325" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC326" class="react-file-line html-div" data-testid="code-cell" data-line-number="326" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_init_batch_counters</span>()</div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC327" class="react-file-line html-div" data-testid="code-cell" data-line-number="327" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_base_model_initialized</span> <span class="pl-c1">=</span> <span class="pl-c1">True</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC328" class="react-file-line html-div" data-testid="code-cell" data-line-number="328" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC329" class="react-file-line html-div" data-testid="code-cell" data-line-number="329" style="position:relative"> <span class="pl-c"># `jit_compile` starts off with None as default and gets overwritten by</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC330" class="react-file-line html-div" data-testid="code-cell" data-line-number="330" style="position:relative"> <span class="pl-c"># the value specified in `Model.compile`, and this is effective for</span></div></div></div><div class="child-of-line-70 child-of-line-202 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC331" class="react-file-line html-div" data-testid="code-cell" data-line-number="331" style="position:relative"> <span class="pl-c"># `fit`, `evaluate`, and `predict`.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC332" class="react-file-line html-div" data-testid="code-cell" data-line-number="332" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_jit_compile</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC333" class="react-file-line html-div" data-testid="code-cell" data-line-number="333" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC334" class="react-file-line html-div" data-testid="code-cell" data-line-number="334" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_create_counter_variable</span>(<span class="pl-s1">self</span>, <span class="pl-s1">init_value</span>):</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC335" class="react-file-line html-div" data-testid="code-cell" data-line-number="335" style="position:relative"> <span class="pl-s">"""Helper function for counter variable creation.</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC336" class="react-file-line html-div" data-testid="code-cell" data-line-number="336" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC337" class="react-file-line html-div" data-testid="code-cell" data-line-number="337" style="position:relative"><span class="pl-s"> For the DTensor use case with layout map, since the variable are not</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC338" class="react-file-line html-div" data-testid="code-cell" data-line-number="338" style="position:relative"><span class="pl-s"> tracked by model, they can't be visited by the layout map, and need to</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC339" class="react-file-line html-div" data-testid="code-cell" data-line-number="339" style="position:relative"><span class="pl-s"> be properly initialized as DVariable.</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC340" class="react-file-line html-div" data-testid="code-cell" data-line-number="340" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC341" class="react-file-line html-div" data-testid="code-cell" data-line-number="341" style="position:relative"> <span class="pl-c"># This function should be removed after we move to the strategy based</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC342" class="react-file-line html-div" data-testid="code-cell" data-line-number="342" style="position:relative"> <span class="pl-c"># implementation for DTensor.</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC343" class="react-file-line html-div" data-testid="code-cell" data-line-number="343" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span> <span class="pl-c1">is</span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC344" class="react-file-line html-div" data-testid="code-cell" data-line-number="344" style="position:relative"> <span class="pl-s1">agg</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">VariableAggregation</span>.<span class="pl-c1">ONLY_FIRST_REPLICA</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC345" class="react-file-line html-div" data-testid="code-cell" data-line-number="345" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">tf</span>.<span class="pl-c1">Variable</span>(<span class="pl-s1">init_value</span>, <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s">"int64"</span>, <span class="pl-s1">aggregation</span><span class="pl-c1">=</span><span class="pl-s1">agg</span>)</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC346" class="react-file-line html-div" data-testid="code-cell" data-line-number="346" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC347" class="react-file-line html-div" data-testid="code-cell" data-line-number="347" style="position:relative"> <span class="pl-s1">layout</span> <span class="pl-c1">=</span> <span class="pl-s1">dtensor_api</span>.<span class="pl-c1">Layout</span>.<span class="pl-c1">replicated</span>(</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC348" class="react-file-line html-div" data-testid="code-cell" data-line-number="348" style="position:relative"> <span class="pl-s1">mesh</span><span class="pl-c1">=</span><span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span>.<span class="pl-c1">get_default_mesh</span>(), <span class="pl-s1">rank</span><span class="pl-c1">=</span><span class="pl-c1">0</span></div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC349" class="react-file-line html-div" data-testid="code-cell" data-line-number="349" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC350" class="react-file-line html-div" data-testid="code-cell" data-line-number="350" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">dtensor_api</span>.<span class="pl-c1">DVariable</span>(</div></div></div><div class="child-of-line-70 child-of-line-333 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC351" class="react-file-line html-div" data-testid="code-cell" data-line-number="351" style="position:relative"> <span class="pl-s1">init_value</span>, <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s">"int64"</span>, <span class="pl-s1">layout</span><span class="pl-c1">=</span><span class="pl-s1">layout</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC352" class="react-file-line html-div" data-testid="code-cell" data-line-number="352" style="position:relative"> )</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC353" class="react-file-line html-div" data-testid="code-cell" data-line-number="353" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC354" class="react-file-line html-div" data-testid="code-cell" data-line-number="354" style="position:relative"> <span class="pl-en">@<span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">tracking</span>.<span class="pl-c1">no_automatic_dependency_tracking</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC355" class="react-file-line html-div" data-testid="code-cell" data-line-number="355" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_init_batch_counters</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC356" class="react-file-line html-div" data-testid="code-cell" data-line-number="356" style="position:relative"> <span class="pl-c"># Untracked Variables, used to keep track of mini-batches seen in `fit`,</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC357" class="react-file-line html-div" data-testid="code-cell" data-line-number="357" style="position:relative"> <span class="pl-c"># `evaluate`, and `predict`.</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC358" class="react-file-line html-div" data-testid="code-cell" data-line-number="358" style="position:relative"> <span class="pl-k">if</span> <span class="pl-c1">not</span> <span class="pl-s1">tf</span>.<span class="pl-c1">inside_function</span>():</div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC359" class="react-file-line html-div" data-testid="code-cell" data-line-number="359" style="position:relative"> <span class="pl-c"># Creating variables inside tf.function is not allowed, hence</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC360" class="react-file-line html-div" data-testid="code-cell" data-line-number="360" style="position:relative"> <span class="pl-c"># these would otherwise prevent users from creating TF-Keras layers</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC361" class="react-file-line html-div" data-testid="code-cell" data-line-number="361" style="position:relative"> <span class="pl-c"># inside tf.function.</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC362" class="react-file-line html-div" data-testid="code-cell" data-line-number="362" style="position:relative"> <span class="pl-c"># These variables are not connected to outputs so they have no</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC363" class="react-file-line html-div" data-testid="code-cell" data-line-number="363" style="position:relative"> <span class="pl-c"># effect on graph generation anyway.</span></div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC364" class="react-file-line html-div" data-testid="code-cell" data-line-number="364" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC365" class="react-file-line html-div" data-testid="code-cell" data-line-number="365" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_train_counter</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_create_counter_variable</span>(<span class="pl-c1">0</span>)</div></div></div><div class="child-of-line-70 child-of-line-354 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC366" class="react-file-line html-div" data-testid="code-cell" data-line-number="366" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_test_counter</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_create_counter_variable</span>(<span class="pl-c1">0</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC367" class="react-file-line html-div" data-testid="code-cell" data-line-number="367" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_predict_counter</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_create_counter_variable</span>(<span class="pl-c1">0</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC368" class="react-file-line html-div" data-testid="code-cell" data-line-number="368" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC369" class="react-file-line html-div" data-testid="code-cell" data-line-number="369" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__setattr__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">name</span>, <span class="pl-s1">value</span>):</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC370" class="react-file-line html-div" data-testid="code-cell" data-line-number="370" style="position:relative"> <span class="pl-k">if</span> <span class="pl-c1">not</span> <span class="pl-en">getattr</span>(<span class="pl-s1">self</span>, <span class="pl-s">"_self_setattr_tracking"</span>, <span class="pl-c1">True</span>):</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC371" class="react-file-line html-div" data-testid="code-cell" data-line-number="371" style="position:relative"> <span class="pl-en">super</span>().<span class="pl-c1">__setattr__</span>(<span class="pl-s1">name</span>, <span class="pl-s1">value</span>)</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC372" class="react-file-line html-div" data-testid="code-cell" data-line-number="372" style="position:relative"> <span class="pl-k">return</span></div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC373" class="react-file-line html-div" data-testid="code-cell" data-line-number="373" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC374" class="react-file-line html-div" data-testid="code-cell" data-line-number="374" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">all</span>(</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC375" class="react-file-line html-div" data-testid="code-cell" data-line-number="375" style="position:relative"> <span class="pl-en">isinstance</span>(<span class="pl-s1">v</span>, (<span class="pl-s1">base_layer</span>.<span class="pl-c1">Layer</span>, <span class="pl-s1">tf</span>.<span class="pl-c1">Variable</span>))</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC376" class="react-file-line html-div" data-testid="code-cell" data-line-number="376" style="position:relative"> <span class="pl-c1">or</span> <span class="pl-s1">base_layer_utils</span>.<span class="pl-c1">has_weights</span>(<span class="pl-s1">v</span>)</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC377" class="react-file-line html-div" data-testid="code-cell" data-line-number="377" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">v</span> <span class="pl-c1">in</span> <span class="pl-s1">tf</span>.<span class="pl-c1">nest</span>.<span class="pl-c1">flatten</span>(<span class="pl-s1">value</span>)</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC378" class="react-file-line html-div" data-testid="code-cell" data-line-number="378" style="position:relative"> ):</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC379" class="react-file-line html-div" data-testid="code-cell" data-line-number="379" style="position:relative"> <span class="pl-k">try</span>:</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC380" class="react-file-line html-div" data-testid="code-cell" data-line-number="380" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_base_model_initialized</span></div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC381" class="react-file-line html-div" data-testid="code-cell" data-line-number="381" style="position:relative"> <span class="pl-k">except</span> <span class="pl-v">AttributeError</span>:</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC382" class="react-file-line html-div" data-testid="code-cell" data-line-number="382" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">RuntimeError</span>(</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC383" class="react-file-line html-div" data-testid="code-cell" data-line-number="383" style="position:relative"> <span class="pl-s">"It looks like you are subclassing `Model` and you "</span></div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC384" class="react-file-line html-div" data-testid="code-cell" data-line-number="384" style="position:relative"> <span class="pl-s">"forgot to call `super().__init__()`."</span></div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC385" class="react-file-line html-div" data-testid="code-cell" data-line-number="385" style="position:relative"> <span class="pl-s">" Always start with this line."</span></div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC386" class="react-file-line html-div" data-testid="code-cell" data-line-number="386" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-368 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC387" class="react-file-line html-div" data-testid="code-cell" data-line-number="387" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC388" class="react-file-line html-div" data-testid="code-cell" data-line-number="388" style="position:relative"> <span class="pl-en">super</span>().<span class="pl-c1">__setattr__</span>(<span class="pl-s1">name</span>, <span class="pl-s1">value</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC389" class="react-file-line html-div" data-testid="code-cell" data-line-number="389" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC390" class="react-file-line html-div" data-testid="code-cell" data-line-number="390" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__reduce__</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC391" class="react-file-line html-div" data-testid="code-cell" data-line-number="391" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">built</span>:</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC392" class="react-file-line html-div" data-testid="code-cell" data-line-number="392" style="position:relative"> <span class="pl-k">return</span> (</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC393" class="react-file-line html-div" data-testid="code-cell" data-line-number="393" style="position:relative"> <span class="pl-s1">pickle_utils</span>.<span class="pl-c1">deserialize_model_from_bytecode</span>,</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC394" class="react-file-line html-div" data-testid="code-cell" data-line-number="394" style="position:relative"> (<span class="pl-s1">pickle_utils</span>.<span class="pl-c1">serialize_model_as_bytecode</span>(<span class="pl-s1">self</span>),),</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC395" class="react-file-line html-div" data-testid="code-cell" data-line-number="395" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC396" class="react-file-line html-div" data-testid="code-cell" data-line-number="396" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC397" class="react-file-line html-div" data-testid="code-cell" data-line-number="397" style="position:relative"> <span class="pl-c"># SavedModel (and hence serialize_model_as_bytecode) only support</span></div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC398" class="react-file-line html-div" data-testid="code-cell" data-line-number="398" style="position:relative"> <span class="pl-c"># built models, but if the model is not built,</span></div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC399" class="react-file-line html-div" data-testid="code-cell" data-line-number="399" style="position:relative"> <span class="pl-c"># it may be possible to serialize as a plain Python object,</span></div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC400" class="react-file-line html-div" data-testid="code-cell" data-line-number="400" style="position:relative"> <span class="pl-c"># as long as the constituent parts (layers, optimizers, losses,</span></div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC401" class="react-file-line html-div" data-testid="code-cell" data-line-number="401" style="position:relative"> <span class="pl-c"># etc.) can be serialized as plain Python objects. Thus we call up</span></div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC402" class="react-file-line html-div" data-testid="code-cell" data-line-number="402" style="position:relative"> <span class="pl-c"># the superclass hierarchy to get an implementation of __reduce__</span></div></div></div><div class="child-of-line-70 child-of-line-389 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC403" class="react-file-line html-div" data-testid="code-cell" data-line-number="403" style="position:relative"> <span class="pl-c"># that can pickle this Model as a plain Python object.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC404" class="react-file-line html-div" data-testid="code-cell" data-line-number="404" style="position:relative"> <span class="pl-k">return</span> <span class="pl-en">super</span>().<span class="pl-c1">__reduce__</span>()</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC405" class="react-file-line html-div" data-testid="code-cell" data-line-number="405" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC406" class="react-file-line html-div" data-testid="code-cell" data-line-number="406" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__deepcopy__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">memo</span>):</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC407" class="react-file-line html-div" data-testid="code-cell" data-line-number="407" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">built</span>:</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC408" class="react-file-line html-div" data-testid="code-cell" data-line-number="408" style="position:relative"> <span class="pl-s1">new</span> <span class="pl-c1">=</span> <span class="pl-s1">pickle_utils</span>.<span class="pl-c1">deserialize_model_from_bytecode</span>(</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC409" class="react-file-line html-div" data-testid="code-cell" data-line-number="409" style="position:relative"> <span class="pl-s1">pickle_utils</span>.<span class="pl-c1">serialize_model_as_bytecode</span>(<span class="pl-s1">self</span>)</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC410" class="react-file-line html-div" data-testid="code-cell" data-line-number="410" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC411" class="react-file-line html-div" data-testid="code-cell" data-line-number="411" style="position:relative"> <span class="pl-s1">memo</span>[<span class="pl-en">id</span>(<span class="pl-s1">self</span>)] <span class="pl-c1">=</span> <span class="pl-s1">new</span></div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC412" class="react-file-line html-div" data-testid="code-cell" data-line-number="412" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC413" class="react-file-line html-div" data-testid="code-cell" data-line-number="413" style="position:relative"> <span class="pl-c"># See comment in __reduce__ for explanation</span></div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC414" class="react-file-line html-div" data-testid="code-cell" data-line-number="414" style="position:relative"> <span class="pl-s1">deserializer</span>, <span class="pl-s1">serialized</span>, <span class="pl-c1">*</span><span class="pl-s1">rest</span> <span class="pl-c1">=</span> <span class="pl-en">super</span>().<span class="pl-c1">__reduce__</span>()</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC415" class="react-file-line html-div" data-testid="code-cell" data-line-number="415" style="position:relative"> <span class="pl-s1">new</span> <span class="pl-c1">=</span> <span class="pl-en">deserializer</span>(<span class="pl-c1">*</span><span class="pl-s1">serialized</span>)</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC416" class="react-file-line html-div" data-testid="code-cell" data-line-number="416" style="position:relative"> <span class="pl-s1">memo</span>[<span class="pl-en">id</span>(<span class="pl-s1">self</span>)] <span class="pl-c1">=</span> <span class="pl-s1">new</span></div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC417" class="react-file-line html-div" data-testid="code-cell" data-line-number="417" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">rest</span>:</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC418" class="react-file-line html-div" data-testid="code-cell" data-line-number="418" style="position:relative"> <span class="pl-s1">state</span> <span class="pl-c1">=</span> <span class="pl-s1">copy</span>.<span class="pl-c1">deepcopy</span>(<span class="pl-s1">rest</span>[<span class="pl-c1">0</span>], <span class="pl-s1">memo</span><span class="pl-c1">=</span><span class="pl-s1">memo</span>)</div></div></div><div class="child-of-line-70 child-of-line-405 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC419" class="react-file-line html-div" data-testid="code-cell" data-line-number="419" style="position:relative"> <span class="pl-s1">new</span>.<span class="pl-c1">__setstate__</span>(<span class="pl-s1">state</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC420" class="react-file-line html-div" data-testid="code-cell" data-line-number="420" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">new</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC421" class="react-file-line html-div" data-testid="code-cell" data-line-number="421" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC422" class="react-file-line html-div" data-testid="code-cell" data-line-number="422" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__copy__</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC423" class="react-file-line html-div" data-testid="code-cell" data-line-number="423" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">self</span>.<span class="pl-c1">__deepcopy__</span>({})</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC424" class="react-file-line html-div" data-testid="code-cell" data-line-number="424" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC425" class="react-file-line html-div" data-testid="code-cell" data-line-number="425" style="position:relative"> <span class="pl-en">@<span class="pl-s1">generic_utils</span>.<span class="pl-c1">default</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC426" class="react-file-line html-div" data-testid="code-cell" data-line-number="426" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">build</span>(<span class="pl-s1">self</span>, <span class="pl-s1">input_shape</span>):</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC427" class="react-file-line html-div" data-testid="code-cell" data-line-number="427" style="position:relative"> <span class="pl-s">"""Builds the model based on input shapes received.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC428" class="react-file-line html-div" data-testid="code-cell" data-line-number="428" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC429" class="react-file-line html-div" data-testid="code-cell" data-line-number="429" style="position:relative"><span class="pl-s"> This is to be used for subclassed models, which do not know at</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC430" class="react-file-line html-div" data-testid="code-cell" data-line-number="430" style="position:relative"><span class="pl-s"> instantiation time what their inputs look like.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC431" class="react-file-line html-div" data-testid="code-cell" data-line-number="431" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC432" class="react-file-line html-div" data-testid="code-cell" data-line-number="432" style="position:relative"><span class="pl-s"> This method only exists for users who want to call `model.build()` in a</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC433" class="react-file-line html-div" data-testid="code-cell" data-line-number="433" style="position:relative"><span class="pl-s"> standalone way (as a substitute for calling the model on real data to</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC434" class="react-file-line html-div" data-testid="code-cell" data-line-number="434" style="position:relative"><span class="pl-s"> build it). It will never be called by the framework (and thus it will</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC435" class="react-file-line html-div" data-testid="code-cell" data-line-number="435" style="position:relative"><span class="pl-s"> never throw unexpected errors in an unrelated workflow).</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC436" class="react-file-line html-div" data-testid="code-cell" data-line-number="436" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC437" class="react-file-line html-div" data-testid="code-cell" data-line-number="437" style="position:relative"><span class="pl-s"> Args:</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC438" class="react-file-line html-div" data-testid="code-cell" data-line-number="438" style="position:relative"><span class="pl-s"> input_shape: Single tuple, `TensorShape` instance, or list/dict of</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC439" class="react-file-line html-div" data-testid="code-cell" data-line-number="439" style="position:relative"><span class="pl-s"> shapes, where shapes are tuples, integers, or `TensorShape`</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC440" class="react-file-line html-div" data-testid="code-cell" data-line-number="440" style="position:relative"><span class="pl-s"> instances.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC441" class="react-file-line html-div" data-testid="code-cell" data-line-number="441" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC442" class="react-file-line html-div" data-testid="code-cell" data-line-number="442" style="position:relative"><span class="pl-s"> Raises:</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC443" class="react-file-line html-div" data-testid="code-cell" data-line-number="443" style="position:relative"><span class="pl-s"> ValueError:</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC444" class="react-file-line html-div" data-testid="code-cell" data-line-number="444" style="position:relative"><span class="pl-s"> 1. In case of invalid user-provided data (not of type tuple,</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC445" class="react-file-line html-div" data-testid="code-cell" data-line-number="445" style="position:relative"><span class="pl-s"> list, `TensorShape`, or dict).</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC446" class="react-file-line html-div" data-testid="code-cell" data-line-number="446" style="position:relative"><span class="pl-s"> 2. If the model requires call arguments that are agnostic</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC447" class="react-file-line html-div" data-testid="code-cell" data-line-number="447" style="position:relative"><span class="pl-s"> to the input shapes (positional or keyword arg in call</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC448" class="react-file-line html-div" data-testid="code-cell" data-line-number="448" style="position:relative"><span class="pl-s"> signature).</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC449" class="react-file-line html-div" data-testid="code-cell" data-line-number="449" style="position:relative"><span class="pl-s"> 3. If not all layers were properly built.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC450" class="react-file-line html-div" data-testid="code-cell" data-line-number="450" style="position:relative"><span class="pl-s"> 4. If float type inputs are not supported within the layers.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC451" class="react-file-line html-div" data-testid="code-cell" data-line-number="451" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC452" class="react-file-line html-div" data-testid="code-cell" data-line-number="452" style="position:relative"><span class="pl-s"> In each of these cases, the user should build their model by calling</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC453" class="react-file-line html-div" data-testid="code-cell" data-line-number="453" style="position:relative"><span class="pl-s"> it on real tensor data.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC454" class="react-file-line html-div" data-testid="code-cell" data-line-number="454" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC455" class="react-file-line html-div" data-testid="code-cell" data-line-number="455" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_is_graph_network</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC456" class="react-file-line html-div" data-testid="code-cell" data-line-number="456" style="position:relative"> <span class="pl-en">super</span>().<span class="pl-c1">build</span>(<span class="pl-s1">input_shape</span>)</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC457" class="react-file-line html-div" data-testid="code-cell" data-line-number="457" style="position:relative"> <span class="pl-k">return</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC458" class="react-file-line html-div" data-testid="code-cell" data-line-number="458" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC459" class="react-file-line html-div" data-testid="code-cell" data-line-number="459" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">input_shape</span> <span class="pl-c1">is</span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC460" class="react-file-line html-div" data-testid="code-cell" data-line-number="460" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC461" class="react-file-line html-div" data-testid="code-cell" data-line-number="461" style="position:relative"> <span class="pl-s">"Input shape must be defined when calling `build()` on "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC462" class="react-file-line html-div" data-testid="code-cell" data-line-number="462" style="position:relative"> <span class="pl-s">"a `Model` subclass."</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC463" class="react-file-line html-div" data-testid="code-cell" data-line-number="463" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC464" class="react-file-line html-div" data-testid="code-cell" data-line-number="464" style="position:relative"> <span class="pl-s1">valid_types</span> <span class="pl-c1">=</span> (<span class="pl-s1">tuple</span>, <span class="pl-s1">list</span>, <span class="pl-s1">tf</span>.<span class="pl-c1">TensorShape</span>, <span class="pl-s1">dict</span>)</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC465" class="react-file-line html-div" data-testid="code-cell" data-line-number="465" style="position:relative"> <span class="pl-k">if</span> <span class="pl-c1">not</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">input_shape</span>, <span class="pl-s1">valid_types</span>):</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC466" class="react-file-line html-div" data-testid="code-cell" data-line-number="466" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC467" class="react-file-line html-div" data-testid="code-cell" data-line-number="467" style="position:relative"> <span class="pl-s">"Specified input shape is not one of the valid types. "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC468" class="react-file-line html-div" data-testid="code-cell" data-line-number="468" style="position:relative"> <span class="pl-s">"Please specify a batch input shape of type tuple or "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC469" class="react-file-line html-div" data-testid="code-cell" data-line-number="469" style="position:relative"> <span class="pl-s">"list of input shapes. User provided "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC470" class="react-file-line html-div" data-testid="code-cell" data-line-number="470" style="position:relative"> <span class="pl-s">"input type: {}."</span>.<span class="pl-c1">format</span>(<span class="pl-en">type</span>(<span class="pl-s1">input_shape</span>))</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC471" class="react-file-line html-div" data-testid="code-cell" data-line-number="471" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC472" class="react-file-line html-div" data-testid="code-cell" data-line-number="472" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC473" class="react-file-line html-div" data-testid="code-cell" data-line-number="473" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">input_shape</span> <span class="pl-c1">and</span> <span class="pl-c1">not</span> <span class="pl-s1">self</span>.<span class="pl-c1">inputs</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC474" class="react-file-line html-div" data-testid="code-cell" data-line-number="474" style="position:relative"> <span class="pl-c"># We create placeholders for the `None`s in the shape and build the</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC475" class="react-file-line html-div" data-testid="code-cell" data-line-number="475" style="position:relative"> <span class="pl-c"># model in a Graph. Since tf.Variable is compatible with both eager</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC476" class="react-file-line html-div" data-testid="code-cell" data-line-number="476" style="position:relative"> <span class="pl-c"># execution and graph building, the variables created after building</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC477" class="react-file-line html-div" data-testid="code-cell" data-line-number="477" style="position:relative"> <span class="pl-c"># the model in a Graph are still valid when executing eagerly.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC478" class="react-file-line html-div" data-testid="code-cell" data-line-number="478" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">tf</span>.<span class="pl-c1">executing_eagerly</span>():</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC479" class="react-file-line html-div" data-testid="code-cell" data-line-number="479" style="position:relative"> <span class="pl-s1">graph</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">FuncGraph</span>(<span class="pl-s">"build_graph"</span>)</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC480" class="react-file-line html-div" data-testid="code-cell" data-line-number="480" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC481" class="react-file-line html-div" data-testid="code-cell" data-line-number="481" style="position:relative"> <span class="pl-s1">graph</span> <span class="pl-c1">=</span> <span class="pl-s1">backend</span>.<span class="pl-c1">get_graph</span>()</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC482" class="react-file-line html-div" data-testid="code-cell" data-line-number="482" style="position:relative"> <span class="pl-k">with</span> <span class="pl-s1">graph</span>.<span class="pl-c1">as_default</span>():</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC483" class="react-file-line html-div" data-testid="code-cell" data-line-number="483" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">input_shape</span>, <span class="pl-s1">list</span>) <span class="pl-c1">and</span> <span class="pl-en">all</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC484" class="react-file-line html-div" data-testid="code-cell" data-line-number="484" style="position:relative"> <span class="pl-s1">d</span> <span class="pl-c1">is</span> <span class="pl-c1">None</span> <span class="pl-c1">or</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">d</span>, <span class="pl-s1">int</span>) <span class="pl-k">for</span> <span class="pl-s1">d</span> <span class="pl-c1">in</span> <span class="pl-s1">input_shape</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC485" class="react-file-line html-div" data-testid="code-cell" data-line-number="485" style="position:relative"> ):</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC486" class="react-file-line html-div" data-testid="code-cell" data-line-number="486" style="position:relative"> <span class="pl-s1">input_shape</span> <span class="pl-c1">=</span> <span class="pl-en">tuple</span>(<span class="pl-s1">input_shape</span>)</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC487" class="react-file-line html-div" data-testid="code-cell" data-line-number="487" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">input_shape</span>, <span class="pl-s1">list</span>):</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC488" class="react-file-line html-div" data-testid="code-cell" data-line-number="488" style="position:relative"> <span class="pl-s1">x</span> <span class="pl-c1">=</span> [</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC489" class="react-file-line html-div" data-testid="code-cell" data-line-number="489" style="position:relative"> <span class="pl-s1">base_layer_utils</span>.<span class="pl-c1">generate_placeholders_from_shape</span>(<span class="pl-s1">shape</span>)</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC490" class="react-file-line html-div" data-testid="code-cell" data-line-number="490" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">shape</span> <span class="pl-c1">in</span> <span class="pl-s1">input_shape</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC491" class="react-file-line html-div" data-testid="code-cell" data-line-number="491" style="position:relative"> ]</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC492" class="react-file-line html-div" data-testid="code-cell" data-line-number="492" style="position:relative"> <span class="pl-k">elif</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">input_shape</span>, <span class="pl-s1">dict</span>):</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC493" class="react-file-line html-div" data-testid="code-cell" data-line-number="493" style="position:relative"> <span class="pl-s1">x</span> <span class="pl-c1">=</span> {</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC494" class="react-file-line html-div" data-testid="code-cell" data-line-number="494" style="position:relative"> <span class="pl-s1">k</span>: <span class="pl-s1">base_layer_utils</span>.<span class="pl-c1">generate_placeholders_from_shape</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC495" class="react-file-line html-div" data-testid="code-cell" data-line-number="495" style="position:relative"> <span class="pl-s1">shape</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC496" class="react-file-line html-div" data-testid="code-cell" data-line-number="496" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC497" class="react-file-line html-div" data-testid="code-cell" data-line-number="497" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">k</span>, <span class="pl-s1">shape</span> <span class="pl-c1">in</span> <span class="pl-s1">input_shape</span>.<span class="pl-c1">items</span>()</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC498" class="react-file-line html-div" data-testid="code-cell" data-line-number="498" style="position:relative"> }</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC499" class="react-file-line html-div" data-testid="code-cell" data-line-number="499" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC500" class="react-file-line html-div" data-testid="code-cell" data-line-number="500" style="position:relative"> <span class="pl-s1">x</span> <span class="pl-c1">=</span> <span class="pl-s1">base_layer_utils</span>.<span class="pl-c1">generate_placeholders_from_shape</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC501" class="react-file-line html-div" data-testid="code-cell" data-line-number="501" style="position:relative"> <span class="pl-s1">input_shape</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC502" class="react-file-line html-div" data-testid="code-cell" data-line-number="502" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC503" class="react-file-line html-div" data-testid="code-cell" data-line-number="503" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC504" class="react-file-line html-div" data-testid="code-cell" data-line-number="504" style="position:relative"> <span class="pl-s1">kwargs</span> <span class="pl-c1">=</span> {}</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC505" class="react-file-line html-div" data-testid="code-cell" data-line-number="505" style="position:relative"> <span class="pl-s1">call_signature</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_call_spec</span>.<span class="pl-c1">full_argspec</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC506" class="react-file-line html-div" data-testid="code-cell" data-line-number="506" style="position:relative"> <span class="pl-s1">call_args</span> <span class="pl-c1">=</span> <span class="pl-s1">call_signature</span>.<span class="pl-c1">args</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC507" class="react-file-line html-div" data-testid="code-cell" data-line-number="507" style="position:relative"> <span class="pl-c"># Exclude `self`, `inputs`, and any argument with a default</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC508" class="react-file-line html-div" data-testid="code-cell" data-line-number="508" style="position:relative"> <span class="pl-c"># value.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC509" class="react-file-line html-div" data-testid="code-cell" data-line-number="509" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">len</span>(<span class="pl-s1">call_args</span>) <span class="pl-c1">></span> <span class="pl-c1">2</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC510" class="react-file-line html-div" data-testid="code-cell" data-line-number="510" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">call_signature</span>.<span class="pl-c1">defaults</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC511" class="react-file-line html-div" data-testid="code-cell" data-line-number="511" style="position:relative"> <span class="pl-s1">call_args</span> <span class="pl-c1">=</span> <span class="pl-s1">call_args</span>[<span class="pl-c1">2</span> : <span class="pl-c1">-</span><span class="pl-en">len</span>(<span class="pl-s1">call_signature</span>.<span class="pl-c1">defaults</span>)]</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC512" class="react-file-line html-div" data-testid="code-cell" data-line-number="512" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC513" class="react-file-line html-div" data-testid="code-cell" data-line-number="513" style="position:relative"> <span class="pl-s1">call_args</span> <span class="pl-c1">=</span> <span class="pl-s1">call_args</span>[<span class="pl-c1">2</span>:]</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC514" class="react-file-line html-div" data-testid="code-cell" data-line-number="514" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">arg</span> <span class="pl-c1">in</span> <span class="pl-s1">call_args</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC515" class="react-file-line html-div" data-testid="code-cell" data-line-number="515" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">arg</span> <span class="pl-c1">==</span> <span class="pl-s">"training"</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC516" class="react-file-line html-div" data-testid="code-cell" data-line-number="516" style="position:relative"> <span class="pl-c"># Case where `training` is a positional arg with no</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC517" class="react-file-line html-div" data-testid="code-cell" data-line-number="517" style="position:relative"> <span class="pl-c"># default.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC518" class="react-file-line html-div" data-testid="code-cell" data-line-number="518" style="position:relative"> <span class="pl-s1">kwargs</span>[<span class="pl-s">"training"</span>] <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC519" class="react-file-line html-div" data-testid="code-cell" data-line-number="519" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC520" class="react-file-line html-div" data-testid="code-cell" data-line-number="520" style="position:relative"> <span class="pl-c"># Has invalid call signature with unknown positional</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC521" class="react-file-line html-div" data-testid="code-cell" data-line-number="521" style="position:relative"> <span class="pl-c"># arguments.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC522" class="react-file-line html-div" data-testid="code-cell" data-line-number="522" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC523" class="react-file-line html-div" data-testid="code-cell" data-line-number="523" style="position:relative"> <span class="pl-s">"Currently, you cannot build your model if it "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC524" class="react-file-line html-div" data-testid="code-cell" data-line-number="524" style="position:relative"> <span class="pl-s">"has positional or keyword arguments that are "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC525" class="react-file-line html-div" data-testid="code-cell" data-line-number="525" style="position:relative"> <span class="pl-s">"not inputs to the model, but are required for "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC526" class="react-file-line html-div" data-testid="code-cell" data-line-number="526" style="position:relative"> <span class="pl-s">"its `call()` method. Instead, in order to "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC527" class="react-file-line html-div" data-testid="code-cell" data-line-number="527" style="position:relative"> <span class="pl-s">"instantiate and build your model, `call()` "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC528" class="react-file-line html-div" data-testid="code-cell" data-line-number="528" style="position:relative"> <span class="pl-s">"your model on real tensor data with all "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC529" class="react-file-line html-div" data-testid="code-cell" data-line-number="529" style="position:relative"> <span class="pl-s">"expected call arguments. The argument "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC530" class="react-file-line html-div" data-testid="code-cell" data-line-number="530" style="position:relative"> <span class="pl-s">"for `call()` can be a single list/tuple that "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC531" class="react-file-line html-div" data-testid="code-cell" data-line-number="531" style="position:relative"> <span class="pl-s">"contains multiple inputs."</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC532" class="react-file-line html-div" data-testid="code-cell" data-line-number="532" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC533" class="react-file-line html-div" data-testid="code-cell" data-line-number="533" style="position:relative"> <span class="pl-k">elif</span> <span class="pl-en">len</span>(<span class="pl-s1">call_args</span>) <span class="pl-c1"><</span> <span class="pl-c1">2</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC534" class="react-file-line html-div" data-testid="code-cell" data-line-number="534" style="position:relative"> <span class="pl-c"># Signature without `inputs`.</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC535" class="react-file-line html-div" data-testid="code-cell" data-line-number="535" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC536" class="react-file-line html-div" data-testid="code-cell" data-line-number="536" style="position:relative"> <span class="pl-s">"You can only call `build()` on a model if its "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC537" class="react-file-line html-div" data-testid="code-cell" data-line-number="537" style="position:relative"> <span class="pl-s">"`call()` method accepts an `inputs` argument."</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC538" class="react-file-line html-div" data-testid="code-cell" data-line-number="538" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC539" class="react-file-line html-div" data-testid="code-cell" data-line-number="539" style="position:relative"> <span class="pl-k">try</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC540" class="react-file-line html-div" data-testid="code-cell" data-line-number="540" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">call</span>(<span class="pl-s1">x</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC541" class="react-file-line html-div" data-testid="code-cell" data-line-number="541" style="position:relative"> <span class="pl-k">except</span> (<span class="pl-s1">tf</span>.<span class="pl-c1">errors</span>.<span class="pl-c1">InvalidArgumentError</span>, <span class="pl-v">TypeError</span>) <span class="pl-k">as</span> <span class="pl-s1">e</span>:</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC542" class="react-file-line html-div" data-testid="code-cell" data-line-number="542" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC543" class="react-file-line html-div" data-testid="code-cell" data-line-number="543" style="position:relative"> <span class="pl-s">"You cannot build your model by calling `build` "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC544" class="react-file-line html-div" data-testid="code-cell" data-line-number="544" style="position:relative"> <span class="pl-s">"if your layers do not support float type inputs. "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC545" class="react-file-line html-div" data-testid="code-cell" data-line-number="545" style="position:relative"> <span class="pl-s">"Instead, in order to instantiate and build your "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC546" class="react-file-line html-div" data-testid="code-cell" data-line-number="546" style="position:relative"> <span class="pl-s">"model, call your model on real tensor data (of "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC547" class="react-file-line html-div" data-testid="code-cell" data-line-number="547" style="position:relative"> <span class="pl-s">"the correct dtype).<span class="pl-cce">\n</span><span class="pl-cce">\n</span>The actual error from "</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC548" class="react-file-line html-div" data-testid="code-cell" data-line-number="548" style="position:relative"> <span class="pl-s">f"`call` is: <span class="pl-s1"><span class="pl-kos">{</span><span class="pl-s1">e</span><span class="pl-kos">}</span></span>."</span></div></div></div><div class="child-of-line-70 child-of-line-425 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC549" class="react-file-line html-div" data-testid="code-cell" data-line-number="549" style="position:relative"> )</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC550" class="react-file-line html-div" data-testid="code-cell" data-line-number="550" style="position:relative"> <span class="pl-en">super</span>().<span class="pl-c1">build</span>(<span class="pl-s1">input_shape</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC551" class="react-file-line html-div" data-testid="code-cell" data-line-number="551" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC552" class="react-file-line html-div" data-testid="code-cell" data-line-number="552" style="position:relative"> <span class="pl-en">@<span class="pl-s1">traceback_utils</span>.<span class="pl-c1">filter_traceback</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC553" class="react-file-line html-div" data-testid="code-cell" data-line-number="553" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">__call__</span>(<span class="pl-s1">self</span>, <span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>):</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC554" class="react-file-line html-div" data-testid="code-cell" data-line-number="554" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span> <span class="pl-c1"><span class="pl-c1">is</span> <span class="pl-c1">not</span></span> <span class="pl-c1">None</span> <span class="pl-c1">and</span> <span class="pl-c1">not</span> <span class="pl-s1">self</span>.<span class="pl-c1">built</span>:</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC555" class="react-file-line html-div" data-testid="code-cell" data-line-number="555" style="position:relative"> <span class="pl-c"># Note that this method is only overridden for DTensor and layout</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC556" class="react-file-line html-div" data-testid="code-cell" data-line-number="556" style="position:relative"> <span class="pl-c"># injection purpose.</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC557" class="react-file-line html-div" data-testid="code-cell" data-line-number="557" style="position:relative"> <span class="pl-c"># Capture the inputs and create graph input as replacement for model</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC558" class="react-file-line html-div" data-testid="code-cell" data-line-number="558" style="position:relative"> <span class="pl-c"># to initialize its weights first.</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC559" class="react-file-line html-div" data-testid="code-cell" data-line-number="559" style="position:relative"> <span class="pl-s1">copied_args</span> <span class="pl-c1">=</span> <span class="pl-s1">copy</span>.<span class="pl-c1">copy</span>(<span class="pl-s1">args</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC560" class="react-file-line html-div" data-testid="code-cell" data-line-number="560" style="position:relative"> <span class="pl-s1">copied_kwargs</span> <span class="pl-c1">=</span> <span class="pl-s1">copy</span>.<span class="pl-c1">copy</span>(<span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC561" class="react-file-line html-div" data-testid="code-cell" data-line-number="561" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC562" class="react-file-line html-div" data-testid="code-cell" data-line-number="562" style="position:relative"> (</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC563" class="react-file-line html-div" data-testid="code-cell" data-line-number="563" style="position:relative"> <span class="pl-s1">inputs</span>,</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC564" class="react-file-line html-div" data-testid="code-cell" data-line-number="564" style="position:relative"> <span class="pl-s1">copied_args</span>,</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC565" class="react-file-line html-div" data-testid="code-cell" data-line-number="565" style="position:relative"> <span class="pl-s1">copied_kwargs</span>,</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC566" class="react-file-line html-div" data-testid="code-cell" data-line-number="566" style="position:relative"> ) <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_call_spec</span>.<span class="pl-c1">split_out_first_arg</span>(<span class="pl-s1">copied_args</span>, <span class="pl-s1">copied_kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC567" class="react-file-line html-div" data-testid="code-cell" data-line-number="567" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC568" class="react-file-line html-div" data-testid="code-cell" data-line-number="568" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_convert_to_graph_inputs</span>(<span class="pl-s1">x</span>):</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC569" class="react-file-line html-div" data-testid="code-cell" data-line-number="569" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">x</span>, (<span class="pl-s1">tf</span>.<span class="pl-c1">Tensor</span>, <span class="pl-s1">np</span>.<span class="pl-c1">ndarray</span>, <span class="pl-s1">float</span>, <span class="pl-s1">int</span>)):</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC570" class="react-file-line html-div" data-testid="code-cell" data-line-number="570" style="position:relative"> <span class="pl-s1">x</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">convert_to_tensor</span>(<span class="pl-s1">x</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC571" class="react-file-line html-div" data-testid="code-cell" data-line-number="571" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">input_layer_module</span>.<span class="pl-c1">Input</span>(<span class="pl-s1">x</span>.<span class="pl-c1">shape</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC572" class="react-file-line html-div" data-testid="code-cell" data-line-number="572" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC573" class="react-file-line html-div" data-testid="code-cell" data-line-number="573" style="position:relative"> <span class="pl-c"># TODO(scottzhu): maybe better handle mask and training flag.</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC574" class="react-file-line html-div" data-testid="code-cell" data-line-number="574" style="position:relative"> <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">nest</span>.<span class="pl-c1">map_structure</span>(<span class="pl-s1">_convert_to_graph_inputs</span>, <span class="pl-s1">inputs</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC575" class="react-file-line html-div" data-testid="code-cell" data-line-number="575" style="position:relative"> <span class="pl-s1">copied_args</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">nest</span>.<span class="pl-c1">map_structure</span>(</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC576" class="react-file-line html-div" data-testid="code-cell" data-line-number="576" style="position:relative"> <span class="pl-s1">_convert_to_graph_inputs</span>, <span class="pl-s1">copied_args</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC577" class="react-file-line html-div" data-testid="code-cell" data-line-number="577" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC578" class="react-file-line html-div" data-testid="code-cell" data-line-number="578" style="position:relative"> <span class="pl-s1">copied_kwargs</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">nest</span>.<span class="pl-c1">map_structure</span>(</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC579" class="react-file-line html-div" data-testid="code-cell" data-line-number="579" style="position:relative"> <span class="pl-s1">_convert_to_graph_inputs</span>, <span class="pl-s1">copied_kwargs</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC580" class="react-file-line html-div" data-testid="code-cell" data-line-number="580" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC581" class="react-file-line html-div" data-testid="code-cell" data-line-number="581" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC582" class="react-file-line html-div" data-testid="code-cell" data-line-number="582" style="position:relative"> <span class="pl-k">with</span> <span class="pl-s1">layout_map_lib</span>.<span class="pl-c1">layout_map_scope</span>(<span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span>):</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC583" class="react-file-line html-div" data-testid="code-cell" data-line-number="583" style="position:relative"> <span class="pl-c"># We ignore the result here.</span></div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC584" class="react-file-line html-div" data-testid="code-cell" data-line-number="584" style="position:relative"> <span class="pl-en">super</span>().<span class="pl-c1">__call__</span>(<span class="pl-s1">inputs</span>, <span class="pl-c1">*</span><span class="pl-s1">copied_args</span>, <span class="pl-c1">**</span><span class="pl-s1">copied_kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC585" class="react-file-line html-div" data-testid="code-cell" data-line-number="585" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC586" class="react-file-line html-div" data-testid="code-cell" data-line-number="586" style="position:relative"> <span class="pl-s1">layout_map_lib</span>.<span class="pl-c1">_map_subclass_model_variable</span>(<span class="pl-s1">self</span>, <span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span>)</div></div></div><div class="child-of-line-70 child-of-line-552 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC587" class="react-file-line html-div" data-testid="code-cell" data-line-number="587" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC588" class="react-file-line html-div" data-testid="code-cell" data-line-number="588" style="position:relative"> <span class="pl-k">return</span> <span class="pl-en">super</span>().<span class="pl-c1">__call__</span>(<span class="pl-c1">*</span><span class="pl-s1">args</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC589" class="react-file-line html-div" data-testid="code-cell" data-line-number="589" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC590" class="react-file-line html-div" data-testid="code-cell" data-line-number="590" style="position:relative"> <span class="pl-en">@<span class="pl-s1">doc_controls</span>.<span class="pl-c1">doc_in_current_and_subclasses</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC591" class="react-file-line html-div" data-testid="code-cell" data-line-number="591" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">call</span>(<span class="pl-s1">self</span>, <span class="pl-s1">inputs</span>, <span class="pl-s1">training</span><span class="pl-c1">=</span><span class="pl-c1">None</span>, <span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-c1">None</span>):</div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC592" class="react-file-line html-div" data-testid="code-cell" data-line-number="592" style="position:relative"> <span class="pl-s">"""Calls the model on new inputs and returns the outputs as tensors.</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC593" class="react-file-line html-div" data-testid="code-cell" data-line-number="593" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC594" class="react-file-line html-div" data-testid="code-cell" data-line-number="594" style="position:relative"><span class="pl-s"> In this case `call()` just reapplies</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC595" class="react-file-line html-div" data-testid="code-cell" data-line-number="595" style="position:relative"><span class="pl-s"> all ops in the graph to the new inputs</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC596" class="react-file-line html-div" data-testid="code-cell" data-line-number="596" style="position:relative"><span class="pl-s"> (e.g. build a new computational graph from the provided inputs).</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC597" class="react-file-line html-div" data-testid="code-cell" data-line-number="597" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC598" class="react-file-line html-div" data-testid="code-cell" data-line-number="598" style="position:relative"><span class="pl-s"> Note: This method should not be called directly. It is only meant to be</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC599" class="react-file-line html-div" data-testid="code-cell" data-line-number="599" style="position:relative"><span class="pl-s"> overridden when subclassing `tf.keras.Model`.</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC600" class="react-file-line html-div" data-testid="code-cell" data-line-number="600" style="position:relative"><span class="pl-s"> To call a model on an input, always use the `__call__()` method,</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC601" class="react-file-line html-div" data-testid="code-cell" data-line-number="601" style="position:relative"><span class="pl-s"> i.e. `model(inputs)`, which relies on the underlying `call()` method.</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC602" class="react-file-line html-div" data-testid="code-cell" data-line-number="602" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC603" class="react-file-line html-div" data-testid="code-cell" data-line-number="603" style="position:relative"><span class="pl-s"> Args:</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC604" class="react-file-line html-div" data-testid="code-cell" data-line-number="604" style="position:relative"><span class="pl-s"> inputs: Input tensor, or dict/list/tuple of input tensors.</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC605" class="react-file-line html-div" data-testid="code-cell" data-line-number="605" style="position:relative"><span class="pl-s"> training: Boolean or boolean scalar tensor, indicating whether to</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC606" class="react-file-line html-div" data-testid="code-cell" data-line-number="606" style="position:relative"><span class="pl-s"> run the `Network` in training mode or inference mode.</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC607" class="react-file-line html-div" data-testid="code-cell" data-line-number="607" style="position:relative"><span class="pl-s"> mask: A mask or list of masks. A mask can be either a boolean tensor</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC608" class="react-file-line html-div" data-testid="code-cell" data-line-number="608" style="position:relative"><span class="pl-s"> or None (no mask). For more details, check the guide</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC609" class="react-file-line html-div" data-testid="code-cell" data-line-number="609" style="position:relative"><span class="pl-s"> [here](https://www.tensorflow.org/guide/keras/masking_and_padding).</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC610" class="react-file-line html-div" data-testid="code-cell" data-line-number="610" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC611" class="react-file-line html-div" data-testid="code-cell" data-line-number="611" style="position:relative"><span class="pl-s"> Returns:</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC612" class="react-file-line html-div" data-testid="code-cell" data-line-number="612" style="position:relative"><span class="pl-s"> A tensor if there is a single output, or</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC613" class="react-file-line html-div" data-testid="code-cell" data-line-number="613" style="position:relative"><span class="pl-s"> a list of tensors if there are more than one outputs.</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC614" class="react-file-line html-div" data-testid="code-cell" data-line-number="614" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC615" class="react-file-line html-div" data-testid="code-cell" data-line-number="615" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">NotImplementedError</span>(</div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC616" class="react-file-line html-div" data-testid="code-cell" data-line-number="616" style="position:relative"> <span class="pl-s">"Unimplemented `tf.keras.Model.call()`: if you "</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC617" class="react-file-line html-div" data-testid="code-cell" data-line-number="617" style="position:relative"> <span class="pl-s">"intend to create a `Model` with the Functional "</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC618" class="react-file-line html-div" data-testid="code-cell" data-line-number="618" style="position:relative"> <span class="pl-s">"API, please provide `inputs` and `outputs` "</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC619" class="react-file-line html-div" data-testid="code-cell" data-line-number="619" style="position:relative"> <span class="pl-s">"arguments. Otherwise, subclass `Model` with an "</span></div></div></div><div class="child-of-line-70 child-of-line-590 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC620" class="react-file-line html-div" data-testid="code-cell" data-line-number="620" style="position:relative"> <span class="pl-s">"overridden `call()` method."</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC621" class="react-file-line html-div" data-testid="code-cell" data-line-number="621" style="position:relative"> )</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC622" class="react-file-line html-div" data-testid="code-cell" data-line-number="622" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC623" class="react-file-line html-div" data-testid="code-cell" data-line-number="623" style="position:relative"> <span class="pl-en">@<span class="pl-s1">traceback_utils</span>.<span class="pl-c1">filter_traceback</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC624" class="react-file-line html-div" data-testid="code-cell" data-line-number="624" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">compile</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC625" class="react-file-line html-div" data-testid="code-cell" data-line-number="625" style="position:relative"> <span class="pl-s1">self</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC626" class="react-file-line html-div" data-testid="code-cell" data-line-number="626" style="position:relative"> <span class="pl-s1">optimizer</span><span class="pl-c1">=</span><span class="pl-s">"rmsprop"</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC627" class="react-file-line html-div" data-testid="code-cell" data-line-number="627" style="position:relative"> <span class="pl-s1">loss</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC628" class="react-file-line html-div" data-testid="code-cell" data-line-number="628" style="position:relative"> <span class="pl-s1">metrics</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC629" class="react-file-line html-div" data-testid="code-cell" data-line-number="629" style="position:relative"> <span class="pl-s1">loss_weights</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC630" class="react-file-line html-div" data-testid="code-cell" data-line-number="630" style="position:relative"> <span class="pl-s1">weighted_metrics</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC631" class="react-file-line html-div" data-testid="code-cell" data-line-number="631" style="position:relative"> <span class="pl-s1">run_eagerly</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC632" class="react-file-line html-div" data-testid="code-cell" data-line-number="632" style="position:relative"> <span class="pl-s1">steps_per_execution</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC633" class="react-file-line html-div" data-testid="code-cell" data-line-number="633" style="position:relative"> <span class="pl-s1">jit_compile</span><span class="pl-c1">=</span><span class="pl-c1">None</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC634" class="react-file-line html-div" data-testid="code-cell" data-line-number="634" style="position:relative"> <span class="pl-s1">pss_evaluation_shards</span><span class="pl-c1">=</span><span class="pl-c1">0</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC635" class="react-file-line html-div" data-testid="code-cell" data-line-number="635" style="position:relative"> <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC636" class="react-file-line html-div" data-testid="code-cell" data-line-number="636" style="position:relative"> ):</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC637" class="react-file-line html-div" data-testid="code-cell" data-line-number="637" style="position:relative"> <span class="pl-s">"""Configures the model for training.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC638" class="react-file-line html-div" data-testid="code-cell" data-line-number="638" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC639" class="react-file-line html-div" data-testid="code-cell" data-line-number="639" style="position:relative"><span class="pl-s"> Example:</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC640" class="react-file-line html-div" data-testid="code-cell" data-line-number="640" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC641" class="react-file-line html-div" data-testid="code-cell" data-line-number="641" style="position:relative"><span class="pl-s"> ```python</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC642" class="react-file-line html-div" data-testid="code-cell" data-line-number="642" style="position:relative"><span class="pl-s"> model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC643" class="react-file-line html-div" data-testid="code-cell" data-line-number="643" style="position:relative"><span class="pl-s"> loss=tf.keras.losses.BinaryCrossentropy(),</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC644" class="react-file-line html-div" data-testid="code-cell" data-line-number="644" style="position:relative"><span class="pl-s"> metrics=[tf.keras.metrics.BinaryAccuracy(),</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC645" class="react-file-line html-div" data-testid="code-cell" data-line-number="645" style="position:relative"><span class="pl-s"> tf.keras.metrics.FalseNegatives()])</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC646" class="react-file-line html-div" data-testid="code-cell" data-line-number="646" style="position:relative"><span class="pl-s"> ```</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC647" class="react-file-line html-div" data-testid="code-cell" data-line-number="647" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC648" class="react-file-line html-div" data-testid="code-cell" data-line-number="648" style="position:relative"><span class="pl-s"> Args:</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC649" class="react-file-line html-div" data-testid="code-cell" data-line-number="649" style="position:relative"><span class="pl-s"> optimizer: String (name of optimizer) or optimizer instance. See</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC650" class="react-file-line html-div" data-testid="code-cell" data-line-number="650" style="position:relative"><span class="pl-s"> `tf.keras.optimizers`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC651" class="react-file-line html-div" data-testid="code-cell" data-line-number="651" style="position:relative"><span class="pl-s"> loss: Loss function. May be a string (name of loss function), or</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC652" class="react-file-line html-div" data-testid="code-cell" data-line-number="652" style="position:relative"><span class="pl-s"> a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC653" class="react-file-line html-div" data-testid="code-cell" data-line-number="653" style="position:relative"><span class="pl-s"> function is any callable with the signature `loss = fn(y_true,</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC654" class="react-file-line html-div" data-testid="code-cell" data-line-number="654" style="position:relative"><span class="pl-s"> y_pred)`, where `y_true` are the ground truth values, and</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC655" class="react-file-line html-div" data-testid="code-cell" data-line-number="655" style="position:relative"><span class="pl-s"> `y_pred` are the model's predictions.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC656" class="react-file-line html-div" data-testid="code-cell" data-line-number="656" style="position:relative"><span class="pl-s"> `y_true` should have shape</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC657" class="react-file-line html-div" data-testid="code-cell" data-line-number="657" style="position:relative"><span class="pl-s"> `(batch_size, d0, .. dN)` (except in the case of</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC658" class="react-file-line html-div" data-testid="code-cell" data-line-number="658" style="position:relative"><span class="pl-s"> sparse loss functions such as</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC659" class="react-file-line html-div" data-testid="code-cell" data-line-number="659" style="position:relative"><span class="pl-s"> sparse categorical crossentropy which expects integer arrays of</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC660" class="react-file-line html-div" data-testid="code-cell" data-line-number="660" style="position:relative"><span class="pl-s"> shape `(batch_size, d0, .. dN-1)`).</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC661" class="react-file-line html-div" data-testid="code-cell" data-line-number="661" style="position:relative"><span class="pl-s"> `y_pred` should have shape `(batch_size, d0, .. dN)`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC662" class="react-file-line html-div" data-testid="code-cell" data-line-number="662" style="position:relative"><span class="pl-s"> The loss function should return a float tensor.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC663" class="react-file-line html-div" data-testid="code-cell" data-line-number="663" style="position:relative"><span class="pl-s"> If a custom `Loss` instance is</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC664" class="react-file-line html-div" data-testid="code-cell" data-line-number="664" style="position:relative"><span class="pl-s"> used and reduction is set to `None`, return value has shape</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC665" class="react-file-line html-div" data-testid="code-cell" data-line-number="665" style="position:relative"><span class="pl-s"> `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC666" class="react-file-line html-div" data-testid="code-cell" data-line-number="666" style="position:relative"><span class="pl-s"> values; otherwise, it is a scalar. If the model has multiple</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC667" class="react-file-line html-div" data-testid="code-cell" data-line-number="667" style="position:relative"><span class="pl-s"> outputs, you can use a different loss on each output by passing a</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC668" class="react-file-line html-div" data-testid="code-cell" data-line-number="668" style="position:relative"><span class="pl-s"> dictionary or a list of losses. The loss value that will be</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC669" class="react-file-line html-div" data-testid="code-cell" data-line-number="669" style="position:relative"><span class="pl-s"> minimized by the model will then be the sum of all individual</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC670" class="react-file-line html-div" data-testid="code-cell" data-line-number="670" style="position:relative"><span class="pl-s"> losses, unless `loss_weights` is specified.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC671" class="react-file-line html-div" data-testid="code-cell" data-line-number="671" style="position:relative"><span class="pl-s"> metrics: List of metrics to be evaluated by the model during</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC672" class="react-file-line html-div" data-testid="code-cell" data-line-number="672" style="position:relative"><span class="pl-s"> training and testing. Each of this can be a string (name of a</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC673" class="react-file-line html-div" data-testid="code-cell" data-line-number="673" style="position:relative"><span class="pl-s"> built-in function), function or a `tf.keras.metrics.Metric`</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC674" class="react-file-line html-div" data-testid="code-cell" data-line-number="674" style="position:relative"><span class="pl-s"> instance. See `tf.keras.metrics`. Typically you will use</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC675" class="react-file-line html-div" data-testid="code-cell" data-line-number="675" style="position:relative"><span class="pl-s"> `metrics=['accuracy']`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC676" class="react-file-line html-div" data-testid="code-cell" data-line-number="676" style="position:relative"><span class="pl-s"> A function is any callable with the signature `result = fn(y_true,</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC677" class="react-file-line html-div" data-testid="code-cell" data-line-number="677" style="position:relative"><span class="pl-s"> y_pred)`. To specify different metrics for different outputs of a</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC678" class="react-file-line html-div" data-testid="code-cell" data-line-number="678" style="position:relative"><span class="pl-s"> multi-output model, you could also pass a dictionary, such as</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC679" class="react-file-line html-div" data-testid="code-cell" data-line-number="679" style="position:relative"><span class="pl-s"> `metrics={'output_a':'accuracy', 'output_b':['accuracy', 'mse']}`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC680" class="react-file-line html-div" data-testid="code-cell" data-line-number="680" style="position:relative"><span class="pl-s"> You can also pass a list to specify a metric or a list of metrics</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC681" class="react-file-line html-div" data-testid="code-cell" data-line-number="681" style="position:relative"><span class="pl-s"> for each output, such as</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC682" class="react-file-line html-div" data-testid="code-cell" data-line-number="682" style="position:relative"><span class="pl-s"> `metrics=[['accuracy'], ['accuracy', 'mse']]`</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC683" class="react-file-line html-div" data-testid="code-cell" data-line-number="683" style="position:relative"><span class="pl-s"> or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC684" class="react-file-line html-div" data-testid="code-cell" data-line-number="684" style="position:relative"><span class="pl-s"> strings 'accuracy' or 'acc', we convert this to one of</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC685" class="react-file-line html-div" data-testid="code-cell" data-line-number="685" style="position:relative"><span class="pl-s"> `tf.keras.metrics.BinaryAccuracy`,</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC686" class="react-file-line html-div" data-testid="code-cell" data-line-number="686" style="position:relative"><span class="pl-s"> `tf.keras.metrics.CategoricalAccuracy`,</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC687" class="react-file-line html-div" data-testid="code-cell" data-line-number="687" style="position:relative"><span class="pl-s"> `tf.keras.metrics.SparseCategoricalAccuracy` based on the shapes</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC688" class="react-file-line html-div" data-testid="code-cell" data-line-number="688" style="position:relative"><span class="pl-s"> of the targets and of the model output. We do a similar</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC689" class="react-file-line html-div" data-testid="code-cell" data-line-number="689" style="position:relative"><span class="pl-s"> conversion for the strings 'crossentropy' and 'ce' as well.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC690" class="react-file-line html-div" data-testid="code-cell" data-line-number="690" style="position:relative"><span class="pl-s"> The metrics passed here are evaluated without sample weighting; if</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC691" class="react-file-line html-div" data-testid="code-cell" data-line-number="691" style="position:relative"><span class="pl-s"> you would like sample weighting to apply, you can specify your</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC692" class="react-file-line html-div" data-testid="code-cell" data-line-number="692" style="position:relative"><span class="pl-s"> metrics via the `weighted_metrics` argument instead.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC693" class="react-file-line html-div" data-testid="code-cell" data-line-number="693" style="position:relative"><span class="pl-s"> loss_weights: Optional list or dictionary specifying scalar</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC694" class="react-file-line html-div" data-testid="code-cell" data-line-number="694" style="position:relative"><span class="pl-s"> coefficients (Python floats) to weight the loss contributions of</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC695" class="react-file-line html-div" data-testid="code-cell" data-line-number="695" style="position:relative"><span class="pl-s"> different model outputs. The loss value that will be minimized by</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC696" class="react-file-line html-div" data-testid="code-cell" data-line-number="696" style="position:relative"><span class="pl-s"> the model will then be the *weighted sum* of all individual</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC697" class="react-file-line html-div" data-testid="code-cell" data-line-number="697" style="position:relative"><span class="pl-s"> losses, weighted by the `loss_weights` coefficients. If a list,</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC698" class="react-file-line html-div" data-testid="code-cell" data-line-number="698" style="position:relative"><span class="pl-s"> it is expected to have a 1:1 mapping to the model's outputs. If a</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC699" class="react-file-line html-div" data-testid="code-cell" data-line-number="699" style="position:relative"><span class="pl-s"> dict, it is expected to map output names (strings) to scalar</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC700" class="react-file-line html-div" data-testid="code-cell" data-line-number="700" style="position:relative"><span class="pl-s"> coefficients.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC701" class="react-file-line html-div" data-testid="code-cell" data-line-number="701" style="position:relative"><span class="pl-s"> weighted_metrics: List of metrics to be evaluated and weighted by</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC702" class="react-file-line html-div" data-testid="code-cell" data-line-number="702" style="position:relative"><span class="pl-s"> `sample_weight` or `class_weight` during training and testing.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC703" class="react-file-line html-div" data-testid="code-cell" data-line-number="703" style="position:relative"><span class="pl-s"> run_eagerly: Bool. If `True`, this `Model`'s logic will not be</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC704" class="react-file-line html-div" data-testid="code-cell" data-line-number="704" style="position:relative"><span class="pl-s"> wrapped in a `tf.function`. Recommended to leave this as `None`</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC705" class="react-file-line html-div" data-testid="code-cell" data-line-number="705" style="position:relative"><span class="pl-s"> unless your `Model` cannot be run inside a `tf.function`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC706" class="react-file-line html-div" data-testid="code-cell" data-line-number="706" style="position:relative"><span class="pl-s"> `run_eagerly=True` is not supported when using</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC707" class="react-file-line html-div" data-testid="code-cell" data-line-number="707" style="position:relative"><span class="pl-s"> `tf.distribute.experimental.ParameterServerStrategy`. Defaults to</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC708" class="react-file-line html-div" data-testid="code-cell" data-line-number="708" style="position:relative"><span class="pl-s"> `False`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC709" class="react-file-line html-div" data-testid="code-cell" data-line-number="709" style="position:relative"><span class="pl-s"> steps_per_execution: Int or `'auto'`. The number of batches to</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC710" class="react-file-line html-div" data-testid="code-cell" data-line-number="710" style="position:relative"><span class="pl-s"> run during each `tf.function` call. If set to "auto", keras will</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC711" class="react-file-line html-div" data-testid="code-cell" data-line-number="711" style="position:relative"><span class="pl-s"> automatically tune `steps_per_execution` during runtime. Running</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC712" class="react-file-line html-div" data-testid="code-cell" data-line-number="712" style="position:relative"><span class="pl-s"> multiple batches inside a single `tf.function` call can greatly</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC713" class="react-file-line html-div" data-testid="code-cell" data-line-number="713" style="position:relative"><span class="pl-s"> improve performance on TPUs, when used with distributed strategies</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC714" class="react-file-line html-div" data-testid="code-cell" data-line-number="714" style="position:relative"><span class="pl-s"> such as `ParameterServerStrategy`, or with small models with a</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC715" class="react-file-line html-div" data-testid="code-cell" data-line-number="715" style="position:relative"><span class="pl-s"> large Python overhead. At most, one full epoch will be run each</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC716" class="react-file-line html-div" data-testid="code-cell" data-line-number="716" style="position:relative"><span class="pl-s"> execution. If a number larger than the size of the epoch is</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC717" class="react-file-line html-div" data-testid="code-cell" data-line-number="717" style="position:relative"><span class="pl-s"> passed, the execution will be truncated to the size of the epoch.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC718" class="react-file-line html-div" data-testid="code-cell" data-line-number="718" style="position:relative"><span class="pl-s"> Note that if `steps_per_execution` is set to `N`,</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC719" class="react-file-line html-div" data-testid="code-cell" data-line-number="719" style="position:relative"><span class="pl-s"> `Callback.on_batch_begin` and `Callback.on_batch_end` methods will</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC720" class="react-file-line html-div" data-testid="code-cell" data-line-number="720" style="position:relative"><span class="pl-s"> only be called every `N` batches (i.e. before/after each</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC721" class="react-file-line html-div" data-testid="code-cell" data-line-number="721" style="position:relative"><span class="pl-s"> `tf.function` execution). Defaults to `1`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC722" class="react-file-line html-div" data-testid="code-cell" data-line-number="722" style="position:relative"><span class="pl-s"> jit_compile: If `True`, compile the model training step with XLA.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC723" class="react-file-line html-div" data-testid="code-cell" data-line-number="723" style="position:relative"><span class="pl-s"> [XLA](https://www.tensorflow.org/xla) is an optimizing compiler</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC724" class="react-file-line html-div" data-testid="code-cell" data-line-number="724" style="position:relative"><span class="pl-s"> for machine learning.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC725" class="react-file-line html-div" data-testid="code-cell" data-line-number="725" style="position:relative"><span class="pl-s"> `jit_compile` is not enabled for by default.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC726" class="react-file-line html-div" data-testid="code-cell" data-line-number="726" style="position:relative"><span class="pl-s"> Note that `jit_compile=True`</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC727" class="react-file-line html-div" data-testid="code-cell" data-line-number="727" style="position:relative"><span class="pl-s"> may not necessarily work for all models.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC728" class="react-file-line html-div" data-testid="code-cell" data-line-number="728" style="position:relative"><span class="pl-s"> For more information on supported operations please refer to the</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC729" class="react-file-line html-div" data-testid="code-cell" data-line-number="729" style="position:relative"><span class="pl-s"> [XLA documentation](https://www.tensorflow.org/xla).</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC730" class="react-file-line html-div" data-testid="code-cell" data-line-number="730" style="position:relative"><span class="pl-s"> Also refer to</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC731" class="react-file-line html-div" data-testid="code-cell" data-line-number="731" style="position:relative"><span class="pl-s"> [known XLA issues](https://www.tensorflow.org/xla/known_issues)</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC732" class="react-file-line html-div" data-testid="code-cell" data-line-number="732" style="position:relative"><span class="pl-s"> for more details.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC733" class="react-file-line html-div" data-testid="code-cell" data-line-number="733" style="position:relative"><span class="pl-s"> pss_evaluation_shards: Integer or 'auto'. Used for</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC734" class="react-file-line html-div" data-testid="code-cell" data-line-number="734" style="position:relative"><span class="pl-s"> `tf.distribute.ParameterServerStrategy` training only. This arg</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC735" class="react-file-line html-div" data-testid="code-cell" data-line-number="735" style="position:relative"><span class="pl-s"> sets the number of shards to split the dataset into, to enable an</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC736" class="react-file-line html-div" data-testid="code-cell" data-line-number="736" style="position:relative"><span class="pl-s"> exact visitation guarantee for evaluation, meaning the model will</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC737" class="react-file-line html-div" data-testid="code-cell" data-line-number="737" style="position:relative"><span class="pl-s"> be applied to each dataset element exactly once, even if workers</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC738" class="react-file-line html-div" data-testid="code-cell" data-line-number="738" style="position:relative"><span class="pl-s"> fail. The dataset must be sharded to ensure separate workers do</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC739" class="react-file-line html-div" data-testid="code-cell" data-line-number="739" style="position:relative"><span class="pl-s"> not process the same data. The number of shards should be at least</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC740" class="react-file-line html-div" data-testid="code-cell" data-line-number="740" style="position:relative"><span class="pl-s"> the number of workers for good performance. A value of 'auto'</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC741" class="react-file-line html-div" data-testid="code-cell" data-line-number="741" style="position:relative"><span class="pl-s"> turns on exact evaluation and uses a heuristic for the number of</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC742" class="react-file-line html-div" data-testid="code-cell" data-line-number="742" style="position:relative"><span class="pl-s"> shards based on the number of workers. 0, meaning no</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC743" class="react-file-line html-div" data-testid="code-cell" data-line-number="743" style="position:relative"><span class="pl-s"> visitation guarantee is provided. NOTE: Custom implementations of</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC744" class="react-file-line html-div" data-testid="code-cell" data-line-number="744" style="position:relative"><span class="pl-s"> `Model.test_step` will be ignored when doing exact evaluation.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC745" class="react-file-line html-div" data-testid="code-cell" data-line-number="745" style="position:relative"><span class="pl-s"> Defaults to `0`.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC746" class="react-file-line html-div" data-testid="code-cell" data-line-number="746" style="position:relative"><span class="pl-s"> **kwargs: Arguments supported for backwards compatibility only.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC747" class="react-file-line html-div" data-testid="code-cell" data-line-number="747" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC748" class="react-file-line html-div" data-testid="code-cell" data-line-number="748" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">jit_compile</span> <span class="pl-c1">and</span> <span class="pl-c1">not</span> <span class="pl-s1">tf_utils</span>.<span class="pl-c1">can_jit_compile</span>(<span class="pl-s1">warn</span><span class="pl-c1">=</span><span class="pl-c1">True</span>):</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC749" class="react-file-line html-div" data-testid="code-cell" data-line-number="749" style="position:relative"> <span class="pl-s1">jit_compile</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC750" class="react-file-line html-div" data-testid="code-cell" data-line-number="750" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_compile_config</span> <span class="pl-c1">=</span> <span class="pl-s1">serialization_lib</span>.<span class="pl-c1">Config</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC751" class="react-file-line html-div" data-testid="code-cell" data-line-number="751" style="position:relative"> <span class="pl-s1">optimizer</span><span class="pl-c1">=</span><span class="pl-s1">optimizer</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC752" class="react-file-line html-div" data-testid="code-cell" data-line-number="752" style="position:relative"> <span class="pl-s1">loss</span><span class="pl-c1">=</span><span class="pl-s1">loss</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC753" class="react-file-line html-div" data-testid="code-cell" data-line-number="753" style="position:relative"> <span class="pl-s1">metrics</span><span class="pl-c1">=</span><span class="pl-s1">metrics</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC754" class="react-file-line html-div" data-testid="code-cell" data-line-number="754" style="position:relative"> <span class="pl-s1">loss_weights</span><span class="pl-c1">=</span><span class="pl-s1">loss_weights</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC755" class="react-file-line html-div" data-testid="code-cell" data-line-number="755" style="position:relative"> <span class="pl-s1">weighted_metrics</span><span class="pl-c1">=</span><span class="pl-s1">weighted_metrics</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC756" class="react-file-line html-div" data-testid="code-cell" data-line-number="756" style="position:relative"> <span class="pl-s1">run_eagerly</span><span class="pl-c1">=</span><span class="pl-s1">run_eagerly</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC757" class="react-file-line html-div" data-testid="code-cell" data-line-number="757" style="position:relative"> <span class="pl-s1">steps_per_execution</span><span class="pl-c1">=</span><span class="pl-s1">steps_per_execution</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC758" class="react-file-line html-div" data-testid="code-cell" data-line-number="758" style="position:relative"> <span class="pl-s1">jit_compile</span><span class="pl-c1">=</span><span class="pl-s1">jit_compile</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC759" class="react-file-line html-div" data-testid="code-cell" data-line-number="759" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC760" class="react-file-line html-div" data-testid="code-cell" data-line-number="760" style="position:relative"> <span class="pl-k">with</span> <span class="pl-s1">self</span>.<span class="pl-c1">distribute_strategy</span>.<span class="pl-c1">scope</span>():</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC761" class="react-file-line html-div" data-testid="code-cell" data-line-number="761" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s">"experimental_steps_per_execution"</span> <span class="pl-c1">in</span> <span class="pl-s1">kwargs</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC762" class="react-file-line html-div" data-testid="code-cell" data-line-number="762" style="position:relative"> <span class="pl-s1">logging</span>.<span class="pl-c1">warning</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC763" class="react-file-line html-div" data-testid="code-cell" data-line-number="763" style="position:relative"> <span class="pl-s">"The argument `steps_per_execution` is no longer "</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC764" class="react-file-line html-div" data-testid="code-cell" data-line-number="764" style="position:relative"> <span class="pl-s">"experimental. Pass `steps_per_execution` instead of "</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC765" class="react-file-line html-div" data-testid="code-cell" data-line-number="765" style="position:relative"> <span class="pl-s">"`experimental_steps_per_execution`."</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC766" class="react-file-line html-div" data-testid="code-cell" data-line-number="766" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC767" class="react-file-line html-div" data-testid="code-cell" data-line-number="767" style="position:relative"> <span class="pl-k">if</span> <span class="pl-c1">not</span> <span class="pl-s1">steps_per_execution</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC768" class="react-file-line html-div" data-testid="code-cell" data-line-number="768" style="position:relative"> <span class="pl-s1">steps_per_execution</span> <span class="pl-c1">=</span> <span class="pl-s1">kwargs</span>.<span class="pl-c1">pop</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC769" class="react-file-line html-div" data-testid="code-cell" data-line-number="769" style="position:relative"> <span class="pl-s">"experimental_steps_per_execution"</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC770" class="react-file-line html-div" data-testid="code-cell" data-line-number="770" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC771" class="react-file-line html-div" data-testid="code-cell" data-line-number="771" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC772" class="react-file-line html-div" data-testid="code-cell" data-line-number="772" style="position:relative"> <span class="pl-c"># When compiling from an already-serialized model, we do not want to</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC773" class="react-file-line html-div" data-testid="code-cell" data-line-number="773" style="position:relative"> <span class="pl-c"># reapply some processing steps (e.g. metric renaming for</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC774" class="react-file-line html-div" data-testid="code-cell" data-line-number="774" style="position:relative"> <span class="pl-c"># multi-output models, which have prefixes added for each</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC775" class="react-file-line html-div" data-testid="code-cell" data-line-number="775" style="position:relative"> <span class="pl-c"># corresponding output name).</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC776" class="react-file-line html-div" data-testid="code-cell" data-line-number="776" style="position:relative"> <span class="pl-s1">from_serialized</span> <span class="pl-c1">=</span> <span class="pl-s1">kwargs</span>.<span class="pl-c1">pop</span>(<span class="pl-s">"from_serialized"</span>, <span class="pl-c1">False</span>)</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC777" class="react-file-line html-div" data-testid="code-cell" data-line-number="777" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC778" class="react-file-line html-div" data-testid="code-cell" data-line-number="778" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_validate_compile</span>(<span class="pl-s1">optimizer</span>, <span class="pl-s1">metrics</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span>)</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC779" class="react-file-line html-div" data-testid="code-cell" data-line-number="779" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_run_eagerly</span> <span class="pl-c1">=</span> <span class="pl-s1">run_eagerly</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC780" class="react-file-line html-div" data-testid="code-cell" data-line-number="780" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC781" class="react-file-line html-div" data-testid="code-cell" data-line-number="781" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">optimizer</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_get_optimizer</span>(<span class="pl-s1">optimizer</span>)</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC782" class="react-file-line html-div" data-testid="code-cell" data-line-number="782" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC783" class="react-file-line html-div" data-testid="code-cell" data-line-number="783" style="position:relative"> <span class="pl-s1">mesh</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC784" class="react-file-line html-div" data-testid="code-cell" data-line-number="784" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span> <span class="pl-c1"><span class="pl-c1">is</span> <span class="pl-c1">not</span></span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC785" class="react-file-line html-div" data-testid="code-cell" data-line-number="785" style="position:relative"> <span class="pl-s1">mesh</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_layout_map</span>.<span class="pl-c1">get_default_mesh</span>()</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC786" class="react-file-line html-div" data-testid="code-cell" data-line-number="786" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC787" class="react-file-line html-div" data-testid="code-cell" data-line-number="787" style="position:relative"> <span class="pl-k">if</span> <span class="pl-en">isinstance</span>(<span class="pl-s1">loss</span>, <span class="pl-s1">compile_utils</span>.<span class="pl-c1">LossesContainer</span>):</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC788" class="react-file-line html-div" data-testid="code-cell" data-line-number="788" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_loss</span> <span class="pl-c1">=</span> <span class="pl-s1">loss</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC789" class="react-file-line html-div" data-testid="code-cell" data-line-number="789" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC790" class="react-file-line html-div" data-testid="code-cell" data-line-number="790" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_loss</span> <span class="pl-c1">=</span> <span class="pl-s1">compile_utils</span>.<span class="pl-c1">LossesContainer</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC791" class="react-file-line html-div" data-testid="code-cell" data-line-number="791" style="position:relative"> <span class="pl-s1">loss</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC792" class="react-file-line html-div" data-testid="code-cell" data-line-number="792" style="position:relative"> <span class="pl-s1">loss_weights</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC793" class="react-file-line html-div" data-testid="code-cell" data-line-number="793" style="position:relative"> <span class="pl-s1">output_names</span><span class="pl-c1">=</span><span class="pl-s1">self</span>.<span class="pl-c1">output_names</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC794" class="react-file-line html-div" data-testid="code-cell" data-line-number="794" style="position:relative"> <span class="pl-s1">mesh</span><span class="pl-c1">=</span><span class="pl-s1">mesh</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC795" class="react-file-line html-div" data-testid="code-cell" data-line-number="795" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC796" class="react-file-line html-div" data-testid="code-cell" data-line-number="796" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_metrics</span> <span class="pl-c1">=</span> <span class="pl-s1">compile_utils</span>.<span class="pl-c1">MetricsContainer</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC797" class="react-file-line html-div" data-testid="code-cell" data-line-number="797" style="position:relative"> <span class="pl-s1">metrics</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC798" class="react-file-line html-div" data-testid="code-cell" data-line-number="798" style="position:relative"> <span class="pl-s1">weighted_metrics</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC799" class="react-file-line html-div" data-testid="code-cell" data-line-number="799" style="position:relative"> <span class="pl-s1">output_names</span><span class="pl-c1">=</span><span class="pl-s1">self</span>.<span class="pl-c1">output_names</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC800" class="react-file-line html-div" data-testid="code-cell" data-line-number="800" style="position:relative"> <span class="pl-s1">from_serialized</span><span class="pl-c1">=</span><span class="pl-s1">from_serialized</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC801" class="react-file-line html-div" data-testid="code-cell" data-line-number="801" style="position:relative"> <span class="pl-s1">mesh</span><span class="pl-c1">=</span><span class="pl-s1">mesh</span>,</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC802" class="react-file-line html-div" data-testid="code-cell" data-line-number="802" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC803" class="react-file-line html-div" data-testid="code-cell" data-line-number="803" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC804" class="react-file-line html-div" data-testid="code-cell" data-line-number="804" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">steps_per_execution</span> <span class="pl-c1">==</span> <span class="pl-s">"auto"</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC805" class="react-file-line html-div" data-testid="code-cell" data-line-number="805" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_steps_per_execution</span> <span class="pl-c1">is</span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC806" class="react-file-line html-div" data-testid="code-cell" data-line-number="806" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_configure_steps_per_execution</span>(<span class="pl-c1">1</span>)</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC807" class="react-file-line html-div" data-testid="code-cell" data-line-number="807" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_steps_per_execution_tuner</span> <span class="pl-c1">=</span> (</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC808" class="react-file-line html-div" data-testid="code-cell" data-line-number="808" style="position:relative"> <span class="pl-s1">steps_per_execution_tuning</span>.<span class="pl-c1">StepsPerExecutionTuner</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC809" class="react-file-line html-div" data-testid="code-cell" data-line-number="809" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">optimizer</span>, <span class="pl-s1">self</span>.<span class="pl-c1">_steps_per_execution</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC810" class="react-file-line html-div" data-testid="code-cell" data-line-number="810" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC811" class="react-file-line html-div" data-testid="code-cell" data-line-number="811" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC812" class="react-file-line html-div" data-testid="code-cell" data-line-number="812" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_autotune_steps_per_execution</span> <span class="pl-c1">=</span> <span class="pl-c1">True</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC813" class="react-file-line html-div" data-testid="code-cell" data-line-number="813" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC814" class="react-file-line html-div" data-testid="code-cell" data-line-number="814" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_configure_steps_per_execution</span>(<span class="pl-s1">steps_per_execution</span> <span class="pl-c1">or</span> <span class="pl-c1">1</span>)</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC815" class="react-file-line html-div" data-testid="code-cell" data-line-number="815" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC816" class="react-file-line html-div" data-testid="code-cell" data-line-number="816" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_pss_evaluation_shards</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_infer_exact_eval_shards</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC817" class="react-file-line html-div" data-testid="code-cell" data-line-number="817" style="position:relative"> <span class="pl-s1">pss_evaluation_shards</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC818" class="react-file-line html-div" data-testid="code-cell" data-line-number="818" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC819" class="react-file-line html-div" data-testid="code-cell" data-line-number="819" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC820" class="react-file-line html-div" data-testid="code-cell" data-line-number="820" style="position:relative"> <span class="pl-c"># Initializes attrs that are reset each time `compile` is called.</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC821" class="react-file-line html-div" data-testid="code-cell" data-line-number="821" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_reset_compile_cache</span>()</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC822" class="react-file-line html-div" data-testid="code-cell" data-line-number="822" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_is_compiled</span> <span class="pl-c1">=</span> <span class="pl-c1">True</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC823" class="react-file-line html-div" data-testid="code-cell" data-line-number="823" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">loss</span> <span class="pl-c1">=</span> <span class="pl-s1">loss</span> <span class="pl-c1">or</span> {}</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC824" class="react-file-line html-div" data-testid="code-cell" data-line-number="824" style="position:relative"> <span class="pl-k">if</span> (<span class="pl-s1">self</span>.<span class="pl-c1">_run_eagerly</span> <span class="pl-c1">or</span> <span class="pl-s1">self</span>.<span class="pl-c1">dynamic</span>) <span class="pl-c1">and</span> <span class="pl-s1">jit_compile</span>:</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC825" class="react-file-line html-div" data-testid="code-cell" data-line-number="825" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC826" class="react-file-line html-div" data-testid="code-cell" data-line-number="826" style="position:relative"> <span class="pl-s">"You cannot enable `run_eagerly` and `jit_compile` "</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC827" class="react-file-line html-div" data-testid="code-cell" data-line-number="827" style="position:relative"> <span class="pl-s">"at the same time."</span></div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC828" class="react-file-line html-div" data-testid="code-cell" data-line-number="828" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-623 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC829" class="react-file-line html-div" data-testid="code-cell" data-line-number="829" style="position:relative"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC830" class="react-file-line html-div" data-testid="code-cell" data-line-number="830" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_jit_compile</span> <span class="pl-c1">=</span> <span class="pl-s1">jit_compile</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC831" class="react-file-line html-div" data-testid="code-cell" data-line-number="831" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC832" class="react-file-line html-div" data-testid="code-cell" data-line-number="832" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_get_optimizer</span>(<span class="pl-s1">self</span>, <span class="pl-s1">optimizer</span>):</div></div></div><div class="child-of-line-70 child-of-line-831 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC833" class="react-file-line html-div" data-testid="code-cell" data-line-number="833" style="position:relative"> <span class="pl-s">"""Wraps `optimizer` in `LossScaleOptimizer` if necessary."""</span></div></div></div><div class="child-of-line-70 child-of-line-831 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC834" class="react-file-line html-div" data-testid="code-cell" data-line-number="834" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-831 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC835" class="react-file-line html-div" data-testid="code-cell" data-line-number="835" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_get_single_optimizer</span>(<span class="pl-s1">opt</span>):</div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC836" class="react-file-line html-div" data-testid="code-cell" data-line-number="836" style="position:relative"> <span class="pl-s1">opt</span> <span class="pl-c1">=</span> <span class="pl-s1">optimizers</span>.<span class="pl-c1">get</span>(<span class="pl-s1">opt</span>)</div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC837" class="react-file-line html-div" data-testid="code-cell" data-line-number="837" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">dtype_policy</span>.<span class="pl-c1">name</span> <span class="pl-c1">==</span> <span class="pl-s">"mixed_float16"</span> <span class="pl-c1">and</span> <span class="pl-c1">not</span> <span class="pl-en">isinstance</span>(</div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC838" class="react-file-line html-div" data-testid="code-cell" data-line-number="838" style="position:relative"> <span class="pl-s1">opt</span>, <span class="pl-s1">lso</span>.<span class="pl-c1">BaseLossScaleOptimizer</span></div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC839" class="react-file-line html-div" data-testid="code-cell" data-line-number="839" style="position:relative"> ):</div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC840" class="react-file-line html-div" data-testid="code-cell" data-line-number="840" style="position:relative"> <span class="pl-c"># Loss scaling is necessary with mixed_float16 for models to</span></div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC841" class="react-file-line html-div" data-testid="code-cell" data-line-number="841" style="position:relative"> <span class="pl-c"># converge to the same accuracy as with float32.</span></div></div></div><div class="child-of-line-70 child-of-line-831 child-of-line-834 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC842" class="react-file-line html-div" data-testid="code-cell" data-line-number="842" style="position:relative"> <span class="pl-s1">opt</span> <span class="pl-c1">=</span> <span class="pl-s1">lso</span>.<span class="pl-c1">BaseLossScaleOptimizer</span>(<span class="pl-s1">opt</span>)</div></div></div><div class="child-of-line-70 child-of-line-831 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC843" class="react-file-line html-div" data-testid="code-cell" data-line-number="843" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">opt</span></div></div></div><div class="child-of-line-70 child-of-line-831 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC844" class="react-file-line html-div" data-testid="code-cell" data-line-number="844" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC845" class="react-file-line html-div" data-testid="code-cell" data-line-number="845" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">tf</span>.<span class="pl-c1">nest</span>.<span class="pl-c1">map_structure</span>(<span class="pl-s1">_get_single_optimizer</span>, <span class="pl-s1">optimizer</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC846" class="react-file-line html-div" data-testid="code-cell" data-line-number="846" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC847" class="react-file-line html-div" data-testid="code-cell" data-line-number="847" style="position:relative"> <span class="pl-en">@<span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">tracking</span>.<span class="pl-c1">no_automatic_dependency_tracking</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC848" class="react-file-line html-div" data-testid="code-cell" data-line-number="848" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_reset_compile_cache</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC849" class="react-file-line html-div" data-testid="code-cell" data-line-number="849" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">train_function</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC850" class="react-file-line html-div" data-testid="code-cell" data-line-number="850" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">test_function</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC851" class="react-file-line html-div" data-testid="code-cell" data-line-number="851" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">predict_function</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC852" class="react-file-line html-div" data-testid="code-cell" data-line-number="852" style="position:relative"> <span class="pl-c"># Used to cache the `tf.function`'ed `train_function` to be logged in</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC853" class="react-file-line html-div" data-testid="code-cell" data-line-number="853" style="position:relative"> <span class="pl-c"># TensorBoard, since the original `train_function` is not necessarily</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC854" class="react-file-line html-div" data-testid="code-cell" data-line-number="854" style="position:relative"> <span class="pl-c"># a `tf.function` (e.g., with ParameterServerStrategy, the</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC855" class="react-file-line html-div" data-testid="code-cell" data-line-number="855" style="position:relative"> <span class="pl-c"># `train_function` is a scheduling of the actual training function to a</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC856" class="react-file-line html-div" data-testid="code-cell" data-line-number="856" style="position:relative"> <span class="pl-c"># remote worker).</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC857" class="react-file-line html-div" data-testid="code-cell" data-line-number="857" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">train_tf_function</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC858" class="react-file-line html-div" data-testid="code-cell" data-line-number="858" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-847 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC859" class="react-file-line html-div" data-testid="code-cell" data-line-number="859" style="position:relative"> <span class="pl-c"># Used to cache `trainable` attr of `Layer`s for `fit`.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC860" class="react-file-line html-div" data-testid="code-cell" data-line-number="860" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_compiled_trainable_state</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_get_trainable_state</span>()</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC861" class="react-file-line html-div" data-testid="code-cell" data-line-number="861" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC862" class="react-file-line html-div" data-testid="code-cell" data-line-number="862" style="position:relative"> <span class="pl-en">@<span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">tracking</span>.<span class="pl-c1">no_automatic_dependency_tracking</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC863" class="react-file-line html-div" data-testid="code-cell" data-line-number="863" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_configure_steps_per_execution</span>(<span class="pl-s1">self</span>, <span class="pl-s1">steps_per_execution</span>):</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC864" class="react-file-line html-div" data-testid="code-cell" data-line-number="864" style="position:relative"> <span class="pl-s1">self</span>.<span class="pl-c1">_steps_per_execution</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_create_counter_variable</span>(</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC865" class="react-file-line html-div" data-testid="code-cell" data-line-number="865" style="position:relative"> <span class="pl-s1">steps_per_execution</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC866" class="react-file-line html-div" data-testid="code-cell" data-line-number="866" style="position:relative"> )</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC867" class="react-file-line html-div" data-testid="code-cell" data-line-number="867" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC868" class="react-file-line html-div" data-testid="code-cell" data-line-number="868" style="position:relative"> <span class="pl-en">@<span class="pl-s1">property</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC869" class="react-file-line html-div" data-testid="code-cell" data-line-number="869" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">_should_compute_mask</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC870" class="react-file-line html-div" data-testid="code-cell" data-line-number="870" style="position:relative"> <span class="pl-k">return</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC871" class="react-file-line html-div" data-testid="code-cell" data-line-number="871" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC872" class="react-file-line html-div" data-testid="code-cell" data-line-number="872" style="position:relative"> <span class="pl-en">@<span class="pl-s1">property</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC873" class="react-file-line html-div" data-testid="code-cell" data-line-number="873" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">metrics</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC874" class="react-file-line html-div" data-testid="code-cell" data-line-number="874" style="position:relative"> <span class="pl-s">"""Return metrics added using `compile()` or `add_metric()`.</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC875" class="react-file-line html-div" data-testid="code-cell" data-line-number="875" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC876" class="react-file-line html-div" data-testid="code-cell" data-line-number="876" style="position:relative"><span class="pl-s"> Note: Metrics passed to `compile()` are available only after a</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC877" class="react-file-line html-div" data-testid="code-cell" data-line-number="877" style="position:relative"><span class="pl-s"> `keras.Model` has been trained/evaluated on actual data.</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC878" class="react-file-line html-div" data-testid="code-cell" data-line-number="878" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC879" class="react-file-line html-div" data-testid="code-cell" data-line-number="879" style="position:relative"><span class="pl-s"> Examples:</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC880" class="react-file-line html-div" data-testid="code-cell" data-line-number="880" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC881" class="react-file-line html-div" data-testid="code-cell" data-line-number="881" style="position:relative"><span class="pl-s"> >>> inputs = tf.keras.layers.Input(shape=(3,))</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC882" class="react-file-line html-div" data-testid="code-cell" data-line-number="882" style="position:relative"><span class="pl-s"> >>> outputs = tf.keras.layers.Dense(2)(inputs)</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC883" class="react-file-line html-div" data-testid="code-cell" data-line-number="883" style="position:relative"><span class="pl-s"> >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC884" class="react-file-line html-div" data-testid="code-cell" data-line-number="884" style="position:relative"><span class="pl-s"> >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC885" class="react-file-line html-div" data-testid="code-cell" data-line-number="885" style="position:relative"><span class="pl-s"> >>> [m.name for m in model.metrics]</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC886" class="react-file-line html-div" data-testid="code-cell" data-line-number="886" style="position:relative"><span class="pl-s"> []</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC887" class="react-file-line html-div" data-testid="code-cell" data-line-number="887" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC888" class="react-file-line html-div" data-testid="code-cell" data-line-number="888" style="position:relative"><span class="pl-s"> >>> x = np.random.random((2, 3))</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC889" class="react-file-line html-div" data-testid="code-cell" data-line-number="889" style="position:relative"><span class="pl-s"> >>> y = np.random.randint(0, 2, (2, 2))</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC890" class="react-file-line html-div" data-testid="code-cell" data-line-number="890" style="position:relative"><span class="pl-s"> >>> model.fit(x, y)</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC891" class="react-file-line html-div" data-testid="code-cell" data-line-number="891" style="position:relative"><span class="pl-s"> >>> [m.name for m in model.metrics]</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC892" class="react-file-line html-div" data-testid="code-cell" data-line-number="892" style="position:relative"><span class="pl-s"> ['loss', 'mae']</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC893" class="react-file-line html-div" data-testid="code-cell" data-line-number="893" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC894" class="react-file-line html-div" data-testid="code-cell" data-line-number="894" style="position:relative"><span class="pl-s"> >>> inputs = tf.keras.layers.Input(shape=(3,))</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC895" class="react-file-line html-div" data-testid="code-cell" data-line-number="895" style="position:relative"><span class="pl-s"> >>> d = tf.keras.layers.Dense(2, name='out')</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC896" class="react-file-line html-div" data-testid="code-cell" data-line-number="896" style="position:relative"><span class="pl-s"> >>> output_1 = d(inputs)</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC897" class="react-file-line html-div" data-testid="code-cell" data-line-number="897" style="position:relative"><span class="pl-s"> >>> output_2 = d(inputs)</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC898" class="react-file-line html-div" data-testid="code-cell" data-line-number="898" style="position:relative"><span class="pl-s"> >>> model = tf.keras.models.Model(</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC899" class="react-file-line html-div" data-testid="code-cell" data-line-number="899" style="position:relative"><span class="pl-s"> ... inputs=inputs, outputs=[output_1, output_2])</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC900" class="react-file-line html-div" data-testid="code-cell" data-line-number="900" style="position:relative"><span class="pl-s"> >>> model.add_metric(</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC901" class="react-file-line html-div" data-testid="code-cell" data-line-number="901" style="position:relative"><span class="pl-s"> ... tf.reduce_sum(output_2), name='mean', aggregation='mean')</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC902" class="react-file-line html-div" data-testid="code-cell" data-line-number="902" style="position:relative"><span class="pl-s"> >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC903" class="react-file-line html-div" data-testid="code-cell" data-line-number="903" style="position:relative"><span class="pl-s"> >>> model.fit(x, (y, y))</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC904" class="react-file-line html-div" data-testid="code-cell" data-line-number="904" style="position:relative"><span class="pl-s"> >>> [m.name for m in model.metrics]</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC905" class="react-file-line html-div" data-testid="code-cell" data-line-number="905" style="position:relative"><span class="pl-s"> ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC906" class="react-file-line html-div" data-testid="code-cell" data-line-number="906" style="position:relative"><span class="pl-s"> 'out_1_acc', 'mean']</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC907" class="react-file-line html-div" data-testid="code-cell" data-line-number="907" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC908" class="react-file-line html-div" data-testid="code-cell" data-line-number="908" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC909" class="react-file-line html-div" data-testid="code-cell" data-line-number="909" style="position:relative"> <span class="pl-s1">metrics</span> <span class="pl-c1">=</span> []</div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC910" class="react-file-line html-div" data-testid="code-cell" data-line-number="910" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_is_compiled</span>:</div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC911" class="react-file-line html-div" data-testid="code-cell" data-line-number="911" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_loss</span> <span class="pl-c1"><span class="pl-c1">is</span> <span class="pl-c1">not</span></span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC912" class="react-file-line html-div" data-testid="code-cell" data-line-number="912" style="position:relative"> <span class="pl-s1">metrics</span> <span class="pl-c1">+=</span> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_loss</span>.<span class="pl-c1">metrics</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC913" class="react-file-line html-div" data-testid="code-cell" data-line-number="913" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_metrics</span> <span class="pl-c1"><span class="pl-c1">is</span> <span class="pl-c1">not</span></span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC914" class="react-file-line html-div" data-testid="code-cell" data-line-number="914" style="position:relative"> <span class="pl-s1">metrics</span> <span class="pl-c1">+=</span> <span class="pl-s1">self</span>.<span class="pl-c1">compiled_metrics</span>.<span class="pl-c1">metrics</span></div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC915" class="react-file-line html-div" data-testid="code-cell" data-line-number="915" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC916" class="react-file-line html-div" data-testid="code-cell" data-line-number="916" style="position:relative"> <span class="pl-k">for</span> <span class="pl-s1">l</span> <span class="pl-c1">in</span> <span class="pl-s1">self</span>.<span class="pl-c1">_flatten_layers</span>():</div></div></div><div class="child-of-line-70 child-of-line-872 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC917" class="react-file-line html-div" data-testid="code-cell" data-line-number="917" style="position:relative"> <span class="pl-s1">metrics</span>.<span class="pl-c1">extend</span>(<span class="pl-s1">l</span>.<span class="pl-c1">_metrics</span>)</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC918" class="react-file-line html-div" data-testid="code-cell" data-line-number="918" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">metrics</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC919" class="react-file-line html-div" data-testid="code-cell" data-line-number="919" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC920" class="react-file-line html-div" data-testid="code-cell" data-line-number="920" style="position:relative"> <span class="pl-en">@<span class="pl-s1">property</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC921" class="react-file-line html-div" data-testid="code-cell" data-line-number="921" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">metrics_names</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC922" class="react-file-line html-div" data-testid="code-cell" data-line-number="922" style="position:relative"> <span class="pl-s">"""Returns the model's display labels for all outputs.</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC923" class="react-file-line html-div" data-testid="code-cell" data-line-number="923" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC924" class="react-file-line html-div" data-testid="code-cell" data-line-number="924" style="position:relative"><span class="pl-s"> Note: `metrics_names` are available only after a `keras.Model` has been</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC925" class="react-file-line html-div" data-testid="code-cell" data-line-number="925" style="position:relative"><span class="pl-s"> trained/evaluated on actual data.</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC926" class="react-file-line html-div" data-testid="code-cell" data-line-number="926" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC927" class="react-file-line html-div" data-testid="code-cell" data-line-number="927" style="position:relative"><span class="pl-s"> Examples:</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC928" class="react-file-line html-div" data-testid="code-cell" data-line-number="928" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC929" class="react-file-line html-div" data-testid="code-cell" data-line-number="929" style="position:relative"><span class="pl-s"> >>> inputs = tf.keras.layers.Input(shape=(3,))</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC930" class="react-file-line html-div" data-testid="code-cell" data-line-number="930" style="position:relative"><span class="pl-s"> >>> outputs = tf.keras.layers.Dense(2)(inputs)</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC931" class="react-file-line html-div" data-testid="code-cell" data-line-number="931" style="position:relative"><span class="pl-s"> >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC932" class="react-file-line html-div" data-testid="code-cell" data-line-number="932" style="position:relative"><span class="pl-s"> >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC933" class="react-file-line html-div" data-testid="code-cell" data-line-number="933" style="position:relative"><span class="pl-s"> >>> model.metrics_names</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC934" class="react-file-line html-div" data-testid="code-cell" data-line-number="934" style="position:relative"><span class="pl-s"> []</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC935" class="react-file-line html-div" data-testid="code-cell" data-line-number="935" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC936" class="react-file-line html-div" data-testid="code-cell" data-line-number="936" style="position:relative"><span class="pl-s"> >>> x = np.random.random((2, 3))</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC937" class="react-file-line html-div" data-testid="code-cell" data-line-number="937" style="position:relative"><span class="pl-s"> >>> y = np.random.randint(0, 2, (2, 2))</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC938" class="react-file-line html-div" data-testid="code-cell" data-line-number="938" style="position:relative"><span class="pl-s"> >>> model.fit(x, y)</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC939" class="react-file-line html-div" data-testid="code-cell" data-line-number="939" style="position:relative"><span class="pl-s"> >>> model.metrics_names</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC940" class="react-file-line html-div" data-testid="code-cell" data-line-number="940" style="position:relative"><span class="pl-s"> ['loss', 'mae']</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC941" class="react-file-line html-div" data-testid="code-cell" data-line-number="941" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC942" class="react-file-line html-div" data-testid="code-cell" data-line-number="942" style="position:relative"><span class="pl-s"> >>> inputs = tf.keras.layers.Input(shape=(3,))</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC943" class="react-file-line html-div" data-testid="code-cell" data-line-number="943" style="position:relative"><span class="pl-s"> >>> d = tf.keras.layers.Dense(2, name='out')</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC944" class="react-file-line html-div" data-testid="code-cell" data-line-number="944" style="position:relative"><span class="pl-s"> >>> output_1 = d(inputs)</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC945" class="react-file-line html-div" data-testid="code-cell" data-line-number="945" style="position:relative"><span class="pl-s"> >>> output_2 = d(inputs)</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC946" class="react-file-line html-div" data-testid="code-cell" data-line-number="946" style="position:relative"><span class="pl-s"> >>> model = tf.keras.models.Model(</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC947" class="react-file-line html-div" data-testid="code-cell" data-line-number="947" style="position:relative"><span class="pl-s"> ... inputs=inputs, outputs=[output_1, output_2])</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC948" class="react-file-line html-div" data-testid="code-cell" data-line-number="948" style="position:relative"><span class="pl-s"> >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC949" class="react-file-line html-div" data-testid="code-cell" data-line-number="949" style="position:relative"><span class="pl-s"> >>> model.fit(x, (y, y))</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC950" class="react-file-line html-div" data-testid="code-cell" data-line-number="950" style="position:relative"><span class="pl-s"> >>> model.metrics_names</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC951" class="react-file-line html-div" data-testid="code-cell" data-line-number="951" style="position:relative"><span class="pl-s"> ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC952" class="react-file-line html-div" data-testid="code-cell" data-line-number="952" style="position:relative"><span class="pl-s"> 'out_1_acc']</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC953" class="react-file-line html-div" data-testid="code-cell" data-line-number="953" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC954" class="react-file-line html-div" data-testid="code-cell" data-line-number="954" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC955" class="react-file-line html-div" data-testid="code-cell" data-line-number="955" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC956" class="react-file-line html-div" data-testid="code-cell" data-line-number="956" style="position:relative"> <span class="pl-c"># This property includes all output names including `loss` and</span></div></div></div><div class="child-of-line-70 child-of-line-920 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC957" class="react-file-line html-div" data-testid="code-cell" data-line-number="957" style="position:relative"> <span class="pl-c"># per-output losses for backward compatibility.</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC958" class="react-file-line html-div" data-testid="code-cell" data-line-number="958" style="position:relative"> <span class="pl-k">return</span> [<span class="pl-s1">m</span>.<span class="pl-c1">name</span> <span class="pl-k">for</span> <span class="pl-s1">m</span> <span class="pl-c1">in</span> <span class="pl-s1">self</span>.<span class="pl-c1">metrics</span>]</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC959" class="react-file-line html-div" data-testid="code-cell" data-line-number="959" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC960" class="react-file-line html-div" data-testid="code-cell" data-line-number="960" style="position:relative"> <span class="pl-en">@<span class="pl-s1">property</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC961" class="react-file-line html-div" data-testid="code-cell" data-line-number="961" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">distribute_strategy</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC962" class="react-file-line html-div" data-testid="code-cell" data-line-number="962" style="position:relative"> <span class="pl-s">"""The `tf.distribute.Strategy` this model was created under."""</span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC963" class="react-file-line html-div" data-testid="code-cell" data-line-number="963" style="position:relative"> <span class="pl-k">return</span> <span class="pl-s1">self</span>.<span class="pl-c1">_distribution_strategy</span> <span class="pl-c1">or</span> <span class="pl-s1">tf</span>.<span class="pl-c1">distribute</span>.<span class="pl-c1">get_strategy</span>()</div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC964" class="react-file-line html-div" data-testid="code-cell" data-line-number="964" style="position:relative"> </div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC965" class="react-file-line html-div" data-testid="code-cell" data-line-number="965" style="position:relative"> <span class="pl-en">@<span class="pl-s1">property</span></span></div></div></div><div class="child-of-line-70 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC966" class="react-file-line html-div" data-testid="code-cell" data-line-number="966" style="position:relative"> <span class="pl-k">def</span> <span class="pl-en">run_eagerly</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC967" class="react-file-line html-div" data-testid="code-cell" data-line-number="967" style="position:relative"> <span class="pl-s">"""Settable attribute indicating whether the model should run eagerly.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC968" class="react-file-line html-div" data-testid="code-cell" data-line-number="968" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC969" class="react-file-line html-div" data-testid="code-cell" data-line-number="969" style="position:relative"><span class="pl-s"> Running eagerly means that your model will be run step by step,</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC970" class="react-file-line html-div" data-testid="code-cell" data-line-number="970" style="position:relative"><span class="pl-s"> like Python code. Your model might run slower, but it should become</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC971" class="react-file-line html-div" data-testid="code-cell" data-line-number="971" style="position:relative"><span class="pl-s"> easier for you to debug it by stepping into individual layer calls.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC972" class="react-file-line html-div" data-testid="code-cell" data-line-number="972" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC973" class="react-file-line html-div" data-testid="code-cell" data-line-number="973" style="position:relative"><span class="pl-s"> By default, we will attempt to compile your model to a static graph to</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC974" class="react-file-line html-div" data-testid="code-cell" data-line-number="974" style="position:relative"><span class="pl-s"> deliver the best execution performance.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC975" class="react-file-line html-div" data-testid="code-cell" data-line-number="975" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC976" class="react-file-line html-div" data-testid="code-cell" data-line-number="976" style="position:relative"><span class="pl-s"> Returns:</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC977" class="react-file-line html-div" data-testid="code-cell" data-line-number="977" style="position:relative"><span class="pl-s"> Boolean, whether the model should run eagerly.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC978" class="react-file-line html-div" data-testid="code-cell" data-line-number="978" style="position:relative"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC979" class="react-file-line html-div" data-testid="code-cell" data-line-number="979" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">dynamic</span> <span class="pl-c1">and</span> <span class="pl-s1">self</span>.<span class="pl-c1">_run_eagerly</span> <span class="pl-c1">==</span> <span class="pl-c1">False</span>:</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC980" class="react-file-line html-div" data-testid="code-cell" data-line-number="980" style="position:relative"> <span class="pl-c"># TODO(fchollet): consider using py_func to enable this.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC981" class="react-file-line html-div" data-testid="code-cell" data-line-number="981" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC982" class="react-file-line html-div" data-testid="code-cell" data-line-number="982" style="position:relative"> <span class="pl-s">"Your model contains layers that can only be "</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC983" class="react-file-line html-div" data-testid="code-cell" data-line-number="983" style="position:relative"> <span class="pl-s">"successfully run in eager execution (layers "</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC984" class="react-file-line html-div" data-testid="code-cell" data-line-number="984" style="position:relative"> <span class="pl-s">"constructed with `dynamic=True`). "</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC985" class="react-file-line html-div" data-testid="code-cell" data-line-number="985" style="position:relative"> <span class="pl-s">"You cannot set `run_eagerly=False`."</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC986" class="react-file-line html-div" data-testid="code-cell" data-line-number="986" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC987" class="react-file-line html-div" data-testid="code-cell" data-line-number="987" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC988" class="react-file-line html-div" data-testid="code-cell" data-line-number="988" style="position:relative"> <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_cluster_coordinator</span> <span class="pl-c1">and</span> <span class="pl-s1">self</span>.<span class="pl-c1">_run_eagerly</span>:</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC989" class="react-file-line html-div" data-testid="code-cell" data-line-number="989" style="position:relative"> <span class="pl-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC990" class="react-file-line html-div" data-testid="code-cell" data-line-number="990" style="position:relative"> <span class="pl-s">"When using `Model` with `ParameterServerStrategy`, "</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC991" class="react-file-line html-div" data-testid="code-cell" data-line-number="991" style="position:relative"> <span class="pl-s">"`run_eagerly` is not supported."</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC992" class="react-file-line html-div" data-testid="code-cell" data-line-number="992" style="position:relative"> )</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC993" class="react-file-line html-div" data-testid="code-cell" data-line-number="993" style="position:relative"> </div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC994" class="react-file-line html-div" data-testid="code-cell" data-line-number="994" style="position:relative"> <span class="pl-c"># Run eagerly logic, by priority:</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC995" class="react-file-line html-div" data-testid="code-cell" data-line-number="995" style="position:relative"> <span class="pl-c"># (1) Dynamic models must be run eagerly.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC996" class="react-file-line html-div" data-testid="code-cell" data-line-number="996" style="position:relative"> <span class="pl-c"># (2) Explicitly setting run_eagerly causes a Model to be run eagerly.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC997" class="react-file-line html-div" data-testid="code-cell" data-line-number="997" style="position:relative"> <span class="pl-c"># (3) Not explicitly setting run_eagerly defaults to TF's global</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC998" class="react-file-line html-div" data-testid="code-cell" data-line-number="998" style="position:relative"> <span class="pl-c"># setting.</span></div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC999" class="react-file-line html-div" data-testid="code-cell" data-line-number="999" style="position:relative"> <span class="pl-k">return</span> (</div></div></div><div class="child-of-line-70 child-of-line-965 react-code-text react-code-line-contents" style="min-height:auto"><div><div id="LC1000" class="react-file-line html-div" data-testid="code-cell" 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d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>