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class="octicon octicon-device-desktop"> <path d="M14.25 1c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 14.25 12h-3.727c.099 1.041.52 1.872 1.292 2.757A.752.752 0 0 1 11.25 16h-6.5a.75.75 0 0 1-.565-1.243c.772-.885 1.192-1.716 1.292-2.757H1.75A1.75 1.75 0 0 1 0 10.25v-7.5C0 1.784.784 1 1.75 1ZM1.75 2.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25ZM9.018 12H6.982a5.72 5.72 0 0 1-.765 2.5h3.566a5.72 5.72 0 0 1-.765-2.5Z"></path> </svg> </template> <div class="position-relative"> <ul role="listbox" class="ActionListWrap QueryBuilder-ListWrap" aria-label="Suggestions" data-action=" combobox-commit:query-builder#comboboxCommit mousedown:query-builder#resultsMousedown " data-target="query-builder.resultsList" data-persist-list=false id="query-builder-test-results" ></ul> </div> <div class="FormControl-inlineValidation" id="validation-342da5fb-0018-430b-8ddd-30240198c749" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> 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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.","# ==============================================================================","","\"\"\"Built-in loss functions.\"\"\"","","","import abc","import functools","import warnings","","import tensorflow.compat.v2 as tf","","from tf_keras import backend","from tf_keras.saving import saving_lib","from tf_keras.saving.legacy import serialization as legacy_serialization","from tf_keras.saving.serialization_lib import deserialize_keras_object","from tf_keras.saving.serialization_lib import serialize_keras_object","from tf_keras.utils import losses_utils","from tf_keras.utils import tf_utils","","# isort: off","from tensorflow.python.ops.ragged import ragged_map_ops","from tensorflow.python.ops.ragged import ragged_util","from tensorflow.python.util import dispatch","from tensorflow.python.util.tf_export import keras_export","from tensorflow.tools.docs import doc_controls","","","@keras_export(\"keras.losses.Loss\")","class Loss:"," \"\"\"Loss base class.",""," To be implemented by subclasses:"," * `call()`: Contains the logic for loss calculation using `y_true`,"," `y_pred`.",""," Example subclass implementation:",""," ```python"," class MeanSquaredError(Loss):",""," def call(self, y_true, y_pred):"," return tf.reduce_mean(tf.math.square(y_pred - y_true), axis=-1)"," ```",""," When using a Loss under a `tf.distribute.Strategy`, except passing it"," to `Model.compile()` for use by `Model.fit()`, please use reduction"," types 'SUM' or 'NONE', and reduce losses explicitly. Using 'AUTO' or"," 'SUM_OVER_BATCH_SIZE' will raise an error when calling the Loss object"," from a custom training loop or from user-defined code in `Layer.call()`."," Please see this custom training"," [tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details on this."," \"\"\"",""," def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name=None):"," \"\"\"Initializes `Loss` class.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," \"\"\""," losses_utils.ReductionV2.validate(reduction)"," self.reduction = reduction"," self.name = name"," # SUM_OVER_BATCH is only allowed in losses managed by `fit` or"," # CannedEstimators."," self._allow_sum_over_batch_size = False"," self._set_name_scope()",""," def _set_name_scope(self):"," \"\"\"Creates a valid `name_scope` name.\"\"\""," if self.name is None:"," self._name_scope = self.__class__.__name__.strip(\"_\")"," elif self.name == \"\u003clambda\u003e\":"," self._name_scope = \"lambda\""," else:"," # E.g. '_my_loss' =\u003e 'my_loss'"," self._name_scope = self.name.strip(\"_\")",""," def __call__(self, y_true, y_pred, sample_weight=None):"," \"\"\"Invokes the `Loss` instance.",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`,"," except sparse loss functions such as sparse categorical"," crossentropy where shape = `[batch_size, d0, .. dN-1]`"," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`"," sample_weight: Optional `sample_weight` acts as a coefficient for"," the loss. If a scalar is provided, then the loss is simply"," scaled by the given value. If `sample_weight` is a tensor of"," size `[batch_size]`, then the total loss for each sample of the"," batch is rescaled by the corresponding element in the"," `sample_weight` vector. If the shape of `sample_weight` is"," `[batch_size, d0, .. dN-1]` (or can be broadcasted to this"," shape), then each loss element of `y_pred` is scaled by the"," corresponding value of `sample_weight`. (Note on`dN-1`: all loss"," functions reduce by 1 dimension, usually axis=-1.)",""," Returns:"," Weighted loss float `Tensor`. If `reduction` is `NONE`, this has"," shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar."," (Note `dN-1` because all loss functions reduce by 1 dimension,"," usually axis=-1.)",""," Raises:"," ValueError: If the shape of `sample_weight` is invalid."," \"\"\""," # If we are wrapping a lambda function strip '\u003c\u003e' from the name as it is"," # not accepted in scope name."," graph_ctx = tf_utils.graph_context_for_symbolic_tensors("," y_true, y_pred, sample_weight"," )"," with backend.name_scope(self._name_scope), graph_ctx:"," if tf.executing_eagerly():"," call_fn = self.call"," else:"," call_fn = tf.__internal__.autograph.tf_convert("," self.call, tf.__internal__.autograph.control_status_ctx()"," )",""," losses = call_fn(y_true, y_pred)",""," in_mask = losses_utils.get_mask(y_pred)"," out_mask = losses_utils.get_mask(losses)",""," if in_mask is not None and out_mask is not None:"," mask = in_mask \u0026 out_mask"," elif in_mask is not None:"," mask = in_mask"," elif out_mask is not None:"," mask = out_mask"," else:"," mask = None",""," reduction = self._get_reduction()"," sample_weight = losses_utils.apply_valid_mask("," losses, sample_weight, mask, reduction"," )"," return losses_utils.compute_weighted_loss("," losses, sample_weight, reduction=reduction"," )",""," @classmethod"," def from_config(cls, config):"," \"\"\"Instantiates a `Loss` from its config (output of `get_config()`).",""," Args:"," config: Output of `get_config()`.",""," Returns:"," A `Loss` instance."," \"\"\""," return cls(**config)",""," def get_config(self):"," \"\"\"Returns the config dictionary for a `Loss` instance.\"\"\""," return {\"reduction\": self.reduction, \"name\": self.name}",""," @abc.abstractmethod"," @doc_controls.for_subclass_implementers"," def call(self, y_true, y_pred):"," \"\"\"Invokes the `Loss` instance.",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`,"," except sparse loss functions such as sparse categorical"," crossentropy where shape = `[batch_size, d0, .. dN-1]`"," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`",""," Returns:"," Loss values with the shape `[batch_size, d0, .. dN-1]`."," \"\"\""," raise NotImplementedError(\"Must be implemented in subclasses.\")",""," def _get_reduction(self):"," \"\"\"Handles `AUTO` reduction cases and returns the reduction value.\"\"\""," if ("," not self._allow_sum_over_batch_size"," and tf.distribute.has_strategy()"," and ("," self.reduction == losses_utils.ReductionV2.AUTO"," or self.reduction"," == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE"," )"," ):"," raise ValueError("," \"Please use `tf.keras.losses.Reduction.SUM` or \""," \"`tf.keras.losses.Reduction.NONE` for loss reduction when \""," \"losses are used with `tf.distribute.Strategy`, \""," \"except for specifying losses in `Model.compile()` \""," \"for use by the built-in training looop `Model.fit()`.\\n\""," \"Please see https://www.tensorflow.org/tutorials\""," \"/distribute/custom_training for more details.\""," )",""," if self.reduction == losses_utils.ReductionV2.AUTO:"," return losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE"," return self.reduction","","","@keras_export(\"keras.__internal__.losses.LossFunctionWrapper\", v1=[])","class LossFunctionWrapper(Loss):"," \"\"\"Wraps a loss function in the `Loss` class.\"\"\"",""," def __init__("," self, fn, reduction=losses_utils.ReductionV2.AUTO, name=None, **kwargs"," ):"," \"\"\"Initializes `LossFunctionWrapper` class.",""," Args:"," fn: The loss function to wrap, with signature `fn(y_true, y_pred,"," **kwargs)`."," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," **kwargs: The keyword arguments that are passed on to `fn`."," \"\"\""," super().__init__(reduction=reduction, name=name)"," self.fn = fn"," self._fn_kwargs = kwargs",""," def call(self, y_true, y_pred):"," \"\"\"Invokes the `LossFunctionWrapper` instance.",""," Args:"," y_true: Ground truth values."," y_pred: The predicted values.",""," Returns:"," Loss values per sample."," \"\"\""," if tf.is_tensor(y_pred) and tf.is_tensor(y_true):"," y_pred, y_true = losses_utils.squeeze_or_expand_dimensions("," y_pred, y_true"," )",""," ag_fn = tf.__internal__.autograph.tf_convert("," self.fn, tf.__internal__.autograph.control_status_ctx()"," )"," return ag_fn(y_true, y_pred, **self._fn_kwargs)",""," def get_config(self):"," config = {}"," for k, v in self._fn_kwargs.items():"," config[k] = ("," backend.eval(v) if tf_utils.is_tensor_or_variable(v) else v"," )",""," if saving_lib.saving_v3_enabled():"," from tf_keras.utils import get_registered_name",""," config[\"fn\"] = get_registered_name(self.fn)",""," base_config = super().get_config()"," return dict(list(base_config.items()) + list(config.items()))",""," @classmethod"," def from_config(cls, config):"," \"\"\"Instantiates a `Loss` from its config (output of `get_config()`).",""," Args:"," config: Output of `get_config()`.",""," Returns:"," A `keras.losses.Loss` instance."," \"\"\""," if saving_lib.saving_v3_enabled():"," fn_name = config.pop(\"fn\", None)"," if fn_name and cls is LossFunctionWrapper:"," config[\"fn\"] = get(fn_name)"," return cls(**config)","","","@keras_export(\"keras.losses.MeanSquaredError\")","class MeanSquaredError(LossFunctionWrapper):"," \"\"\"Computes the mean of squares of errors between labels and predictions.",""," `loss = mean(square(y_true - y_pred))`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[1., 1.], [1., 0.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e mse = tf.keras.losses.MeanSquaredError()"," \u003e\u003e\u003e mse(y_true, y_pred).numpy()"," 0.5",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e mse(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()"," 0.25",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e mse = tf.keras.losses.MeanSquaredError("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e mse(y_true, y_pred).numpy()"," 1.0",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e mse = tf.keras.losses.MeanSquaredError("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e mse(y_true, y_pred).numpy()"," array([0.5, 0.5], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredError())"," ```"," \"\"\"",""," def __init__("," self, reduction=losses_utils.ReductionV2.AUTO, name=\"mean_squared_error\""," ):"," \"\"\"Initializes `MeanSquaredError` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to"," 'mean_squared_error'."," \"\"\""," super().__init__(mean_squared_error, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.MeanAbsoluteError\")","class MeanAbsoluteError(LossFunctionWrapper):"," \"\"\"Computes the mean of absolute difference between labels and predictions.",""," `loss = mean(abs(y_true - y_pred))`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[1., 1.], [1., 0.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e mae = tf.keras.losses.MeanAbsoluteError()"," \u003e\u003e\u003e mae(y_true, y_pred).numpy()"," 0.5",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e mae(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()"," 0.25",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e mae = tf.keras.losses.MeanAbsoluteError("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e mae(y_true, y_pred).numpy()"," 1.0",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e mae = tf.keras.losses.MeanAbsoluteError("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e mae(y_true, y_pred).numpy()"," array([0.5, 0.5], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.MeanAbsoluteError())"," ```"," \"\"\"",""," def __init__("," self,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"mean_absolute_error\","," ):"," \"\"\"Initializes `MeanAbsoluteError` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to"," 'mean_absolute_error'."," \"\"\""," super().__init__(mean_absolute_error, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.MeanAbsolutePercentageError\")","class MeanAbsolutePercentageError(LossFunctionWrapper):"," \"\"\"Computes the mean absolute percentage error between `y_true` \u0026 `y_pred`.",""," Formula:",""," `loss = 100 * abs((y_true - y_pred) / y_true)`",""," Note that to avoid dividing by zero, a small epsilon value"," is added to the denominator.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[2., 1.], [2., 3.]]"," \u003e\u003e\u003e y_pred = [[1., 1.], [1., 0.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e mape = tf.keras.losses.MeanAbsolutePercentageError()"," \u003e\u003e\u003e mape(y_true, y_pred).numpy()"," 50.",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e mape(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()"," 20.",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e mape = tf.keras.losses.MeanAbsolutePercentageError("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e mape(y_true, y_pred).numpy()"," 100.",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e mape = tf.keras.losses.MeanAbsolutePercentageError("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e mape(y_true, y_pred).numpy()"," array([25., 75.], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd',"," loss=tf.keras.losses.MeanAbsolutePercentageError())"," ```"," \"\"\"",""," def __init__("," self,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"mean_absolute_percentage_error\","," ):"," \"\"\"Initializes `MeanAbsolutePercentageError` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to"," 'mean_absolute_percentage_error'."," \"\"\""," super().__init__("," mean_absolute_percentage_error, name=name, reduction=reduction"," )","","","@keras_export(\"keras.losses.MeanSquaredLogarithmicError\")","class MeanSquaredLogarithmicError(LossFunctionWrapper):"," \"\"\"Computes the mean squared logarithmic error between `y_true` \u0026 `y_pred`.",""," `loss = square(log(y_true + 1.) - log(y_pred + 1.))`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[1., 1.], [1., 0.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e msle = tf.keras.losses.MeanSquaredLogarithmicError()"," \u003e\u003e\u003e msle(y_true, y_pred).numpy()"," 0.240",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e msle(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()"," 0.120",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e msle = tf.keras.losses.MeanSquaredLogarithmicError("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e msle(y_true, y_pred).numpy()"," 0.480",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e msle = tf.keras.losses.MeanSquaredLogarithmicError("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e msle(y_true, y_pred).numpy()"," array([0.240, 0.240], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd',"," loss=tf.keras.losses.MeanSquaredLogarithmicError())"," ```"," \"\"\"",""," def __init__("," self,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"mean_squared_logarithmic_error\","," ):"," \"\"\"Initializes `MeanSquaredLogarithmicError` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to"," 'mean_squared_logarithmic_error'."," \"\"\""," super().__init__("," mean_squared_logarithmic_error, name=name, reduction=reduction"," )","","","@keras_export(\"keras.losses.BinaryCrossentropy\")","class BinaryCrossentropy(LossFunctionWrapper):"," \"\"\"Computes the cross-entropy loss between true labels and predicted labels.",""," Use this cross-entropy loss for binary (0 or 1) classification applications."," The loss function requires the following inputs:",""," - `y_true` (true label): This is either 0 or 1."," - `y_pred` (predicted value): This is the model's prediction, i.e, a single"," floating-point value which either represents a"," [logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf]"," when `from_logits=True`) or a probability (i.e, value in [0., 1.] when"," `from_logits=False`).",""," **Recommended Usage:** (set `from_logits=True`)",""," With `tf.keras` API:",""," ```python"," model.compile("," loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),"," ...."," )"," ```",""," As a standalone function:",""," \u003e\u003e\u003e # Example 1: (batch_size = 1, number of samples = 4)"," \u003e\u003e\u003e y_true = [0, 1, 0, 0]"," \u003e\u003e\u003e y_pred = [-18.6, 0.51, 2.94, -12.8]"," \u003e\u003e\u003e bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)"," \u003e\u003e\u003e bce(y_true, y_pred).numpy()"," 0.865",""," \u003e\u003e\u003e # Example 2: (batch_size = 2, number of samples = 4)"," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[-18.6, 0.51], [2.94, -12.8]]"," \u003e\u003e\u003e # Using default 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)"," \u003e\u003e\u003e bce(y_true, y_pred).numpy()"," 0.865"," \u003e\u003e\u003e # Using 'sample_weight' attribute"," \u003e\u003e\u003e bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," 0.243"," \u003e\u003e\u003e # Using 'sum' reduction` type."," \u003e\u003e\u003e bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,"," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e bce(y_true, y_pred).numpy()"," 1.730"," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,"," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e bce(y_true, y_pred).numpy()"," array([0.235, 1.496], dtype=float32)",""," **Default Usage:** (set `from_logits=False`)",""," \u003e\u003e\u003e # Make the following updates to the above \"Recommended Usage\" section"," \u003e\u003e\u003e # 1. Set `from_logits=False`"," \u003e\u003e\u003e tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')"," \u003e\u003e\u003e # 2. Update `y_pred` to use probabilities instead of logits"," \u003e\u003e\u003e y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]"," \"\"\"",""," def __init__("," self,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"binary_crossentropy\","," ):"," \"\"\"Initializes `BinaryCrossentropy` instance.",""," Args:"," from_logits: Whether to interpret `y_pred` as a tensor of"," [logit](https://en.wikipedia.org/wiki/Logit) values. By default,"," we assume that `y_pred` contains probabilities (i.e., values in"," [0, 1])."," label_smoothing: Float in [0, 1]. When 0, no smoothing occurs."," When \u003e 0, we compute the loss between the predicted labels and a"," smoothed version of the true labels, where the smoothing"," squeezes the labels towards 0.5. Larger values of"," `label_smoothing` correspond to heavier smoothing."," axis: The axis along which to compute crossentropy (the features"," axis). Defaults to -1."," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction option will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Name for the op. Defaults to 'binary_crossentropy'."," \"\"\""," super().__init__("," binary_crossentropy,"," name=name,"," reduction=reduction,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," self.from_logits = from_logits","","","@keras_export(\"keras.losses.BinaryFocalCrossentropy\")","class BinaryFocalCrossentropy(LossFunctionWrapper):"," \"\"\"Computes focal cross-entropy loss between true labels and predictions.",""," Binary cross-entropy loss is often used for binary (0 or 1) classification"," tasks. The loss function requires the following inputs:",""," - `y_true` (true label): This is either 0 or 1."," - `y_pred` (predicted value): This is the model's prediction, i.e, a single"," floating-point value which either represents a"," [logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf]"," when `from_logits=True`) or a probability (i.e, value in [0., 1.] when"," `from_logits=False`).",""," According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it"," helps to apply a \"focal factor\" to down-weight easy examples and focus more"," on hard examples. By default, the focal tensor is computed as follows:",""," `focal_factor = (1 - output) ** gamma` for class 1"," `focal_factor = output ** gamma` for class 0"," where `gamma` is a focusing parameter. When `gamma=0`, this function is"," equivalent to the binary crossentropy loss.",""," With the `compile()` API:",""," ```python"," model.compile("," loss=tf.keras.losses.BinaryFocalCrossentropy(gamma=2.0, from_logits=True),"," ...."," )"," ```",""," As a standalone function:",""," \u003e\u003e\u003e # Example 1: (batch_size = 1, number of samples = 4)"," \u003e\u003e\u003e y_true = [0, 1, 0, 0]"," \u003e\u003e\u003e y_pred = [-18.6, 0.51, 2.94, -12.8]"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=2,"," ... from_logits=True)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," 0.691",""," \u003e\u003e\u003e # Apply class weight"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy("," ... apply_class_balancing=True, gamma=2, from_logits=True)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," 0.51",""," \u003e\u003e\u003e # Example 2: (batch_size = 2, number of samples = 4)"," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[-18.6, 0.51], [2.94, -12.8]]"," \u003e\u003e\u003e # Using default 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=3,"," ... from_logits=True)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," 0.647",""," \u003e\u003e\u003e # Apply class weight"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy("," ... apply_class_balancing=True, gamma=3, from_logits=True)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," 0.482",""," \u003e\u003e\u003e # Using 'sample_weight' attribute with focal effect"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=3,"," ... from_logits=True)"," \u003e\u003e\u003e loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," 0.133",""," \u003e\u003e\u003e # Apply class weight"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy("," ... apply_class_balancing=True, gamma=3, from_logits=True)"," \u003e\u003e\u003e loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," 0.097",""," \u003e\u003e\u003e # Using 'sum' reduction` type."," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=4,"," ... from_logits=True,"," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," 1.222",""," \u003e\u003e\u003e # Apply class weight"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy("," ... apply_class_balancing=True, gamma=4, from_logits=True,"," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," 0.914",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy("," ... gamma=5, from_logits=True,"," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," array([0.0017 1.1561], dtype=float32)",""," \u003e\u003e\u003e # Apply class weight"," \u003e\u003e\u003e loss = tf.keras.losses.BinaryFocalCrossentropy("," ... apply_class_balancing=True, gamma=5, from_logits=True,"," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e loss(y_true, y_pred).numpy()"," array([0.0004 0.8670], dtype=float32)","",""," Args:"," apply_class_balancing: A bool, whether to apply weight balancing on the"," binary classes 0 and 1."," alpha: A weight balancing factor for class 1, default is `0.25` as"," mentioned in reference [Lin et al., 2018]("," https://arxiv.org/pdf/1708.02002.pdf). The weight for class 0 is"," `1.0 - alpha`."," gamma: A focusing parameter used to compute the focal factor, default is"," `2.0` as mentioned in the reference"," [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf)."," from_logits: Whether to interpret `y_pred` as a tensor of"," [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we"," assume that `y_pred` are probabilities (i.e., values in `[0, 1]`)."," label_smoothing: Float in `[0, 1]`. When `0`, no smoothing occurs."," When \u003e `0`, we compute the loss between the predicted labels and a"," smoothed version of the true labels, where the smoothing squeezes"," the labels towards `0.5`. Larger values of `label_smoothing`"," correspond to heavier smoothing."," axis: The axis along which to compute crossentropy (the features axis)."," Defaults to `-1`."," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the reduction"," option will be determined by the usage context. For almost all cases"," this defaults to `SUM_OVER_BATCH_SIZE`. When used under a"," `tf.distribute.Strategy`, except via `Model.compile()` and"," `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE`"," will raise an error. Please see this custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Name for the op. Defaults to 'binary_focal_crossentropy'."," \"\"\"",""," def __init__("," self,"," apply_class_balancing=False,"," alpha=0.25,"," gamma=2.0,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"binary_focal_crossentropy\","," ):"," \"\"\"Initializes `BinaryFocalCrossentropy` instance.\"\"\""," super().__init__("," binary_focal_crossentropy,"," apply_class_balancing=apply_class_balancing,"," alpha=alpha,"," gamma=gamma,"," name=name,"," reduction=reduction,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," self.from_logits = from_logits"," self.apply_class_balancing = apply_class_balancing"," self.alpha = alpha"," self.gamma = gamma",""," def get_config(self):"," config = {"," \"apply_class_balancing\": self.apply_class_balancing,"," \"alpha\": self.alpha,"," \"gamma\": self.gamma,"," }"," base_config = super().get_config()"," return dict(list(base_config.items()) + list(config.items()))","","","@keras_export(\"keras.losses.CategoricalCrossentropy\")","class CategoricalCrossentropy(LossFunctionWrapper):"," \"\"\"Computes the crossentropy loss between the labels and predictions.",""," Use this crossentropy loss function when there are two or more label"," classes. We expect labels to be provided in a `one_hot` representation. If"," you want to provide labels as integers, please use"," `SparseCategoricalCrossentropy` loss. There should be `# classes` floating"," point values per feature.",""," In the snippet below, there is `# classes` floating pointing values per"," example. The shape of both `y_pred` and `y_true` are"," `[batch_size, num_classes]`.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1, 0], [0, 0, 1]]"," \u003e\u003e\u003e y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e cce = tf.keras.losses.CategoricalCrossentropy()"," \u003e\u003e\u003e cce(y_true, y_pred).numpy()"," 1.177",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()"," 0.814",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e cce = tf.keras.losses.CategoricalCrossentropy("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e cce(y_true, y_pred).numpy()"," 2.354",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e cce = tf.keras.losses.CategoricalCrossentropy("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e cce(y_true, y_pred).numpy()"," array([0.0513, 2.303], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd',"," loss=tf.keras.losses.CategoricalCrossentropy())"," ```"," \"\"\"",""," def __init__("," self,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"categorical_crossentropy\","," ):"," \"\"\"Initializes `CategoricalCrossentropy` instance.",""," Args:"," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability"," distribution."," label_smoothing: Float in [0, 1]. When \u003e 0, label values are"," smoothed, meaning the confidence on label values are relaxed."," For example, if `0.1`, use `0.1 / num_classes` for non-target"," labels and `0.9 + 0.1 / num_classes` for target labels."," axis: The axis along which to compute crossentropy (the features"," axis). Defaults to -1."," reduction: Type of `tf.keras.losses.Reduction` to apply to loss."," Default value is `AUTO`. `AUTO` indicates that the reduction"," option will be determined by the usage context. For almost all"," cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a"," `tf.distribute.Strategy`, except via `Model.compile()` and"," `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE`"," will raise an error. Please see this custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," Defaults to 'categorical_crossentropy'."," \"\"\""," super().__init__("," categorical_crossentropy,"," name=name,"," reduction=reduction,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )","","","@keras_export(\"keras.losses.CategoricalFocalCrossentropy\")","class CategoricalFocalCrossentropy(LossFunctionWrapper):"," \"\"\"Computes the alpha balanced focal crossentropy loss.",""," Use this crossentropy loss function when there are two or more label"," classes and if you want to handle class imbalance without using"," `class_weights`. We expect labels to be provided in a `one_hot`"," representation.",""," According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it"," helps to apply a focal factor to down-weight easy examples and focus more on"," hard examples. The general formula for the focal loss (FL)"," is as follows:",""," `FL(p_t) = (1 − p_t)^gamma * log(p_t)`",""," where `p_t` is defined as follows:"," `p_t = output if y_true == 1, else 1 - output`",""," `(1 − p_t)^gamma` is the `modulating_factor`, where `gamma` is a focusing"," parameter. When `gamma` = 0, there is no focal effect on the cross entropy."," `gamma` reduces the importance given to simple examples in a smooth manner.",""," The authors use alpha-balanced variant of focal loss (FL) in the paper:"," `FL(p_t) = −alpha * (1 − p_t)^gamma * log(p_t)`",""," where `alpha` is the weight factor for the classes. If `alpha` = 1, the"," loss won't be able to handle class imbalance properly as all"," classes will have the same weight. This can be a constant or a list of"," constants. If alpha is a list, it must have the same length as the number"," of classes.",""," The formula above can be generalized to:"," `FL(p_t) = alpha * (1 − p_t)^gamma * CrossEntropy(y_true, y_pred)`",""," where minus comes from `CrossEntropy(y_true, y_pred)` (CE).",""," Extending this to multi-class case is straightforward:"," `FL(p_t) = alpha * (1 − p_t)^gamma * CategoricalCE(y_true, y_pred)`",""," In the snippet below, there is `# classes` floating pointing values per"," example. The shape of both `y_pred` and `y_true` are"," `[batch_size, num_classes]`.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1., 0.], [0., 0., 1.]]"," \u003e\u003e\u003e y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e cce = tf.keras.losses.CategoricalFocalCrossentropy()"," \u003e\u003e\u003e cce(y_true, y_pred).numpy()"," 0.23315276",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()"," 0.1632",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e cce = tf.keras.losses.CategoricalFocalCrossentropy("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e cce(y_true, y_pred).numpy()"," 0.46631",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e cce = tf.keras.losses.CategoricalFocalCrossentropy("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e cce(y_true, y_pred).numpy()"," array([3.2058331e-05, 4.6627346e-01], dtype=float32)",""," Usage with the `compile()` API:"," ```python"," model.compile(optimizer='adam',"," loss=tf.keras.losses.CategoricalFocalCrossentropy())"," ```",""," Args:"," alpha: A weight balancing factor for all classes, default is `0.25` as"," mentioned in the reference. It can be a list of floats or a scalar."," In the multi-class case, alpha may be set by inverse class"," frequency by using `compute_class_weight` from `sklearn.utils`."," gamma: A focusing parameter, default is `2.0` as mentioned in the"," reference. It helps to gradually reduce the importance given to"," simple (easy) examples in a smooth manner."," from_logits: Whether `output` is expected to be a logits tensor. By"," default, we consider that `output` encodes a probability"," distribution."," label_smoothing: Float in [0, 1]. When \u003e 0, label values are smoothed,"," meaning the confidence on label values are relaxed. For example, if"," `0.1`, use `0.1 / num_classes` for non-target labels and"," `0.9 + 0.1 / num_classes` for target labels."," axis: The axis along which to compute crossentropy (the features"," axis). Defaults to -1."," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the reduction"," option will be determined by the usage context. For almost all cases"," this defaults to `SUM_OVER_BATCH_SIZE`. When used under a"," `tf.distribute.Strategy`, except via `Model.compile()` and"," `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE`"," will raise an error. Please see this custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," Defaults to 'categorical_focal_crossentropy'.",""," \"\"\"",""," def __init__("," self,"," alpha=0.25,"," gamma=2.0,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"categorical_focal_crossentropy\","," ):"," \"\"\"Initializes `CategoricalFocalCrossentropy` instance.\"\"\""," super().__init__("," categorical_focal_crossentropy,"," alpha=alpha,"," gamma=gamma,"," name=name,"," reduction=reduction,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," self.from_logits = from_logits"," self.alpha = alpha"," self.gamma = gamma",""," def get_config(self):"," config = {"," \"alpha\": self.alpha,"," \"gamma\": self.gamma,"," }"," base_config = super().get_config()"," return dict(list(base_config.items()) + list(config.items()))","","","@keras_export(\"keras.losses.SparseCategoricalCrossentropy\")","class SparseCategoricalCrossentropy(LossFunctionWrapper):"," \"\"\"Computes the crossentropy loss between the labels and predictions.",""," Use this crossentropy loss function when there are two or more label"," classes. We expect labels to be provided as integers. If you want to"," provide labels using `one-hot` representation, please use"," `CategoricalCrossentropy` loss. There should be `# classes` floating point"," values per feature for `y_pred` and a single floating point value per"," feature for `y_true`.",""," In the snippet below, there is a single floating point value per example for"," `y_true` and `# classes` floating pointing values per example for `y_pred`."," The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is"," `[batch_size, num_classes]`.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [1, 2]"," \u003e\u003e\u003e y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e scce = tf.keras.losses.SparseCategoricalCrossentropy()"," \u003e\u003e\u003e scce(y_true, y_pred).numpy()"," 1.177",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e scce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()"," 0.814",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e scce = tf.keras.losses.SparseCategoricalCrossentropy("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e scce(y_true, y_pred).numpy()"," 2.354",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e scce = tf.keras.losses.SparseCategoricalCrossentropy("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e scce(y_true, y_pred).numpy()"," array([0.0513, 2.303], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd',"," loss=tf.keras.losses.SparseCategoricalCrossentropy())"," ```"," \"\"\"",""," def __init__("," self,"," from_logits=False,"," ignore_class=None,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"sparse_categorical_crossentropy\","," ):"," \"\"\"Initializes `SparseCategoricalCrossentropy` instance.",""," Args:"," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability"," distribution."," ignore_class: Optional integer. The ID of a class to be ignored"," during loss computation. This is useful, for example, in"," segmentation problems featuring a \"void\" class (commonly -1 or"," 255) in segmentation maps."," By default (`ignore_class=None`), all classes are considered."," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," Defaults to 'sparse_categorical_crossentropy'."," \"\"\""," super().__init__("," sparse_categorical_crossentropy,"," name=name,"," reduction=reduction,"," from_logits=from_logits,"," ignore_class=ignore_class,"," )","","","@keras_export(\"keras.losses.CosineSimilarity\")","class CosineSimilarity(LossFunctionWrapper):"," \"\"\"Computes the cosine similarity between labels and predictions.",""," Note that it is a number between -1 and 1. When it is a negative number"," between -1 and 0, 0 indicates orthogonality and values closer to -1"," indicate greater similarity. The values closer to 1 indicate greater"," dissimilarity. This makes it usable as a loss function in a setting"," where you try to maximize the proximity between predictions and targets."," If either `y_true` or `y_pred` is a zero vector, cosine similarity will be 0"," regardless of the proximity between predictions and targets.",""," `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [1., 1.]]"," \u003e\u003e\u003e y_pred = [[1., 0.], [1., 1.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e cosine_loss = tf.keras.losses.CosineSimilarity(axis=1)"," \u003e\u003e\u003e # l2_norm(y_true) = [[0., 1.], [1./1.414, 1./1.414]]"," \u003e\u003e\u003e # l2_norm(y_pred) = [[1., 0.], [1./1.414, 1./1.414]]"," \u003e\u003e\u003e # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]"," \u003e\u003e\u003e # loss = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))"," \u003e\u003e\u003e # = -((0. + 0.) + (0.5 + 0.5)) / 2"," \u003e\u003e\u003e cosine_loss(y_true, y_pred).numpy()"," -0.5",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e cosine_loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," -0.0999",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e cosine_loss = tf.keras.losses.CosineSimilarity(axis=1,"," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e cosine_loss(y_true, y_pred).numpy()"," -0.999",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e cosine_loss = tf.keras.losses.CosineSimilarity(axis=1,"," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e cosine_loss(y_true, y_pred).numpy()"," array([-0., -0.999], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd',"," loss=tf.keras.losses.CosineSimilarity(axis=1))"," ```",""," Args:"," axis: The axis along which the cosine similarity is computed"," (the features axis). Defaults to -1."," reduction: Type of `tf.keras.losses.Reduction` to apply to loss."," Default value is `AUTO`. `AUTO` indicates that the reduction option"," will be determined by the usage context. For almost all cases this"," defaults to `SUM_OVER_BATCH_SIZE`. When used under a"," `tf.distribute.Strategy`, except via `Model.compile()` and"," `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an"," error. Please see this custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to 'cosine_similarity'."," \"\"\"",""," def __init__("," self,"," axis=-1,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"cosine_similarity\","," ):"," super().__init__("," cosine_similarity, reduction=reduction, name=name, axis=axis"," )","","","@keras_export(\"keras.losses.Hinge\")","class Hinge(LossFunctionWrapper):"," \"\"\"Computes the hinge loss between `y_true` \u0026 `y_pred`.",""," `loss = maximum(1 - y_true * y_pred, 0)`",""," `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are"," provided we will convert them to -1 or 1.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.Hinge()"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 1.3",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e h(y_true, y_pred, sample_weight=[1, 0]).numpy()"," 0.55",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.Hinge("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 2.6",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.Hinge("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," array([1.1, 1.5], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.Hinge())"," ```"," \"\"\"",""," def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name=\"hinge\"):"," \"\"\"Initializes `Hinge` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to 'hinge'."," \"\"\""," super().__init__(hinge, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.SquaredHinge\")","class SquaredHinge(LossFunctionWrapper):"," \"\"\"Computes the squared hinge loss between `y_true` \u0026 `y_pred`.",""," `loss = square(maximum(1 - y_true * y_pred, 0))`",""," `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are"," provided we will convert them to -1 or 1.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.SquaredHinge()"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 1.86",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e h(y_true, y_pred, sample_weight=[1, 0]).numpy()"," 0.73",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.SquaredHinge("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 3.72",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.SquaredHinge("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," array([1.46, 2.26], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge())"," ```"," \"\"\"",""," def __init__("," self, reduction=losses_utils.ReductionV2.AUTO, name=\"squared_hinge\""," ):"," \"\"\"Initializes `SquaredHinge` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to 'squared_hinge'."," \"\"\""," super().__init__(squared_hinge, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.CategoricalHinge\")","class CategoricalHinge(LossFunctionWrapper):"," \"\"\"Computes the categorical hinge loss between `y_true` \u0026 `y_pred`.",""," `loss = maximum(neg - pos + 1, 0)`"," where `neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.CategoricalHinge()"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 1.4",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e h(y_true, y_pred, sample_weight=[1, 0]).numpy()"," 0.6",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.CategoricalHinge("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 2.8",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.CategoricalHinge("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," array([1.2, 1.6], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalHinge())"," ```"," \"\"\"",""," def __init__("," self, reduction=losses_utils.ReductionV2.AUTO, name=\"categorical_hinge\""," ):"," \"\"\"Initializes `CategoricalHinge` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," Defaults to 'categorical_hinge'."," \"\"\""," super().__init__(categorical_hinge, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.Poisson\")","class Poisson(LossFunctionWrapper):"," \"\"\"Computes the Poisson loss between `y_true` \u0026 `y_pred`.",""," `loss = y_pred - y_true * log(y_pred)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[1., 1.], [0., 0.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e p = tf.keras.losses.Poisson()"," \u003e\u003e\u003e p(y_true, y_pred).numpy()"," 0.5",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e p(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," 0.4",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e p = tf.keras.losses.Poisson("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e p(y_true, y_pred).numpy()"," 0.999",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e p = tf.keras.losses.Poisson("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e p(y_true, y_pred).numpy()"," array([0.999, 0.], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.Poisson())"," ```"," \"\"\"",""," def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name=\"poisson\"):"," \"\"\"Initializes `Poisson` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to 'poisson'."," \"\"\""," super().__init__(poisson, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.LogCosh\")","class LogCosh(LossFunctionWrapper):"," \"\"\"Computes the logarithm of the hyperbolic cosine of the prediction error.",""," `logcosh = log((exp(x) + exp(-x))/2)`,"," where x is the error `y_pred - y_true`.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [0., 0.]]"," \u003e\u003e\u003e y_pred = [[1., 1.], [0., 0.]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e l = tf.keras.losses.LogCosh()"," \u003e\u003e\u003e l(y_true, y_pred).numpy()"," 0.108",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e l(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," 0.087",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e l = tf.keras.losses.LogCosh("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e l(y_true, y_pred).numpy()"," 0.217",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e l = tf.keras.losses.LogCosh("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e l(y_true, y_pred).numpy()"," array([0.217, 0.], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.LogCosh())"," ```"," \"\"\"",""," def __init__("," self, reduction=losses_utils.ReductionV2.AUTO, name=\"log_cosh\""," ):"," \"\"\"Initializes `LogCosh` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to 'log_cosh'."," \"\"\""," super().__init__(log_cosh, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.KLDivergence\")","class KLDivergence(LossFunctionWrapper):"," \"\"\"Computes Kullback-Leibler divergence loss between `y_true` \u0026 `y_pred`.",""," `loss = y_true * log(y_true / y_pred)`",""," See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e kl = tf.keras.losses.KLDivergence()"," \u003e\u003e\u003e kl(y_true, y_pred).numpy()"," 0.458",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e kl(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()"," 0.366",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e kl = tf.keras.losses.KLDivergence("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e kl(y_true, y_pred).numpy()"," 0.916",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e kl = tf.keras.losses.KLDivergence("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e kl(y_true, y_pred).numpy()"," array([0.916, -3.08e-06], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.KLDivergence())"," ```"," \"\"\"",""," def __init__("," self, reduction=losses_utils.ReductionV2.AUTO, name=\"kl_divergence\""," ):"," \"\"\"Initializes `KLDivergence` instance.",""," Args:"," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance."," Defaults to 'kl_divergence'."," \"\"\""," super().__init__(kl_divergence, name=name, reduction=reduction)","","","@keras_export(\"keras.losses.Huber\")","class Huber(LossFunctionWrapper):"," \"\"\"Computes the Huber loss between `y_true` \u0026 `y_pred`.",""," For each value x in `error = y_true - y_pred`:",""," ```"," loss = 0.5 * x^2 if |x| \u003c= d"," loss = 0.5 * d^2 + d * (|x| - d) if |x| \u003e d"," ```"," where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e # Using 'auto'/'sum_over_batch_size' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.Huber()"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 0.155",""," \u003e\u003e\u003e # Calling with 'sample_weight'."," \u003e\u003e\u003e h(y_true, y_pred, sample_weight=[1, 0]).numpy()"," 0.09",""," \u003e\u003e\u003e # Using 'sum' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.Huber("," ... reduction=tf.keras.losses.Reduction.SUM)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," 0.31",""," \u003e\u003e\u003e # Using 'none' reduction type."," \u003e\u003e\u003e h = tf.keras.losses.Huber("," ... reduction=tf.keras.losses.Reduction.NONE)"," \u003e\u003e\u003e h(y_true, y_pred).numpy()"," array([0.18, 0.13], dtype=float32)",""," Usage with the `compile()` API:",""," ```python"," model.compile(optimizer='sgd', loss=tf.keras.losses.Huber())"," ```"," \"\"\"",""," def __init__("," self,"," delta=1.0,"," reduction=losses_utils.ReductionV2.AUTO,"," name=\"huber_loss\","," ):"," \"\"\"Initializes `Huber` instance.",""," Args:"," delta: A float, the point where the Huber loss function changes from"," a quadratic to linear."," reduction: Type of `tf.keras.losses.Reduction` to apply to"," loss. Default value is `AUTO`. `AUTO` indicates that the"," reduction ption will be determined by the usage context. For"," almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When"," used under a `tf.distribute.Strategy`, except via"," `Model.compile()` and `Model.fit()`, using `AUTO` or"," `SUM_OVER_BATCH_SIZE` will raise an error. Please see this"," custom training [tutorial]("," https://www.tensorflow.org/tutorials/distribute/custom_training)"," for more details."," name: Optional name for the instance. Defaults to 'huber_loss'."," \"\"\""," super().__init__(huber, name=name, reduction=reduction, delta=delta)","","","@keras_export("," \"keras.metrics.mean_squared_error\","," \"keras.metrics.mse\","," \"keras.metrics.MSE\","," \"keras.losses.mean_squared_error\","," \"keras.losses.mse\","," \"keras.losses.MSE\",",")","@tf.__internal__.dispatch.add_dispatch_support","def mean_squared_error(y_true, y_pred):"," \"\"\"Computes the mean squared error between labels and predictions.",""," After computing the squared distance between the inputs, the mean value over"," the last dimension is returned.",""," `loss = mean(square(y_true - y_pred), axis=-1)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.randint(0, 2, size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.mean_squared_error(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e assert np.array_equal("," ... loss.numpy(), np.mean(np.square(y_true - y_pred), axis=-1))",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Mean squared error values. shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)","","","def _ragged_tensor_apply_loss(loss_fn, y_true, y_pred, y_pred_extra_dim=False):"," \"\"\"Apply a loss function on a per batch basis.",""," Args:"," loss_fn: The loss function"," y_true: truth values (RaggedTensor)"," y_pred: predicted values (RaggedTensor)"," y_pred_extra_dim: whether y_pred has an additional dimension compared to"," y_true",""," Returns:"," Loss-function result. A dense tensor if the output has a single"," dimension (per-batch loss value); a ragged tensor otherwise."," \"\"\"",""," def rt_is_equiv_dense(rt):"," \"\"\"Returns true if this RaggedTensor has the same row_lengths across",""," all ragged dimensions and thus can be converted to a dense tensor"," without loss of information.",""," Args:"," rt: RaggedTensor."," \"\"\""," return tf.reduce_all("," ["," tf.equal("," tf.math.reduce_variance("," tf.cast(row_lens, backend.floatx())"," ),"," tf.constant([0.0]),"," )"," for row_lens in rt.nested_row_lengths()"," ]"," )",""," def _convert_to_dense(inputs):"," return tuple("," rt.to_tensor() if isinstance(rt, tf.RaggedTensor) else rt"," for rt in inputs"," )",""," def _call_loss(inputs, ragged_output):"," \"\"\"Adapt the result to ragged or dense tensor according to the expected",""," output type. This is done so that all the return values of the map"," operation have the same type."," \"\"\""," r = loss_fn(*inputs)"," if ragged_output and not isinstance(r, tf.RaggedTensor):"," r = tf.RaggedTensor.from_tensor(r)"," elif not ragged_output and isinstance(r, tf.RaggedTensor):"," r = r.to_tensor()"," return r",""," def _wrapper(inputs, ragged_output):"," _, y_pred = inputs"," if isinstance(y_pred, tf.RaggedTensor):"," return tf.cond("," rt_is_equiv_dense(y_pred),"," lambda: _call_loss(_convert_to_dense(inputs), ragged_output),"," lambda: _call_loss(inputs, ragged_output),"," )",""," return loss_fn(*inputs)",""," if not isinstance(y_true, tf.RaggedTensor):"," return loss_fn(y_true, y_pred.to_tensor())",""," lshape = y_pred.shape.as_list()[1:-1]"," if len(lshape) \u003e 0:"," spec = tf.RaggedTensorSpec(shape=lshape, dtype=y_pred.dtype)"," else:"," spec = tf.TensorSpec(shape=[], dtype=y_pred.dtype)",""," nested_splits_list = [rt.nested_row_splits for rt in (y_true, y_pred)]"," if y_pred_extra_dim:"," # The last dimension of a categorical prediction may be ragged or not."," rdims = [len(slist) for slist in nested_splits_list]"," if rdims[0] == rdims[1] - 1:"," nested_splits_list[1] = nested_splits_list[1][:-1]",""," map_fn = functools.partial(_wrapper, ragged_output=len(lshape) \u003e 1)",""," assertion_list = ragged_util.assert_splits_match(nested_splits_list)"," with tf.control_dependencies(assertion_list):"," return ragged_map_ops.map_fn(map_fn, elems=(y_true, y_pred), dtype=spec)","","","@dispatch.dispatch_for_types(mean_squared_error, tf.RaggedTensor)","def _ragged_tensor_mse(y_true, y_pred):"," \"\"\"Implements support for handling RaggedTensors.",""," Args:"," y_true: RaggedTensor truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: RaggedTensor predicted values."," shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Mean squared error values. shape = `[batch_size, d0, .. dN-1]`."," When the number of dimensions of the batch feature vector [d0, .. dN] is"," greater than one the return value is a RaggedTensor. Otherwise, a Dense"," tensor with dimensions [batch_size] is returned."," \"\"\""," return _ragged_tensor_apply_loss(mean_squared_error, y_true, y_pred)","","","@keras_export("," \"keras.metrics.mean_absolute_error\","," \"keras.metrics.mae\","," \"keras.metrics.MAE\","," \"keras.losses.mean_absolute_error\","," \"keras.losses.mae\","," \"keras.losses.MAE\",",")","@tf.__internal__.dispatch.add_dispatch_support","def mean_absolute_error(y_true, y_pred):"," \"\"\"Computes the mean absolute error between labels and predictions.",""," `loss = mean(abs(y_true - y_pred), axis=-1)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.randint(0, 2, size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.mean_absolute_error(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e assert np.array_equal("," ... loss.numpy(), np.mean(np.abs(y_true - y_pred), axis=-1))",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Mean absolute error values. shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," return backend.mean(tf.abs(y_pred - y_true), axis=-1)","","","@dispatch.dispatch_for_types(mean_absolute_error, tf.RaggedTensor)","def _ragged_tensor_mae(y_true, y_pred):"," \"\"\"RaggedTensor adapter for mean_absolute_error.\"\"\""," return _ragged_tensor_apply_loss(mean_absolute_error, y_true, y_pred)","","","@keras_export("," \"keras.metrics.mean_absolute_percentage_error\","," \"keras.metrics.mape\","," \"keras.metrics.MAPE\","," \"keras.losses.mean_absolute_percentage_error\","," \"keras.losses.mape\","," \"keras.losses.MAPE\",",")","@tf.__internal__.dispatch.add_dispatch_support","def mean_absolute_percentage_error(y_true, y_pred):"," \"\"\"Computes the mean absolute percentage error between `y_true` \u0026 `y_pred`.",""," `loss = 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.random(size=(2, 3))"," \u003e\u003e\u003e y_true = np.maximum(y_true, 1e-7) # Prevent division by zero"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.mean_absolute_percentage_error(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e assert np.array_equal("," ... loss.numpy(),"," ... 100. * np.mean(np.abs((y_true - y_pred) / y_true), axis=-1))",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Mean absolute percentage error values. shape = `[batch_size, d0, .."," dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," diff = tf.abs("," (y_true - y_pred) / backend.maximum(tf.abs(y_true), backend.epsilon())"," )"," return 100.0 * backend.mean(diff, axis=-1)","","","@dispatch.dispatch_for_types(mean_absolute_percentage_error, tf.RaggedTensor)","def _ragged_tensor_mape(y_true, y_pred):"," \"\"\"Support RaggedTensors.\"\"\""," return _ragged_tensor_apply_loss("," mean_absolute_percentage_error, y_true, y_pred"," )","","","@keras_export("," \"keras.metrics.mean_squared_logarithmic_error\","," \"keras.metrics.msle\","," \"keras.metrics.MSLE\","," \"keras.losses.mean_squared_logarithmic_error\","," \"keras.losses.msle\","," \"keras.losses.MSLE\",",")","@tf.__internal__.dispatch.add_dispatch_support","def mean_squared_logarithmic_error(y_true, y_pred):"," \"\"\"Computes the mean squared logarithmic error between `y_true` \u0026 `y_pred`.",""," `loss = mean(square(log(y_true + 1) - log(y_pred + 1)), axis=-1)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.randint(0, 2, size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.mean_squared_logarithmic_error(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e y_true = np.maximum(y_true, 1e-7)"," \u003e\u003e\u003e y_pred = np.maximum(y_pred, 1e-7)"," \u003e\u003e\u003e assert np.allclose("," ... loss.numpy(),"," ... np.mean("," ... np.square(np.log(y_true + 1.) - np.log(y_pred + 1.)), axis=-1))",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Mean squared logarithmic error values. shape = `[batch_size, d0, .."," dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," first_log = tf.math.log(backend.maximum(y_pred, backend.epsilon()) + 1.0)"," second_log = tf.math.log(backend.maximum(y_true, backend.epsilon()) + 1.0)"," return backend.mean("," tf.math.squared_difference(first_log, second_log), axis=-1"," )","","","@dispatch.dispatch_for_types(mean_squared_logarithmic_error, tf.RaggedTensor)","def _ragged_tensor_msle(y_true, y_pred):"," \"\"\"Implements support for handling RaggedTensors.\"\"\""," return _ragged_tensor_apply_loss("," mean_squared_logarithmic_error, y_true, y_pred"," )","","","def _maybe_convert_labels(y_true):"," \"\"\"Converts binary labels into -1/1.\"\"\""," are_zeros = tf.equal(y_true, 0)"," are_ones = tf.equal(y_true, 1)"," is_binary = tf.reduce_all(tf.logical_or(are_zeros, are_ones))",""," def _convert_binary_labels():"," # Convert the binary labels to -1 or 1."," return 2.0 * y_true - 1.0",""," updated_y_true = tf.__internal__.smart_cond.smart_cond("," is_binary, _convert_binary_labels, lambda: y_true"," )"," return updated_y_true","","","@keras_export(\"keras.metrics.squared_hinge\", \"keras.losses.squared_hinge\")","@tf.__internal__.dispatch.add_dispatch_support","def squared_hinge(y_true, y_pred):"," \"\"\"Computes the squared hinge loss between `y_true` \u0026 `y_pred`.",""," `loss = mean(square(maximum(1 - y_true * y_pred, 0)), axis=-1)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.choice([-1, 1], size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.squared_hinge(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e assert np.array_equal("," ... loss.numpy(),"," ... np.mean(np.square(np.maximum(1. - y_true * y_pred, 0.)), axis=-1))",""," Args:"," y_true: The ground truth values. `y_true` values are expected to be -1"," or 1. If binary (0 or 1) labels are provided we will convert them to"," -1 or 1. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Squared hinge loss values. shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," y_true = _maybe_convert_labels(y_true)"," return backend.mean("," tf.square(tf.maximum(1.0 - y_true * y_pred, 0.0)), axis=-1"," )","","","@keras_export(\"keras.metrics.hinge\", \"keras.losses.hinge\")","@tf.__internal__.dispatch.add_dispatch_support","def hinge(y_true, y_pred):"," \"\"\"Computes the hinge loss between `y_true` \u0026 `y_pred`.",""," `loss = mean(maximum(1 - y_true * y_pred, 0), axis=-1)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.choice([-1, 1], size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.hinge(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e assert np.array_equal("," ... loss.numpy(),"," ... np.mean(np.maximum(1. - y_true * y_pred, 0.), axis=-1))",""," Args:"," y_true: The ground truth values. `y_true` values are expected to be -1"," or 1. If binary (0 or 1) labels are provided we will convert them to"," -1 or 1. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Hinge loss values. shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," y_true = _maybe_convert_labels(y_true)"," return backend.mean(tf.maximum(1.0 - y_true * y_pred, 0.0), axis=-1)","","","@keras_export(\"keras.losses.categorical_hinge\")","@tf.__internal__.dispatch.add_dispatch_support","def categorical_hinge(y_true, y_pred):"," \"\"\"Computes the categorical hinge loss between `y_true` \u0026 `y_pred`.",""," `loss = maximum(neg - pos + 1, 0)`"," where `neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.randint(0, 3, size=(2,))"," \u003e\u003e\u003e y_true = tf.keras.utils.to_categorical(y_true, num_classes=3)"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.categorical_hinge(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e pos = np.sum(y_true * y_pred, axis=-1)"," \u003e\u003e\u003e neg = np.amax((1. - y_true) * y_pred, axis=-1)"," \u003e\u003e\u003e assert np.array_equal(loss.numpy(), np.maximum(0., neg - pos + 1.))",""," Args:"," y_true: The ground truth values. `y_true` values are expected to be"," either `{-1, +1}` or `{0, 1}` (i.e. a one-hot-encoded tensor)."," y_pred: The predicted values.",""," Returns:"," Categorical hinge loss values."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," pos = tf.reduce_sum(y_true * y_pred, axis=-1)"," neg = tf.reduce_max((1.0 - y_true) * y_pred, axis=-1)"," zero = tf.cast(0.0, y_pred.dtype)"," return tf.maximum(neg - pos + 1.0, zero)","","","@keras_export(\"keras.losses.huber\", v1=[])","@tf.__internal__.dispatch.add_dispatch_support","def huber(y_true, y_pred, delta=1.0):"," \"\"\"Computes Huber loss value.",""," For each value x in `error = y_true - y_pred`:",""," ```"," loss = 0.5 * x^2 if |x| \u003c= d"," loss = d * |x| - 0.5 * d^2 if |x| \u003e d"," ```"," where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss",""," Args:"," y_true: tensor of true targets."," y_pred: tensor of predicted targets."," delta: A float, the point where the Huber loss function changes from a"," quadratic to linear.",""," Returns:"," Tensor with one scalar loss entry per sample."," \"\"\""," y_pred = tf.cast(y_pred, dtype=backend.floatx())"," y_true = tf.cast(y_true, dtype=backend.floatx())"," delta = tf.cast(delta, dtype=backend.floatx())"," error = tf.subtract(y_pred, y_true)"," abs_error = tf.abs(error)"," half = tf.convert_to_tensor(0.5, dtype=abs_error.dtype)"," return backend.mean("," tf.where("," abs_error \u003c= delta,"," half * tf.square(error),"," delta * abs_error - half * tf.square(delta),"," ),"," axis=-1,"," )","","","@keras_export("," \"keras.losses.log_cosh\","," \"keras.losses.logcosh\","," \"keras.metrics.log_cosh\","," \"keras.metrics.logcosh\",",")","@tf.__internal__.dispatch.add_dispatch_support","def log_cosh(y_true, y_pred):"," \"\"\"Logarithm of the hyperbolic cosine of the prediction error.",""," `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and"," to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly"," like the mean squared error, but will not be so strongly affected by the"," occasional wildly incorrect prediction.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.random(size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.logcosh(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e x = y_pred - y_true"," \u003e\u003e\u003e assert np.allclose("," ... loss.numpy(),"," ... np.mean(x + np.log(np.exp(-2. * x) + 1.) - tf.math.log(2.),"," ... axis=-1),"," ... atol=1e-5)",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Logcosh error values. shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)",""," def _logcosh(x):"," return ("," x + tf.math.softplus(-2.0 * x) - tf.cast(tf.math.log(2.0), x.dtype)"," )",""," return backend.mean(_logcosh(y_pred - y_true), axis=-1)","","","@keras_export("," \"keras.metrics.categorical_crossentropy\","," \"keras.losses.categorical_crossentropy\",",")","@tf.__internal__.dispatch.add_dispatch_support","def categorical_crossentropy("," y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1","):"," \"\"\"Computes the categorical crossentropy loss.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1, 0], [0, 0, 1]]"," \u003e\u003e\u003e y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]"," \u003e\u003e\u003e loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e loss.numpy()"," array([0.0513, 2.303], dtype=float32)",""," Args:"," y_true: Tensor of one-hot true targets."," y_pred: Tensor of predicted targets."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in [0, 1]. If \u003e `0` then smooth the labels. For"," example, if `0.1`, use `0.1 / num_classes` for non-target labels"," and `0.9 + 0.1 / num_classes` for target labels."," axis: Defaults to -1. The dimension along which the entropy is"," computed.",""," Returns:"," Categorical crossentropy loss value."," \"\"\""," if isinstance(axis, bool):"," raise ValueError("," \"`axis` must be of type `int`. \""," f\"Received: axis={axis} of type {type(axis)}\""," )"," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," label_smoothing = tf.convert_to_tensor(label_smoothing, dtype=y_pred.dtype)",""," if y_pred.shape[-1] == 1:"," warnings.warn("," \"In loss categorical_crossentropy, expected \""," \"y_pred.shape to be (batch_size, num_classes) \""," f\"with num_classes \u003e 1. Received: y_pred.shape={y_pred.shape}. \""," \"Consider using 'binary_crossentropy' if you only have 2 classes.\","," SyntaxWarning,"," stacklevel=2,"," )",""," def _smooth_labels():"," num_classes = tf.cast(tf.shape(y_true)[axis], y_pred.dtype)"," return y_true * (1.0 - label_smoothing) + ("," label_smoothing / num_classes"," )",""," y_true = tf.__internal__.smart_cond.smart_cond("," label_smoothing, _smooth_labels, lambda: y_true"," )",""," return backend.categorical_crossentropy("," y_true, y_pred, from_logits=from_logits, axis=axis"," )","","","@dispatch.dispatch_for_types(categorical_crossentropy, tf.RaggedTensor)","def _ragged_tensor_categorical_crossentropy("," y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1","):"," \"\"\"Implements support for handling RaggedTensors.",""," Args:"," y_true: Tensor of one-hot true targets."," y_pred: Tensor of predicted targets."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in [0, 1]. If \u003e `0` then smooth the labels. For"," example, if `0.1`, use `0.1 / num_classes` for non-target labels"," and `0.9 + 0.1 / num_classes` for target labels."," axis: The axis along which to compute crossentropy (the features axis)."," Defaults to -1.",""," Returns:"," Categorical crossentropy loss value.",""," Expected shape: (batch, sequence_len, n_classes) with sequence_len"," being variable per batch."," Return shape: (batch, sequence_len).",""," When used by CategoricalCrossentropy() with the default reduction"," (SUM_OVER_BATCH_SIZE), the reduction averages the loss over the"," number of elements independent of the batch. E.g. if the RaggedTensor"," has 2 batches with [2, 1] values respectively the resulting loss is"," the sum of the individual loss values divided by 3."," \"\"\""," fn = functools.partial("," categorical_crossentropy,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," return _ragged_tensor_apply_loss(fn, y_true, y_pred)","","","@keras_export("," \"keras.metrics.categorical_focal_crossentropy\","," \"keras.losses.categorical_focal_crossentropy\",",")","@tf.__internal__.dispatch.add_dispatch_support","def categorical_focal_crossentropy("," y_true,"," y_pred,"," alpha=0.25,"," gamma=2.0,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,","):"," \"\"\"Computes the categorical focal crossentropy loss.",""," Standalone usage:"," \u003e\u003e\u003e y_true = [[0, 1, 0], [0, 0, 1]]"," \u003e\u003e\u003e y_pred = [[0.05, 0.9, 0.05], [0.1, 0.85, 0.05]]"," \u003e\u003e\u003e loss = tf.keras.losses.categorical_focal_crossentropy(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e loss.numpy()"," array([2.63401289e-04, 6.75912094e-01], dtype=float32)",""," Args:"," y_true: Tensor of one-hot true targets."," y_pred: Tensor of predicted targets."," alpha: A weight balancing factor for all classes, default is `0.25` as"," mentioned in the reference. It can be a list of floats or a scalar."," In the multi-class case, alpha may be set by inverse class"," frequency by using `compute_class_weight` from `sklearn.utils`."," gamma: A focusing parameter, default is `2.0` as mentioned in the"," reference. It helps to gradually reduce the importance given to"," simple examples in a smooth manner. When `gamma` = 0, there is"," no focal effect on the categorical crossentropy."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability"," distribution."," label_smoothing: Float in [0, 1]. If \u003e `0` then smooth the labels. For"," example, if `0.1`, use `0.1 / num_classes` for non-target labels"," and `0.9 + 0.1 / num_classes` for target labels."," axis: Defaults to -1. The dimension along which the entropy is"," computed.",""," Returns:"," Categorical focal crossentropy loss value."," \"\"\""," if isinstance(axis, bool):"," raise ValueError("," \"`axis` must be of type `int`. \""," f\"Received: axis={axis} of type {type(axis)}\""," )"," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," label_smoothing = tf.convert_to_tensor(label_smoothing, dtype=y_pred.dtype)",""," if y_pred.shape[-1] == 1:"," warnings.warn("," \"In loss categorical_focal_crossentropy, expected \""," \"y_pred.shape to be (batch_size, num_classes) \""," f\"with num_classes \u003e 1. Received: y_pred.shape={y_pred.shape}. \""," \"Consider using 'binary_crossentropy' if you only have 2 classes.\","," SyntaxWarning,"," stacklevel=2,"," )",""," def _smooth_labels():"," num_classes = tf.cast(tf.shape(y_true)[-1], y_pred.dtype)"," return y_true * (1.0 - label_smoothing) + ("," label_smoothing / num_classes"," )",""," y_true = tf.__internal__.smart_cond.smart_cond("," label_smoothing, _smooth_labels, lambda: y_true"," )",""," return backend.categorical_focal_crossentropy("," target=y_true,"," output=y_pred,"," alpha=alpha,"," gamma=gamma,"," from_logits=from_logits,"," axis=axis,"," )","","","@dispatch.dispatch_for_types(categorical_focal_crossentropy, tf.RaggedTensor)","def _ragged_tensor_categorical_focal_crossentropy("," y_true,"," y_pred,"," alpha=0.25,"," gamma=2.0,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,","):"," \"\"\"Implements support for handling RaggedTensors.",""," Expected shape: (batch, sequence_len, n_classes) with sequence_len"," being variable per batch."," Return shape: (batch, sequence_len)."," When used by CategoricalFocalCrossentropy() with the default reduction"," (SUM_OVER_BATCH_SIZE), the reduction averages the loss over the"," number of elements independent of the batch. E.g. if the RaggedTensor"," has 2 batches with [2, 1] values respectively the resulting loss is"," the sum of the individual loss values divided by 3.",""," Args:"," alpha: A weight balancing factor for all classes, default is `0.25` as"," mentioned in the reference. It can be a list of floats or a scalar."," In the multi-class case, alpha may be set by inverse class"," frequency by using `compute_class_weight` from `sklearn.utils`."," gamma: A focusing parameter, default is `2.0` as mentioned in the"," reference. It helps to gradually reduce the importance given to"," simple examples in a smooth manner. When `gamma` = 0, there is"," no focal effect on the categorical crossentropy."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in [0, 1]. If \u003e `0` then smooth the labels. For"," example, if `0.1`, use `0.1 / num_classes` for non-target labels"," and `0.9 + 0.1 / num_classes` for target labels."," axis: Defaults to -1. The dimension along which the entropy is"," computed.",""," Returns:"," Categorical focal crossentropy loss value."," \"\"\""," fn = functools.partial("," categorical_focal_crossentropy,"," alpha=alpha,"," gamma=gamma,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," return _ragged_tensor_apply_loss(fn, y_true, y_pred)","","","@keras_export("," \"keras.metrics.sparse_categorical_crossentropy\","," \"keras.losses.sparse_categorical_crossentropy\",",")","@tf.__internal__.dispatch.add_dispatch_support","def sparse_categorical_crossentropy("," y_true, y_pred, from_logits=False, axis=-1, ignore_class=None","):"," \"\"\"Computes the sparse categorical crossentropy loss.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [1, 2]"," \u003e\u003e\u003e y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]"," \u003e\u003e\u003e loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e loss.numpy()"," array([0.0513, 2.303], dtype=float32)",""," \u003e\u003e\u003e y_true = [[[ 0, 2],"," ... [-1, -1]],"," ... [[ 0, 2],"," ... [-1, -1]]]"," \u003e\u003e\u003e y_pred = [[[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]],"," ... [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]],"," ... [[[1.0, 0.0, 0.0], [0.0, 0.5, 0.5]],"," ... [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]]]"," \u003e\u003e\u003e loss = tf.keras.losses.sparse_categorical_crossentropy("," ... y_true, y_pred, ignore_class=-1)"," \u003e\u003e\u003e loss.numpy()"," array([[[2.3841855e-07, 2.3841855e-07],"," [0.0000000e+00, 0.0000000e+00]],"," [[2.3841855e-07, 6.9314730e-01],"," [0.0000000e+00, 0.0000000e+00]]], dtype=float32)",""," Args:"," y_true: Ground truth values."," y_pred: The predicted values."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," axis: Defaults to -1. The dimension along which the entropy is"," computed."," ignore_class: Optional integer. The ID of a class to be ignored during"," loss computation. This is useful, for example, in segmentation"," problems featuring a \"void\" class (commonly -1 or 255) in"," segmentation maps. By default (`ignore_class=None`), all classes are"," considered.",""," Returns:"," Sparse categorical crossentropy loss value."," \"\"\""," return backend.sparse_categorical_crossentropy("," y_true,"," y_pred,"," from_logits=from_logits,"," ignore_class=ignore_class,"," axis=axis,"," )","","","@dispatch.dispatch_for_types(sparse_categorical_crossentropy, tf.RaggedTensor)","def _ragged_tensor_sparse_categorical_crossentropy("," y_true, y_pred, from_logits=False, axis=-1, ignore_class=None","):"," \"\"\"Implements support for handling RaggedTensors.",""," Expected y_pred shape: (batch, sequence_len, n_classes) with sequence_len"," being variable per batch."," Return shape: (batch, sequence_len).",""," When used by SparseCategoricalCrossentropy() with the default reduction"," (SUM_OVER_BATCH_SIZE), the reduction averages the loss over the"," number of elements independent of the batch. E.g. if the RaggedTensor"," has 2 batches with [2, 1] values respectively, the resulting loss is"," the sum of the individual loss values divided by 3."," \"\"\""," fn = functools.partial("," sparse_categorical_crossentropy,"," from_logits=from_logits,"," ignore_class=ignore_class,"," axis=axis,"," )"," return _ragged_tensor_apply_loss(fn, y_true, y_pred, y_pred_extra_dim=True)","","","@keras_export("," \"keras.metrics.binary_crossentropy\", \"keras.losses.binary_crossentropy\"",")","@tf.__internal__.dispatch.add_dispatch_support","def binary_crossentropy("," y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1","):"," \"\"\"Computes the binary crossentropy loss.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e loss.numpy()"," array([0.916 , 0.714], dtype=float32)",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in [0, 1]. If \u003e `0` then smooth the labels by"," squeezing them towards 0.5 That is, using"," `1. - 0.5 * label_smoothing` for the target class and"," `0.5 * label_smoothing` for the non-target class."," axis: The axis along which the mean is computed. Defaults to -1.",""," Returns:"," Binary crossentropy loss value. shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," label_smoothing = tf.convert_to_tensor(label_smoothing, dtype=y_pred.dtype)",""," def _smooth_labels():"," return y_true * (1.0 - label_smoothing) + 0.5 * label_smoothing",""," y_true = tf.__internal__.smart_cond.smart_cond("," label_smoothing, _smooth_labels, lambda: y_true"," )",""," return backend.mean("," backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),"," axis=axis,"," )","","","@dispatch.dispatch_for_types(binary_crossentropy, tf.RaggedTensor)","def _ragged_tensor_binary_crossentropy("," y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1","):"," \"\"\"Implements support for handling RaggedTensors.",""," Args:"," y_true: Tensor of one-hot true targets."," y_pred: Tensor of predicted targets."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in [0, 1]. If \u003e `0` then smooth the labels. For"," example, if `0.1`, use `0.1 / num_classes` for non-target labels"," and `0.9 + 0.1 / num_classes` for target labels."," axis: Axis along which to compute crossentropy.",""," Returns:"," Binary crossentropy loss value.",""," Expected shape: (batch, sequence_len) with sequence_len being variable"," per batch."," Return shape: (batch,); returns the per batch mean of the loss values.",""," When used by BinaryCrossentropy() with the default reduction"," (SUM_OVER_BATCH_SIZE), the reduction averages the per batch losses over"," the number of batches."," \"\"\""," fn = functools.partial("," binary_crossentropy,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," return _ragged_tensor_apply_loss(fn, y_true, y_pred)","","","@keras_export("," \"keras.metrics.binary_focal_crossentropy\","," \"keras.losses.binary_focal_crossentropy\",",")","@tf.__internal__.dispatch.add_dispatch_support","def binary_focal_crossentropy("," y_true,"," y_pred,"," apply_class_balancing=False,"," alpha=0.25,"," gamma=2.0,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,","):"," \"\"\"Computes the binary focal crossentropy loss.",""," According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it"," helps to apply a focal factor to down-weight easy examples and focus more on"," hard examples. By default, the focal tensor is computed as follows:",""," `focal_factor = (1 - output)**gamma` for class 1"," `focal_factor = output**gamma` for class 0"," where `gamma` is a focusing parameter. When `gamma` = 0, there is no focal"," effect on the binary crossentropy loss.",""," If `apply_class_balancing == True`, this function also takes into account a"," weight balancing factor for the binary classes 0 and 1 as follows:",""," `weight = alpha` for class 1 (`target == 1`)"," `weight = 1 - alpha` for class 0"," where `alpha` is a float in the range of `[0, 1]`.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0, 1], [0, 0]]"," \u003e\u003e\u003e y_pred = [[0.6, 0.4], [0.4, 0.6]]"," \u003e\u003e\u003e loss = tf.keras.losses.binary_focal_crossentropy(y_true, y_pred,"," ... gamma=2)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e loss.numpy()"," array([0.330, 0.206], dtype=float32)",""," Args:"," y_true: Ground truth values, of shape `(batch_size, d0, .. dN)`."," y_pred: The predicted values, of shape `(batch_size, d0, .. dN)`."," apply_class_balancing: A bool, whether to apply weight balancing on the"," binary classes 0 and 1."," alpha: A weight balancing factor for class 1, default is `0.25` as"," mentioned in the reference. The weight for class 0 is `1.0 - alpha`."," gamma: A focusing parameter, default is `2.0` as mentioned in the"," reference."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in `[0, 1]`. If higher than 0 then smooth the"," labels by squeezing them towards `0.5`, i.e., using `1. - 0.5 *"," label_smoothing` for the target class and `0.5 * label_smoothing`"," for the non-target class."," axis: The axis along which the mean is computed. Defaults to `-1`.",""," Returns:"," Binary focal crossentropy loss value."," shape = `[batch_size, d0, .. dN-1]`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," label_smoothing = tf.convert_to_tensor(label_smoothing, dtype=y_pred.dtype)",""," def _smooth_labels():"," return y_true * (1.0 - label_smoothing) + 0.5 * label_smoothing",""," y_true = tf.__internal__.smart_cond.smart_cond("," label_smoothing, _smooth_labels, lambda: y_true"," )",""," return backend.mean("," backend.binary_focal_crossentropy("," target=y_true,"," output=y_pred,"," apply_class_balancing=apply_class_balancing,"," alpha=alpha,"," gamma=gamma,"," from_logits=from_logits,"," ),"," axis=axis,"," )","","","@dispatch.dispatch_for_types(binary_focal_crossentropy, tf.RaggedTensor)","def _ragged_tensor_binary_focal_crossentropy("," y_true,"," y_pred,"," apply_class_balancing=False,"," alpha=0.25,"," gamma=2.0,"," from_logits=False,"," label_smoothing=0.0,"," axis=-1,","):"," \"\"\"Implements support for handling RaggedTensors.",""," Expected shape: `(batch, sequence_len)` with sequence_len being variable per"," batch."," Return shape: `(batch,)`; returns the per batch mean of the loss values.",""," When used by BinaryFocalCrossentropy() with the default reduction"," (SUM_OVER_BATCH_SIZE), the reduction averages the per batch losses over"," the number of batches.",""," Args:"," y_true: Tensor of one-hot true targets."," y_pred: Tensor of predicted targets."," apply_class_balancing: A bool, whether to apply weight balancing on the"," binary classes 0 and 1."," alpha: A weight balancing factor for class 1, default is `0.25` as"," mentioned in the reference [Lin et al., 2018]("," https://arxiv.org/pdf/1708.02002.pdf). The weight for class 0 is"," `1.0 - alpha`."," gamma: A focusing parameter, default is `2.0` as mentioned in the"," reference."," from_logits: Whether `y_pred` is expected to be a logits tensor. By"," default, we assume that `y_pred` encodes a probability distribution."," label_smoothing: Float in `[0, 1]`. If \u003e `0` then smooth the labels. For"," example, if `0.1`, use `0.1 / num_classes` for non-target labels"," and `0.9 + 0.1 / num_classes` for target labels."," axis: Axis along which to compute crossentropy.",""," Returns:"," Binary focal crossentropy loss value."," \"\"\""," fn = functools.partial("," binary_focal_crossentropy,"," apply_class_balancing=apply_class_balancing,"," alpha=alpha,"," gamma=gamma,"," from_logits=from_logits,"," label_smoothing=label_smoothing,"," axis=axis,"," )"," return _ragged_tensor_apply_loss(fn, y_true, y_pred)","","","@keras_export("," \"keras.metrics.kl_divergence\","," \"keras.metrics.kullback_leibler_divergence\","," \"keras.metrics.kld\","," \"keras.metrics.KLD\","," \"keras.losses.kl_divergence\","," \"keras.losses.kullback_leibler_divergence\","," \"keras.losses.kld\","," \"keras.losses.KLD\",",")","@tf.__internal__.dispatch.add_dispatch_support","def kl_divergence(y_true, y_pred):"," \"\"\"Computes Kullback-Leibler divergence loss between `y_true` \u0026 `y_pred`.",""," `loss = y_true * log(y_true / y_pred)`",""," See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.randint(0, 2, size=(2, 3)).astype(np.float64)"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.kullback_leibler_divergence(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e y_true = tf.keras.backend.clip(y_true, 1e-7, 1)"," \u003e\u003e\u003e y_pred = tf.keras.backend.clip(y_pred, 1e-7, 1)"," \u003e\u003e\u003e assert np.array_equal("," ... loss.numpy(), np.sum(y_true * np.log(y_true / y_pred), axis=-1))",""," Args:"," y_true: Tensor of true targets."," y_pred: Tensor of predicted targets.",""," Returns:"," A `Tensor` with loss.",""," Raises:"," TypeError: If `y_true` cannot be cast to the `y_pred.dtype`."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," y_true = backend.clip(y_true, backend.epsilon(), 1)"," y_pred = backend.clip(y_pred, backend.epsilon(), 1)"," return tf.reduce_sum(y_true * tf.math.log(y_true / y_pred), axis=-1)","","","@keras_export(\"keras.metrics.poisson\", \"keras.losses.poisson\")","@tf.__internal__.dispatch.add_dispatch_support","def poisson(y_true, y_pred):"," \"\"\"Computes the Poisson loss between y_true and y_pred.",""," The Poisson loss is the mean of the elements of the `Tensor`"," `y_pred - y_true * log(y_pred)`.",""," Standalone usage:",""," \u003e\u003e\u003e y_true = np.random.randint(0, 2, size=(2, 3))"," \u003e\u003e\u003e y_pred = np.random.random(size=(2, 3))"," \u003e\u003e\u003e loss = tf.keras.losses.poisson(y_true, y_pred)"," \u003e\u003e\u003e assert loss.shape == (2,)"," \u003e\u003e\u003e y_pred = y_pred + 1e-7"," \u003e\u003e\u003e assert np.allclose("," ... loss.numpy(), np.mean(y_pred - y_true * np.log(y_pred), axis=-1),"," ... atol=1e-5)",""," Args:"," y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`."," y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.",""," Returns:"," Poisson loss value. shape = `[batch_size, d0, .. dN-1]`.",""," Raises:"," InvalidArgumentError: If `y_true` and `y_pred` have incompatible shapes."," \"\"\""," y_pred = tf.convert_to_tensor(y_pred)"," y_true = tf.cast(y_true, y_pred.dtype)"," return backend.mean("," y_pred - y_true * tf.math.log(y_pred + backend.epsilon()), axis=-1"," )","","","@keras_export("," \"keras.losses.cosine_similarity\","," v1=["," \"keras.metrics.cosine_proximity\","," \"keras.metrics.cosine\","," \"keras.losses.cosine_proximity\","," \"keras.losses.cosine\","," \"keras.losses.cosine_similarity\","," ],",")","@tf.__internal__.dispatch.add_dispatch_support","def cosine_similarity(y_true, y_pred, axis=-1):"," \"\"\"Computes the cosine similarity between labels and predictions.",""," Note that it is a number between -1 and 1. When it is a negative number"," between -1 and 0, 0 indicates orthogonality and values closer to -1"," indicate greater similarity. The values closer to 1 indicate greater"," dissimilarity. This makes it usable as a loss function in a setting"," where you try to maximize the proximity between predictions and"," targets. If either `y_true` or `y_pred` is a zero vector, cosine"," similarity will be 0 regardless of the proximity between predictions"," and targets.",""," `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`",""," Standalone usage:",""," \u003e\u003e\u003e y_true = [[0., 1.], [1., 1.], [1., 1.]]"," \u003e\u003e\u003e y_pred = [[1., 0.], [1., 1.], [-1., -1.]]"," \u003e\u003e\u003e loss = tf.keras.losses.cosine_similarity(y_true, y_pred, axis=1)"," \u003e\u003e\u003e loss.numpy()"," array([-0., -0.999, 0.999], dtype=float32)",""," Args:"," y_true: Tensor of true targets."," y_pred: Tensor of predicted targets."," axis: Axis along which to determine similarity.",""," Returns:"," Cosine similarity tensor."," \"\"\""," y_true = tf.linalg.l2_normalize(y_true, axis=axis)"," y_pred = tf.linalg.l2_normalize(y_pred, axis=axis)"," return -tf.reduce_sum(y_true * y_pred, axis=axis)","","","# Aliases.","","bce = BCE = binary_crossentropy","mse = MSE = mean_squared_error","mae = MAE = mean_absolute_error","mape = MAPE = mean_absolute_percentage_error","msle = MSLE = mean_squared_logarithmic_error","kld = KLD = kullback_leibler_divergence = kl_divergence","logcosh = log_cosh","huber_loss = huber","","","def is_categorical_crossentropy(loss):"," result = ("," isinstance(loss, CategoricalCrossentropy)"," or ("," isinstance(loss, LossFunctionWrapper)"," and loss.fn == categorical_crossentropy"," )"," or ("," hasattr(loss, \"__name__\")"," and loss.__name__ == \"categorical_crossentropy\""," )"," or (loss == \"categorical_crossentropy\")"," )"," return result","","","@keras_export(\"keras.losses.serialize\")","def serialize(loss, use_legacy_format=False):"," \"\"\"Serializes loss function or `Loss` instance.",""," Args:"," loss: A TF-Keras `Loss` instance or a loss function."," use_legacy_format: Boolean, whether to use the legacy serialization"," format. Defaults to `False`.",""," Returns:"," Loss configuration dictionary."," \"\"\""," if loss is None:"," return None"," if not isinstance(loss, Loss):"," warnings.warn("," \"The `keras.losses.serialize()` API should only be used for \""," \"objects of type `keras.losses.Loss`. Found an instance of type \""," f\"{type(loss)}, which may lead to improper serialization.\""," )"," if use_legacy_format:"," return legacy_serialization.serialize_keras_object(loss)"," return serialize_keras_object(loss)","","","@keras_export(\"keras.losses.deserialize\")","def deserialize(name, custom_objects=None, use_legacy_format=False):"," \"\"\"Deserializes a serialized loss class/function instance.",""," Args:"," name: Loss configuration."," custom_objects: Optional dictionary mapping names (strings) to custom"," objects (classes and functions) to be considered during"," deserialization."," use_legacy_format: Boolean, whether to use the legacy serialization"," format. Defaults to `False`.",""," Returns:"," A TF-Keras `Loss` instance or a loss function."," \"\"\""," if use_legacy_format:"," return legacy_serialization.deserialize_keras_object("," name,"," module_objects=globals(),"," custom_objects=custom_objects,"," printable_module_name=\"loss function\","," )"," return deserialize_keras_object("," name,"," module_objects=globals(),"," custom_objects=custom_objects,"," printable_module_name=\"loss function\","," )","","","@keras_export(\"keras.losses.get\")","def get(identifier):"," \"\"\"Retrieves a TF-Keras loss as a `function`/`Loss` class instance.",""," The `identifier` may be the string name of a loss function or `Loss` class.",""," \u003e\u003e\u003e loss = tf.keras.losses.get(\"categorical_crossentropy\")"," \u003e\u003e\u003e type(loss)"," \u003cclass 'function'\u003e"," \u003e\u003e\u003e loss = tf.keras.losses.get(\"CategoricalCrossentropy\")"," \u003e\u003e\u003e type(loss)"," \u003cclass '...keras.losses.CategoricalCrossentropy'\u003e",""," You can also specify `config` of the loss to this function by passing dict"," containing `class_name` and `config` as an identifier. Also note that the"," `class_name` must map to a `Loss` class",""," \u003e\u003e\u003e identifier = {\"class_name\": \"CategoricalCrossentropy\","," ... \"config\": {\"from_logits\": True}}"," \u003e\u003e\u003e loss = tf.keras.losses.get(identifier)"," \u003e\u003e\u003e type(loss)"," \u003cclass '...keras.losses.CategoricalCrossentropy'\u003e",""," Args:"," identifier: A loss identifier. One of None or string name of a loss"," function/class or loss configuration dictionary or a loss function"," or a loss class instance.",""," Returns:"," A TF-Keras loss as a `function`/ `Loss` class instance.",""," Raises:"," ValueError: If `identifier` cannot be interpreted."," \"\"\""," if identifier is None:"," return None"," if isinstance(identifier, str):"," identifier = str(identifier)"," use_legacy_format = \"module\" not in identifier"," return deserialize(identifier, use_legacy_format=use_legacy_format)"," if isinstance(identifier, dict):"," return deserialize(identifier)"," if callable(identifier):"," return identifier"," raise ValueError("," f\"Could not interpret loss function identifier: {identifier}\""," )","","","LABEL_DTYPES_FOR_LOSSES = {"," tf.compat.v1.losses.sparse_softmax_cross_entropy: \"int32\","," sparse_categorical_crossentropy: 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style="padding-right:16px">975</div><div data-line-number="976" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">976</div><div data-line-number="977" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">977</div><div data-line-number="978" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">978</div><div data-line-number="979" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">979</div><div data-line-number="980" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">980</div><div data-line-number="981" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">981</div><div data-line-number="982" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">982</div><div data-line-number="983" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">983</div><div data-line-number="984" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">984</div><div data-line-number="985" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">985</div><div data-line-number="986" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">986</div><div data-line-number="987" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">987</div><div data-line-number="988" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">988</div><div data-line-number="989" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">989</div><div data-line-number="990" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">990</div><div data-line-number="991" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">991</div><div data-line-number="992" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">992</div><div data-line-number="993" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">993</div><div data-line-number="994" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">994</div><div data-line-number="995" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">995</div><div data-line-number="996" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">996</div><div data-line-number="997" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">997</div><div data-line-number="998" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">998</div><div data-line-number="999" class="child-of-line-933 react-line-number react-code-text" style="padding-right:16px">999</div><div data-line-number="1000" class="child-of-line-933 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"> </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"><span class="pl-s">"""Built-in loss functions."""</span></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"> </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"> </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">abc</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">functools</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">warnings</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">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="LC24" class="react-file-line html-div" data-testid="code-cell" data-line-number="24" style="position:relative"> </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">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="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">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="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">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> <span class="pl-k">as</span> <span class="pl-s1">legacy_serialization</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">tf_keras</span>.<span class="pl-s1">saving</span>.<span class="pl-s1">serialization_lib</span> <span class="pl-k">import</span> <span class="pl-s1">deserialize_keras_object</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">tf_keras</span>.<span class="pl-s1">saving</span>.<span class="pl-s1">serialization_lib</span> <span class="pl-k">import</span> <span class="pl-s1">serialize_keras_object</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">tf_keras</span>.<span class="pl-s1">utils</span> <span class="pl-k">import</span> <span class="pl-s1">losses_utils</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"><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="LC32" class="react-file-line html-div" data-testid="code-cell" data-line-number="32" style="position:relative"> </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-c"># isort: off</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">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">ops</span>.<span class="pl-s1">ragged</span> <span class="pl-k">import</span> <span class="pl-s1">ragged_map_ops</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">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">ops</span>.<span class="pl-s1">ragged</span> <span class="pl-k">import</span> <span class="pl-s1">ragged_util</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">tensorflow</span>.<span class="pl-s1">python</span>.<span class="pl-s1">util</span> <span class="pl-k">import</span> <span class="pl-s1">dispatch</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">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="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">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="LC39" class="react-file-line html-div" data-testid="code-cell" data-line-number="39" style="position:relative"> </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"> </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-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.Loss"</span>)</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">class</span> <span class="pl-v">Loss</span>:</div></div></div><div class="child-of-line-41 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-s">"""Loss base class.</span></div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-s"> To be implemented by subclasses:</span></div></div></div><div class="child-of-line-41 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-s"> * `call()`: Contains the logic for loss calculation using `y_true`,</span></div></div></div><div class="child-of-line-41 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-s"> `y_pred`.</span></div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-s"> Example subclass implementation:</span></div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-s"> ```python</span></div></div></div><div class="child-of-line-41 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-s"> class MeanSquaredError(Loss):</span></div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-s"> def call(self, y_true, y_pred):</span></div></div></div><div class="child-of-line-41 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-s"> return tf.reduce_mean(tf.math.square(y_pred - y_true), axis=-1)</span></div></div></div><div class="child-of-line-41 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-s"> ```</span></div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-s"> When using a Loss under a `tf.distribute.Strategy`, except passing it</span></div></div></div><div class="child-of-line-41 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-s"> to `Model.compile()` for use by `Model.fit()`, please use reduction</span></div></div></div><div class="child-of-line-41 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-s"> types 'SUM' or 'NONE', and reduce losses explicitly. Using 'AUTO' or</span></div></div></div><div class="child-of-line-41 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-s"> 'SUM_OVER_BATCH_SIZE' will raise an error when calling the Loss object</span></div></div></div><div class="child-of-line-41 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-s"> from a custom training loop or from user-defined code in `Layer.call()`.</span></div></div></div><div class="child-of-line-41 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"><span class="pl-s"> Please see this custom training</span></div></div></div><div class="child-of-line-41 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-s"> [tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-41 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-s"> for more details on this.</span></div></div></div><div class="child-of-line-41 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-s"> """</span></div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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"> <span class="pl-k">def</span> <span class="pl-en">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-c1">None</span>):</div></div></div><div class="child-of-line-41 child-of-line-67 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"> <span class="pl-s">"""Initializes `Loss` class.</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> </div></div></div><div class="child-of-line-41 child-of-line-67 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-s"> Args:</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"><span class="pl-s"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> custom training [tutorial](</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"><span class="pl-s"> for more details.</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> name: Optional name for the instance.</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"><span class="pl-s"> """</span></div></div></div><div class="child-of-line-41 child-of-line-67 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-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">validate</span>(<span class="pl-s1">reduction</span>)</div></div></div><div class="child-of-line-41 child-of-line-67 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-s1">self</span>.<span class="pl-c1">reduction</span> <span class="pl-c1">=</span> <span class="pl-s1">reduction</span></div></div></div><div class="child-of-line-41 child-of-line-67 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-s1">self</span>.<span class="pl-c1">name</span> <span class="pl-c1">=</span> <span class="pl-s1">name</span></div></div></div><div class="child-of-line-41 child-of-line-67 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"> <span class="pl-c"># SUM_OVER_BATCH is only allowed in losses managed by `fit` or</span></div></div></div><div class="child-of-line-41 child-of-line-67 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-c"># CannedEstimators.</span></div></div></div><div class="child-of-line-41 child-of-line-67 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-s1">self</span>.<span class="pl-c1">_allow_sum_over_batch_size</span> <span class="pl-c1">=</span> <span class="pl-c1">False</span></div></div></div><div class="child-of-line-41 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"> <span class="pl-s1">self</span>.<span class="pl-c1">_set_name_scope</span>()</div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-k">def</span> <span class="pl-en">_set_name_scope</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-41 child-of-line-91 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">"""Creates a valid `name_scope` name."""</span></div></div></div><div class="child-of-line-41 child-of-line-91 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-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">name</span> <span class="pl-c1">is</span> <span class="pl-c1">None</span>:</div></div></div><div class="child-of-line-41 child-of-line-91 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-s1">self</span>.<span class="pl-c1">_name_scope</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">__class__</span>.<span class="pl-c1">__name__</span>.<span class="pl-c1">strip</span>(<span class="pl-s">"_"</span>)</div></div></div><div class="child-of-line-41 child-of-line-91 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"> <span class="pl-k">elif</span> <span class="pl-s1">self</span>.<span class="pl-c1">name</span> <span class="pl-c1">==</span> <span class="pl-s">"<lambda>"</span>:</div></div></div><div class="child-of-line-41 child-of-line-91 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-s1">self</span>.<span class="pl-c1">_name_scope</span> <span class="pl-c1">=</span> <span class="pl-s">"lambda"</span></div></div></div><div class="child-of-line-41 child-of-line-91 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-k">else</span>:</div></div></div><div class="child-of-line-41 child-of-line-91 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"> <span class="pl-c"># E.g. '_my_loss' => 'my_loss'</span></div></div></div><div class="child-of-line-41 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-s1">self</span>.<span class="pl-c1">_name_scope</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">name</span>.<span class="pl-c1">strip</span>(<span class="pl-s">"_"</span>)</div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-k">def</span> <span class="pl-en">__call__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">y_true</span>, <span class="pl-s1">y_pred</span>, <span class="pl-s1">sample_weight</span><span class="pl-c1">=</span><span class="pl-c1">None</span>):</div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-s">"""Invokes the `Loss` instance.</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> </div></div></div><div class="child-of-line-41 child-of-line-101 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"><span class="pl-s"> Args:</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`,</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> except sparse loss functions such as sparse categorical</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> crossentropy where shape = `[batch_size, d0, .. dN-1]`</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> sample_weight: Optional `sample_weight` acts as a coefficient for</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> the loss. If a scalar is provided, then the loss is simply</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"><span class="pl-s"> scaled by the given value. If `sample_weight` is a tensor of</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> size `[batch_size]`, then the total loss for each sample of the</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> batch is rescaled by the corresponding element in the</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> `sample_weight` vector. If the shape of `sample_weight` is</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> `[batch_size, d0, .. dN-1]` (or can be broadcasted to this</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"><span class="pl-s"> shape), then each loss element of `y_pred` is scaled by the</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> corresponding value of `sample_weight`. (Note on`dN-1`: all loss</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> functions reduce by 1 dimension, usually axis=-1.)</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> </div></div></div><div class="child-of-line-41 child-of-line-101 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"> Returns:</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> Weighted loss float `Tensor`. If `reduction` is `NONE`, this has</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar.</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> (Note `dN-1` because all loss functions reduce by 1 dimension,</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> usually axis=-1.)</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-41 child-of-line-101 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"> Raises:</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> ValueError: If the shape of `sample_weight` is invalid.</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> """</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-c"># If we are wrapping a lambda function strip '<>' from the name as it is</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-c"># not accepted in scope name.</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">graph_ctx</span> <span class="pl-c1">=</span> <span class="pl-s1">tf_utils</span>.<span class="pl-c1">graph_context_for_symbolic_tensors</span>(</div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-s1">y_true</span>, <span class="pl-s1">y_pred</span>, <span class="pl-s1">sample_weight</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> )</div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-k">with</span> <span class="pl-s1">backend</span>.<span class="pl-c1">name_scope</span>(<span class="pl-s1">self</span>.<span class="pl-c1">_name_scope</span>), <span class="pl-s1">graph_ctx</span>:</div></div></div><div class="child-of-line-41 child-of-line-101 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-k">if</span> <span class="pl-s1">tf</span>.<span class="pl-c1">executing_eagerly</span>():</div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">call_fn</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">call</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-k">else</span>:</div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">call_fn</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">autograph</span>.<span class="pl-c1">tf_convert</span>(</div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-s1">self</span>.<span class="pl-c1">call</span>, <span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">autograph</span>.<span class="pl-c1">control_status_ctx</span>()</div></div></div><div class="child-of-line-41 child-of-line-101 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"> )</div></div></div><div class="child-of-line-41 child-of-line-101 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"> </div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">losses</span> <span class="pl-c1">=</span> <span class="pl-en">call_fn</span>(<span class="pl-s1">y_true</span>, <span class="pl-s1">y_pred</span>)</div></div></div><div class="child-of-line-41 child-of-line-101 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-41 child-of-line-101 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-s1">in_mask</span> <span class="pl-c1">=</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">get_mask</span>(<span class="pl-s1">y_pred</span>)</div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">out_mask</span> <span class="pl-c1">=</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">get_mask</span>(<span class="pl-s1">losses</span>)</div></div></div><div class="child-of-line-41 child-of-line-101 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-41 child-of-line-101 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-k">if</span> <span class="pl-s1">in_mask</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-s1">out_mask</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-41 child-of-line-101 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-s1">mask</span> <span class="pl-c1">=</span> <span class="pl-s1">in_mask</span> <span class="pl-c1">&</span> <span class="pl-s1">out_mask</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-k">elif</span> <span class="pl-s1">in_mask</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-41 child-of-line-101 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"> <span class="pl-s1">mask</span> <span class="pl-c1">=</span> <span class="pl-s1">in_mask</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-k">elif</span> <span class="pl-s1">out_mask</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-41 child-of-line-101 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-s1">mask</span> <span class="pl-c1">=</span> <span class="pl-s1">out_mask</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-k">else</span>:</div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">mask</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></div></div></div><div class="child-of-line-41 child-of-line-101 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-41 child-of-line-101 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-s1">reduction</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_get_reduction</span>()</div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">sample_weight</span> <span class="pl-c1">=</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">apply_valid_mask</span>(</div></div></div><div class="child-of-line-41 child-of-line-101 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-s1">losses</span>, <span class="pl-s1">sample_weight</span>, <span class="pl-s1">mask</span>, <span class="pl-s1">reduction</span></div></div></div><div class="child-of-line-41 child-of-line-101 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"> )</div></div></div><div class="child-of-line-41 child-of-line-101 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-k">return</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">compute_weighted_loss</span>(</div></div></div><div class="child-of-line-41 child-of-line-101 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"> <span class="pl-s1">losses</span>, <span class="pl-s1">sample_weight</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span></div></div></div><div class="child-of-line-41 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"> )</div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-en">@<span class="pl-s1">classmethod</span></span></div></div></div><div class="child-of-line-41 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-k">def</span> <span class="pl-en">from_config</span>(<span class="pl-s1">cls</span>, <span class="pl-s1">config</span>):</div></div></div><div class="child-of-line-41 child-of-line-165 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">"""Instantiates a `Loss` from its config (output of `get_config()`).</span></div></div></div><div class="child-of-line-41 child-of-line-165 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-41 child-of-line-165 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"> Args:</span></div></div></div><div class="child-of-line-41 child-of-line-165 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"> config: Output of `get_config()`.</span></div></div></div><div class="child-of-line-41 child-of-line-165 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-41 child-of-line-165 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"> Returns:</span></div></div></div><div class="child-of-line-41 child-of-line-165 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"> A `Loss` instance.</span></div></div></div><div class="child-of-line-41 child-of-line-165 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"> """</span></div></div></div><div class="child-of-line-41 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-k">return</span> <span class="pl-en">cls</span>(<span class="pl-c1">**</span><span class="pl-s1">config</span>)</div></div></div><div class="child-of-line-41 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-41 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-k">def</span> <span class="pl-en">get_config</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-41 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-s">"""Returns the config dictionary for a `Loss` instance."""</span></div></div></div><div class="child-of-line-41 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"> <span class="pl-k">return</span> {<span class="pl-s">"reduction"</span>: <span class="pl-s1">self</span>.<span class="pl-c1">reduction</span>, <span class="pl-s">"name"</span>: <span class="pl-s1">self</span>.<span class="pl-c1">name</span>}</div></div></div><div class="child-of-line-41 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"> </div></div></div><div class="child-of-line-41 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-en">@<span class="pl-s1">abc</span>.<span class="pl-c1">abstractmethod</span></span></div></div></div><div class="child-of-line-41 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-en">@<span class="pl-s1">doc_controls</span>.<span class="pl-c1">for_subclass_implementers</span></span></div></div></div><div class="child-of-line-41 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-k">def</span> <span class="pl-en">call</span>(<span class="pl-s1">self</span>, <span class="pl-s1">y_true</span>, <span class="pl-s1">y_pred</span>):</div></div></div><div class="child-of-line-41 child-of-line-182 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">"""Invokes the `Loss` instance.</span></div></div></div><div class="child-of-line-41 child-of-line-182 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-41 child-of-line-182 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-s"> Args:</span></div></div></div><div class="child-of-line-41 child-of-line-182 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"><span class="pl-s"> y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`,</span></div></div></div><div class="child-of-line-41 child-of-line-182 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"><span class="pl-s"> except sparse loss functions such as sparse categorical</span></div></div></div><div class="child-of-line-41 child-of-line-182 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-s"> crossentropy where shape = `[batch_size, d0, .. dN-1]`</span></div></div></div><div class="child-of-line-41 child-of-line-182 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"><span class="pl-s"> y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`</span></div></div></div><div class="child-of-line-41 child-of-line-182 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"> </div></div></div><div class="child-of-line-41 child-of-line-182 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-s"> Returns:</span></div></div></div><div class="child-of-line-41 child-of-line-182 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-s"> Loss values with the shape `[batch_size, d0, .. dN-1]`.</span></div></div></div><div class="child-of-line-41 child-of-line-182 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-s"> """</span></div></div></div><div class="child-of-line-41 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">raise</span> <span class="pl-en">NotImplementedError</span>(<span class="pl-s">"Must be implemented in subclasses."</span>)</div></div></div><div class="child-of-line-41 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-41 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">def</span> <span class="pl-en">_get_reduction</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-41 child-of-line-196 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-s">"""Handles `AUTO` reduction cases and returns the reduction value."""</span></div></div></div><div class="child-of-line-41 child-of-line-196 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">if</span> (</div></div></div><div class="child-of-line-41 child-of-line-196 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"> <span class="pl-c1">not</span> <span class="pl-s1">self</span>.<span class="pl-c1">_allow_sum_over_batch_size</span></div></div></div><div class="child-of-line-41 child-of-line-196 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-c1">and</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-41 child-of-line-196 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-c1">and</span> (</div></div></div><div class="child-of-line-41 child-of-line-196 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-s1">self</span>.<span class="pl-c1">reduction</span> <span class="pl-c1">==</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span></div></div></div><div class="child-of-line-41 child-of-line-196 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-c1">or</span> <span class="pl-s1">self</span>.<span class="pl-c1">reduction</span></div></div></div><div class="child-of-line-41 child-of-line-196 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"> <span class="pl-c1">==</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">SUM_OVER_BATCH_SIZE</span></div></div></div><div class="child-of-line-41 child-of-line-196 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"> )</div></div></div><div class="child-of-line-41 child-of-line-196 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"> ):</div></div></div><div class="child-of-line-41 child-of-line-196 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-k">raise</span> <span class="pl-en">ValueError</span>(</div></div></div><div class="child-of-line-41 child-of-line-196 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-s">"Please use `tf.keras.losses.Reduction.SUM` or "</span></div></div></div><div class="child-of-line-41 child-of-line-196 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"> <span class="pl-s">"`tf.keras.losses.Reduction.NONE` for loss reduction when "</span></div></div></div><div class="child-of-line-41 child-of-line-196 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-s">"losses are used with `tf.distribute.Strategy`, "</span></div></div></div><div class="child-of-line-41 child-of-line-196 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-s">"except for specifying losses in `Model.compile()` "</span></div></div></div><div class="child-of-line-41 child-of-line-196 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"> <span class="pl-s">"for use by the built-in training looop `Model.fit()`.<span class="pl-cce">\n</span>"</span></div></div></div><div class="child-of-line-41 child-of-line-196 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-s">"Please see https://www.tensorflow.org/tutorials"</span></div></div></div><div class="child-of-line-41 child-of-line-196 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-s">"/distribute/custom_training for more details."</span></div></div></div><div class="child-of-line-41 child-of-line-196 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"> )</div></div></div><div class="child-of-line-41 child-of-line-196 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"> </div></div></div><div class="child-of-line-41 child-of-line-196 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-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">reduction</span> <span class="pl-c1">==</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>:</div></div></div><div class="child-of-line-41 child-of-line-196 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-k">return</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">SUM_OVER_BATCH_SIZE</span></div></div></div><div class="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-k">return</span> <span class="pl-s1">self</span>.<span class="pl-c1">reduction</span></div></div></div><div class="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="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"> </div></div></div><div class="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-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.__internal__.losses.LossFunctionWrapper"</span>, <span class="pl-s1">v1</span><span class="pl-c1">=</span>[])</span></div></div></div><div class="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"><span class="pl-k">class</span> <span class="pl-v">LossFunctionWrapper</span>(<span class="pl-v">Loss</span>):</div></div></div><div class="child-of-line-223 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-s">"""Wraps a loss function in the `Loss` class."""</span></div></div></div><div class="child-of-line-223 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"> </div></div></div><div class="child-of-line-223 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"> <span class="pl-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-223 child-of-line-226 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-s1">self</span>, <span class="pl-s1">fn</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-c1">None</span>, <span class="pl-c1">**</span><span class="pl-s1">kwargs</span></div></div></div><div class="child-of-line-223 child-of-line-226 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"> ):</div></div></div><div class="child-of-line-223 child-of-line-226 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"> <span class="pl-s">"""Initializes `LossFunctionWrapper` class.</span></div></div></div><div class="child-of-line-223 child-of-line-226 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"> </div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> Args:</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> fn: The loss function to wrap, with signature `fn(y_true, y_pred,</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> **kwargs)`.</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-223 child-of-line-226 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"><span class="pl-s"> custom training [tutorial](</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> for more details.</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> name: Optional name for the instance.</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> **kwargs: The keyword arguments that are passed on to `fn`.</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-s"> """</span></div></div></div><div class="child-of-line-223 child-of-line-226 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-en">super</span>().<span class="pl-c1">__init__</span>(<span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>)</div></div></div><div class="child-of-line-223 child-of-line-226 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-s1">self</span>.<span class="pl-c1">fn</span> <span class="pl-c1">=</span> <span class="pl-s1">fn</span></div></div></div><div class="child-of-line-223 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-s1">self</span>.<span class="pl-c1">_fn_kwargs</span> <span class="pl-c1">=</span> <span class="pl-s1">kwargs</span></div></div></div><div class="child-of-line-223 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"> </div></div></div><div class="child-of-line-223 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"> <span class="pl-k">def</span> <span class="pl-en">call</span>(<span class="pl-s1">self</span>, <span class="pl-s1">y_true</span>, <span class="pl-s1">y_pred</span>):</div></div></div><div class="child-of-line-223 child-of-line-251 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-s">"""Invokes the `LossFunctionWrapper` instance.</span></div></div></div><div class="child-of-line-223 child-of-line-251 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-223 child-of-line-251 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-s"> Args:</span></div></div></div><div class="child-of-line-223 child-of-line-251 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-s"> y_true: Ground truth values.</span></div></div></div><div class="child-of-line-223 child-of-line-251 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-s"> y_pred: The predicted values.</span></div></div></div><div class="child-of-line-223 child-of-line-251 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"> </div></div></div><div class="child-of-line-223 child-of-line-251 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-s"> Returns:</span></div></div></div><div class="child-of-line-223 child-of-line-251 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-s"> Loss values per sample.</span></div></div></div><div class="child-of-line-223 child-of-line-251 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-s"> """</span></div></div></div><div class="child-of-line-223 child-of-line-251 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"> <span class="pl-k">if</span> <span class="pl-s1">tf</span>.<span class="pl-c1">is_tensor</span>(<span class="pl-s1">y_pred</span>) <span class="pl-c1">and</span> <span class="pl-s1">tf</span>.<span class="pl-c1">is_tensor</span>(<span class="pl-s1">y_true</span>):</div></div></div><div class="child-of-line-223 child-of-line-251 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-s1">y_pred</span>, <span class="pl-s1">y_true</span> <span class="pl-c1">=</span> <span class="pl-s1">losses_utils</span>.<span class="pl-c1">squeeze_or_expand_dimensions</span>(</div></div></div><div class="child-of-line-223 child-of-line-251 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-s1">y_pred</span>, <span class="pl-s1">y_true</span></div></div></div><div class="child-of-line-223 child-of-line-251 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"> )</div></div></div><div class="child-of-line-223 child-of-line-251 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"> </div></div></div><div class="child-of-line-223 child-of-line-251 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-s1">ag_fn</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">autograph</span>.<span class="pl-c1">tf_convert</span>(</div></div></div><div class="child-of-line-223 child-of-line-251 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-s1">self</span>.<span class="pl-c1">fn</span>, <span class="pl-s1">tf</span>.<span class="pl-c1">__internal__</span>.<span class="pl-c1">autograph</span>.<span class="pl-c1">control_status_ctx</span>()</div></div></div><div class="child-of-line-223 child-of-line-251 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"> )</div></div></div><div class="child-of-line-223 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"> <span class="pl-k">return</span> <span class="pl-en">ag_fn</span>(<span class="pl-s1">y_true</span>, <span class="pl-s1">y_pred</span>, <span class="pl-c1">**</span><span class="pl-s1">self</span>.<span class="pl-c1">_fn_kwargs</span>)</div></div></div><div class="child-of-line-223 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-223 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-k">def</span> <span class="pl-en">get_config</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-223 child-of-line-271 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-s1">config</span> <span class="pl-c1">=</span> {}</div></div></div><div class="child-of-line-223 child-of-line-271 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-k">for</span> <span class="pl-s1">k</span>, <span class="pl-s1">v</span> <span class="pl-c1">in</span> <span class="pl-s1">self</span>.<span class="pl-c1">_fn_kwargs</span>.<span class="pl-c1">items</span>():</div></div></div><div class="child-of-line-223 child-of-line-271 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"> <span class="pl-s1">config</span>[<span class="pl-s1">k</span>] <span class="pl-c1">=</span> (</div></div></div><div class="child-of-line-223 child-of-line-271 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">backend</span>.<span class="pl-c1">eval</span>(<span class="pl-s1">v</span>) <span class="pl-k">if</span> <span class="pl-s1">tf_utils</span>.<span class="pl-c1">is_tensor_or_variable</span>(<span class="pl-s1">v</span>) <span class="pl-k">else</span> <span class="pl-s1">v</span></div></div></div><div class="child-of-line-223 child-of-line-271 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"> )</div></div></div><div class="child-of-line-223 child-of-line-271 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"> </div></div></div><div class="child-of-line-223 child-of-line-271 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-k">if</span> <span class="pl-s1">saving_lib</span>.<span class="pl-c1">saving_v3_enabled</span>():</div></div></div><div class="child-of-line-223 child-of-line-271 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-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">get_registered_name</span></div></div></div><div class="child-of-line-223 child-of-line-271 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"> </div></div></div><div class="child-of-line-223 child-of-line-271 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">config</span>[<span class="pl-s">"fn"</span>] <span class="pl-c1">=</span> <span class="pl-en">get_registered_name</span>(<span class="pl-s1">self</span>.<span class="pl-c1">fn</span>)</div></div></div><div class="child-of-line-223 child-of-line-271 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"> </div></div></div><div class="child-of-line-223 child-of-line-271 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-s1">base_config</span> <span class="pl-c1">=</span> <span class="pl-en">super</span>().<span class="pl-c1">get_config</span>()</div></div></div><div class="child-of-line-223 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-k">return</span> <span class="pl-en">dict</span>(<span class="pl-en">list</span>(<span class="pl-s1">base_config</span>.<span class="pl-c1">items</span>()) <span class="pl-c1">+</span> <span class="pl-en">list</span>(<span class="pl-s1">config</span>.<span class="pl-c1">items</span>()))</div></div></div><div class="child-of-line-223 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"> </div></div></div><div class="child-of-line-223 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-en">@<span class="pl-s1">classmethod</span></span></div></div></div><div class="child-of-line-223 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"> <span class="pl-k">def</span> <span class="pl-en">from_config</span>(<span class="pl-s1">cls</span>, <span class="pl-s1">config</span>):</div></div></div><div class="child-of-line-223 child-of-line-287 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-s">"""Instantiates a `Loss` from its config (output of `get_config()`).</span></div></div></div><div class="child-of-line-223 child-of-line-287 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"> </div></div></div><div class="child-of-line-223 child-of-line-287 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"><span class="pl-s"> Args:</span></div></div></div><div class="child-of-line-223 child-of-line-287 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-s"> config: Output of `get_config()`.</span></div></div></div><div class="child-of-line-223 child-of-line-287 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"> </div></div></div><div class="child-of-line-223 child-of-line-287 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-s"> Returns:</span></div></div></div><div class="child-of-line-223 child-of-line-287 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-s"> A `keras.losses.Loss` instance.</span></div></div></div><div class="child-of-line-223 child-of-line-287 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-s"> """</span></div></div></div><div class="child-of-line-223 child-of-line-287 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-k">if</span> <span class="pl-s1">saving_lib</span>.<span class="pl-c1">saving_v3_enabled</span>():</div></div></div><div class="child-of-line-223 child-of-line-287 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"> <span class="pl-s1">fn_name</span> <span class="pl-c1">=</span> <span class="pl-s1">config</span>.<span class="pl-c1">pop</span>(<span class="pl-s">"fn"</span>, <span class="pl-c1">None</span>)</div></div></div><div class="child-of-line-223 child-of-line-287 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-k">if</span> <span class="pl-s1">fn_name</span> <span class="pl-c1">and</span> <span class="pl-s1">cls</span> <span class="pl-c1">is</span> <span class="pl-v">LossFunctionWrapper</span>:</div></div></div><div class="child-of-line-223 child-of-line-287 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-s1">config</span>[<span class="pl-s">"fn"</span>] <span class="pl-c1">=</span> <span class="pl-en">get</span>(<span class="pl-s1">fn_name</span>)</div></div></div><div class="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">return</span> <span class="pl-en">cls</span>(<span class="pl-c1">**</span><span class="pl-s1">config</span>)</div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.MeanSquaredError"</span>)</span></div></div></div><div class="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-k">class</span> <span class="pl-v">MeanSquaredError</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-304 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"> <span class="pl-s">"""Computes the mean of squares of errors between labels and predictions.</span></div></div></div><div class="child-of-line-304 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"> </div></div></div><div class="child-of-line-304 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"><span class="pl-s"> `loss = mean(square(y_true - y_pred))`</span></div></div></div><div class="child-of-line-304 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"> </div></div></div><div class="child-of-line-304 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-s"> Standalone usage:</span></div></div></div><div class="child-of-line-304 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"> </div></div></div><div class="child-of-line-304 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-s"> >>> y_true = [[0., 1.], [0., 0.]]</span></div></div></div><div class="child-of-line-304 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"><span class="pl-s"> >>> y_pred = [[1., 1.], [1., 0.]]</span></div></div></div><div class="child-of-line-304 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-s"> >>> # Using 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse = tf.keras.losses.MeanSquaredError()</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-304 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-s"> 0.5</span></div></div></div><div class="child-of-line-304 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"> </div></div></div><div class="child-of-line-304 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"><span class="pl-s"> >>> # Calling with 'sample_weight'.</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()</span></div></div></div><div class="child-of-line-304 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-s"> 0.25</span></div></div></div><div class="child-of-line-304 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"> </div></div></div><div class="child-of-line-304 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"><span class="pl-s"> >>> # Using 'sum' reduction type.</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse = tf.keras.losses.MeanSquaredError(</span></div></div></div><div class="child-of-line-304 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"><span class="pl-s"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-304 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-s"> 1.0</span></div></div></div><div class="child-of-line-304 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-304 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-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse = tf.keras.losses.MeanSquaredError(</span></div></div></div><div class="child-of-line-304 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-s"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-304 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-s"> >>> mse(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-304 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"><span class="pl-s"> array([0.5, 0.5], dtype=float32)</span></div></div></div><div class="child-of-line-304 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"> </div></div></div><div class="child-of-line-304 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"> Usage with the `compile()` API:</span></div></div></div><div class="child-of-line-304 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-304 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"> ```python</span></div></div></div><div class="child-of-line-304 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"> model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredError())</span></div></div></div><div class="child-of-line-304 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"> ```</span></div></div></div><div class="child-of-line-304 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-304 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"> </div></div></div><div class="child-of-line-304 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-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-304 child-of-line-341 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-s1">self</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"mean_squared_error"</span></div></div></div><div class="child-of-line-304 child-of-line-341 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"> ):</div></div></div><div class="child-of-line-304 child-of-line-341 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-s">"""Initializes `MeanSquaredError` instance.</span></div></div></div><div class="child-of-line-304 child-of-line-341 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"> </div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> Args:</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-304 child-of-line-341 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"><span class="pl-s"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-304 child-of-line-341 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"><span class="pl-s"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-304 child-of-line-341 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"><span class="pl-s"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> custom training [tutorial](</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> for more details.</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> name: Optional name for the instance. Defaults to</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> 'mean_squared_error'.</span></div></div></div><div class="child-of-line-304 child-of-line-341 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-s"> """</span></div></div></div><div class="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-en">super</span>().<span class="pl-c1">__init__</span>(<span class="pl-s1">mean_squared_error</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span>)</div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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"><span class="pl-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.MeanAbsoluteError"</span>)</span></div></div></div><div class="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-k">class</span> <span class="pl-v">MeanAbsoluteError</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-364 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-s">"""Computes the mean of absolute difference between labels and predictions.</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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"><span class="pl-s"> `loss = mean(abs(y_true - y_pred))`</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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-s"> Standalone usage:</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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-s"> >>> y_true = [[0., 1.], [0., 0.]]</span></div></div></div><div class="child-of-line-364 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"><span class="pl-s"> >>> y_pred = [[1., 1.], [1., 0.]]</span></div></div></div><div class="child-of-line-364 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-s"> >>> # Using 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-364 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-s"> >>> mae = tf.keras.losses.MeanAbsoluteError()</span></div></div></div><div class="child-of-line-364 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-s"> >>> mae(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-364 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-s"> 0.5</span></div></div></div><div class="child-of-line-364 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-364 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-s"> >>> # Calling with 'sample_weight'.</span></div></div></div><div class="child-of-line-364 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-s"> >>> mae(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()</span></div></div></div><div class="child-of-line-364 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-s"> 0.25</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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"> >>> # Using 'sum' reduction type.</span></div></div></div><div class="child-of-line-364 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"> >>> mae = tf.keras.losses.MeanAbsoluteError(</span></div></div></div><div class="child-of-line-364 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"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-364 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"><span class="pl-s"> >>> mae(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-364 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"><span class="pl-s"> 1.0</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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"><span class="pl-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-364 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-s"> >>> mae = tf.keras.losses.MeanAbsoluteError(</span></div></div></div><div class="child-of-line-364 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-s"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-364 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-s"> >>> mae(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-364 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-s"> array([0.5, 0.5], dtype=float32)</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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"><span class="pl-s"> Usage with the `compile()` API:</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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-s"> ```python</span></div></div></div><div class="child-of-line-364 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-s"> model.compile(optimizer='sgd', loss=tf.keras.losses.MeanAbsoluteError())</span></div></div></div><div class="child-of-line-364 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-s"> ```</span></div></div></div><div class="child-of-line-364 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-s"> """</span></div></div></div><div class="child-of-line-364 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"> </div></div></div><div class="child-of-line-364 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-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-364 child-of-line-401 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-s1">self</span>,</div></div></div><div class="child-of-line-364 child-of-line-401 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-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>,</div></div></div><div class="child-of-line-364 child-of-line-401 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"> <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"mean_absolute_error"</span>,</div></div></div><div class="child-of-line-364 child-of-line-401 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"> ):</div></div></div><div class="child-of-line-364 child-of-line-401 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-s">"""Initializes `MeanAbsoluteError` instance.</span></div></div></div><div class="child-of-line-364 child-of-line-401 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"> </div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> Args:</span></div></div></div><div class="child-of-line-364 child-of-line-401 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"><span class="pl-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> custom training [tutorial](</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> for more details.</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> name: Optional name for the instance. Defaults to</span></div></div></div><div class="child-of-line-364 child-of-line-401 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"><span class="pl-s"> 'mean_absolute_error'.</span></div></div></div><div class="child-of-line-364 child-of-line-401 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-s"> """</span></div></div></div><div class="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-en">super</span>().<span class="pl-c1">__init__</span>(<span class="pl-s1">mean_absolute_error</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span>)</div></div></div><div class="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="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"> </div></div></div><div class="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-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.MeanAbsolutePercentageError"</span>)</span></div></div></div><div class="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-k">class</span> <span class="pl-v">MeanAbsolutePercentageError</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-426 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"> <span class="pl-s">"""Computes the mean absolute percentage error between `y_true` & `y_pred`.</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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"> Formula:</span></div></div></div><div class="child-of-line-426 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-426 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"> `loss = 100 * abs((y_true - y_pred) / y_true)`</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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"> Note that to avoid dividing by zero, a small epsilon value</span></div></div></div><div class="child-of-line-426 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"> is added to the denominator.</span></div></div></div><div class="child-of-line-426 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-426 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"> Standalone usage:</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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"> >>> y_true = [[2., 1.], [2., 3.]]</span></div></div></div><div class="child-of-line-426 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"> >>> y_pred = [[1., 1.], [1., 0.]]</span></div></div></div><div class="child-of-line-426 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"><span class="pl-s"> >>> # Using 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-426 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"> >>> mape = tf.keras.losses.MeanAbsolutePercentageError()</span></div></div></div><div class="child-of-line-426 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"> >>> mape(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-426 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"> 50.</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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"> >>> # Calling with 'sample_weight'.</span></div></div></div><div class="child-of-line-426 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"> >>> mape(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()</span></div></div></div><div class="child-of-line-426 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"> 20.</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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"> >>> # Using 'sum' reduction type.</span></div></div></div><div class="child-of-line-426 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"><span class="pl-s"> >>> mape = tf.keras.losses.MeanAbsolutePercentageError(</span></div></div></div><div class="child-of-line-426 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"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-426 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"> >>> mape(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-426 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"> 100.</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-426 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-s"> >>> mape = tf.keras.losses.MeanAbsolutePercentageError(</span></div></div></div><div class="child-of-line-426 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"><span class="pl-s"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-426 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-s"> >>> mape(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-426 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-s"> array([25., 75.], dtype=float32)</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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"> Usage with the `compile()` API:</span></div></div></div><div class="child-of-line-426 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-426 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-s"> ```python</span></div></div></div><div class="child-of-line-426 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-s"> model.compile(optimizer='sgd',</span></div></div></div><div class="child-of-line-426 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-s"> loss=tf.keras.losses.MeanAbsolutePercentageError())</span></div></div></div><div class="child-of-line-426 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"> ```</span></div></div></div><div class="child-of-line-426 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"> """</span></div></div></div><div class="child-of-line-426 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"> </div></div></div><div class="child-of-line-426 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-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-426 child-of-line-469 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"> <span class="pl-s1">self</span>,</div></div></div><div class="child-of-line-426 child-of-line-469 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"> <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>,</div></div></div><div class="child-of-line-426 child-of-line-469 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-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"mean_absolute_percentage_error"</span>,</div></div></div><div class="child-of-line-426 child-of-line-469 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"> ):</div></div></div><div class="child-of-line-426 child-of-line-469 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-s">"""Initializes `MeanAbsolutePercentageError` instance.</span></div></div></div><div class="child-of-line-426 child-of-line-469 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"> </div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> Args:</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-426 child-of-line-469 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"><span class="pl-s"> custom training [tutorial](</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> for more details.</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> name: Optional name for the instance. Defaults to</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> 'mean_absolute_percentage_error'.</span></div></div></div><div class="child-of-line-426 child-of-line-469 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-s"> """</span></div></div></div><div class="child-of-line-426 child-of-line-469 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"> <span class="pl-en">super</span>().<span class="pl-c1">__init__</span>(</div></div></div><div class="child-of-line-426 child-of-line-469 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-s1">mean_absolute_percentage_error</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span></div></div></div><div class="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"> )</div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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"><span class="pl-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.MeanSquaredLogarithmicError"</span>)</span></div></div></div><div class="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">class</span> <span class="pl-v">MeanSquaredLogarithmicError</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-496 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"> <span class="pl-s">"""Computes the mean squared logarithmic error between `y_true` & `y_pred`.</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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-s"> `loss = square(log(y_true + 1.) - log(y_pred + 1.))`</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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"><span class="pl-s"> Standalone usage:</span></div></div></div><div class="child-of-line-496 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-496 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-s"> >>> y_true = [[0., 1.], [0., 0.]]</span></div></div></div><div class="child-of-line-496 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-s"> >>> y_pred = [[1., 1.], [1., 0.]]</span></div></div></div><div class="child-of-line-496 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-s"> >>> # Using 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-496 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-s"> >>> msle = tf.keras.losses.MeanSquaredLogarithmicError()</span></div></div></div><div class="child-of-line-496 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-s"> >>> msle(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-496 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-s"> 0.240</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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-s"> >>> # Calling with 'sample_weight'.</span></div></div></div><div class="child-of-line-496 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-s"> >>> msle(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy()</span></div></div></div><div class="child-of-line-496 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-s"> 0.120</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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-s"> >>> # Using 'sum' reduction type.</span></div></div></div><div class="child-of-line-496 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-s"> >>> msle = tf.keras.losses.MeanSquaredLogarithmicError(</span></div></div></div><div class="child-of-line-496 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-s"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-496 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-s"> >>> msle(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-496 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-s"> 0.480</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-496 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-s"> >>> msle = tf.keras.losses.MeanSquaredLogarithmicError(</span></div></div></div><div class="child-of-line-496 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"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-496 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"> >>> msle(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-496 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"> array([0.240, 0.240], dtype=float32)</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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"> Usage with the `compile()` API:</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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"> ```python</span></div></div></div><div class="child-of-line-496 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"> model.compile(optimizer='sgd',</span></div></div></div><div class="child-of-line-496 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"> loss=tf.keras.losses.MeanSquaredLogarithmicError())</span></div></div></div><div class="child-of-line-496 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"><span class="pl-s"> ```</span></div></div></div><div class="child-of-line-496 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-s"> """</span></div></div></div><div class="child-of-line-496 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"> </div></div></div><div class="child-of-line-496 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">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-496 child-of-line-534 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-s1">self</span>,</div></div></div><div class="child-of-line-496 child-of-line-534 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-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>,</div></div></div><div class="child-of-line-496 child-of-line-534 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"> <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"mean_squared_logarithmic_error"</span>,</div></div></div><div class="child-of-line-496 child-of-line-534 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"> ):</div></div></div><div class="child-of-line-496 child-of-line-534 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-s">"""Initializes `MeanSquaredLogarithmicError` instance.</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> </div></div></div><div class="child-of-line-496 child-of-line-534 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-s"> Args:</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"><span class="pl-s"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-496 child-of-line-534 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-s"> custom training [tutorial](</span></div></div></div><div class="child-of-line-496 child-of-line-534 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"><span class="pl-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-496 child-of-line-534 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-s"> for more details.</span></div></div></div><div class="child-of-line-496 child-of-line-534 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-s"> name: Optional name for the instance. Defaults to</span></div></div></div><div class="child-of-line-496 child-of-line-534 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-s"> 'mean_squared_logarithmic_error'.</span></div></div></div><div class="child-of-line-496 child-of-line-534 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-s"> """</span></div></div></div><div class="child-of-line-496 child-of-line-534 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-en">super</span>().<span class="pl-c1">__init__</span>(</div></div></div><div class="child-of-line-496 child-of-line-534 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-s1">mean_squared_logarithmic_error</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>, <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span></div></div></div><div class="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"> )</div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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"><span class="pl-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.BinaryCrossentropy"</span>)</span></div></div></div><div class="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"><span class="pl-k">class</span> <span class="pl-v">BinaryCrossentropy</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-561 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-s">"""Computes the cross-entropy loss between true labels and predicted labels.</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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-s"> Use this cross-entropy loss for binary (0 or 1) classification applications.</span></div></div></div><div class="child-of-line-561 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-s"> The loss function requires the following inputs:</span></div></div></div><div class="child-of-line-561 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-561 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-s"> - `y_true` (true label): This is either 0 or 1.</span></div></div></div><div class="child-of-line-561 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-s"> - `y_pred` (predicted value): This is the model's prediction, i.e, a single</span></div></div></div><div class="child-of-line-561 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-s"> floating-point value which either represents a</span></div></div></div><div class="child-of-line-561 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-s"> [logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf]</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> when `from_logits=True`) or a probability (i.e, value in [0., 1.] when</span></div></div></div><div class="child-of-line-561 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-s"> `from_logits=False`).</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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-s"> **Recommended Usage:** (set `from_logits=True`)</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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"><span class="pl-s"> With `tf.keras` API:</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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-s"> ```python</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> model.compile(</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),</span></div></div></div><div class="child-of-line-561 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-s"> ....</span></div></div></div><div class="child-of-line-561 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-s"> )</span></div></div></div><div class="child-of-line-561 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-s"> ```</span></div></div></div><div class="child-of-line-561 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-561 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-s"> As a standalone function:</span></div></div></div><div class="child-of-line-561 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-561 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-s"> >>> # Example 1: (batch_size = 1, number of samples = 4)</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> >>> y_true = [0, 1, 0, 0]</span></div></div></div><div class="child-of-line-561 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-s"> >>> y_pred = [-18.6, 0.51, 2.94, -12.8]</span></div></div></div><div class="child-of-line-561 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-s"> >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)</span></div></div></div><div class="child-of-line-561 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"> >>> bce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> 0.865</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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"> >>> # Example 2: (batch_size = 2, number of samples = 4)</span></div></div></div><div class="child-of-line-561 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"> >>> y_true = [[0, 1], [0, 0]]</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> >>> y_pred = [[-18.6, 0.51], [2.94, -12.8]]</span></div></div></div><div class="child-of-line-561 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"> >>> # Using default 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-561 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"> >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)</span></div></div></div><div class="child-of-line-561 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"> >>> bce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-561 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"> 0.865</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> >>> # Using 'sample_weight' attribute</span></div></div></div><div class="child-of-line-561 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"> >>> bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()</span></div></div></div><div class="child-of-line-561 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"> 0.243</span></div></div></div><div class="child-of-line-561 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"> >>> # Using 'sum' reduction` type.</span></div></div></div><div class="child-of-line-561 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"> >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,</span></div></div></div><div class="child-of-line-561 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"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-561 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"> >>> bce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-561 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"> 1.730</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-561 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"> >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True,</span></div></div></div><div class="child-of-line-561 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"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-561 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"> >>> bce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-561 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"> array([0.235, 1.496], dtype=float32)</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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"> **Default Usage:** (set `from_logits=False`)</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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"> >>> # Make the following updates to the above "Recommended Usage" section</span></div></div></div><div class="child-of-line-561 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"> >>> # 1. Set `from_logits=False`</span></div></div></div><div class="child-of-line-561 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"> >>> tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False')</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> >>> # 2. Update `y_pred` to use probabilities instead of logits</span></div></div></div><div class="child-of-line-561 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"><span class="pl-s"> >>> y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]]</span></div></div></div><div class="child-of-line-561 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-s"> """</span></div></div></div><div class="child-of-line-561 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"> </div></div></div><div class="child-of-line-561 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-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-561 child-of-line-624 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">self</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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">from_logits</span><span class="pl-c1">=</span><span class="pl-c1">False</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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">label_smoothing</span><span class="pl-c1">=</span><span class="pl-c1">0.0</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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">axis</span><span class="pl-c1">=</span><span class="pl-c1">-</span><span class="pl-c1">1</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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">name</span><span class="pl-c1">=</span><span class="pl-s">"binary_crossentropy"</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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"> ):</div></div></div><div class="child-of-line-561 child-of-line-624 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-s">"""Initializes `BinaryCrossentropy` instance.</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> </div></div></div><div class="child-of-line-561 child-of-line-624 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-s"> Args:</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"><span class="pl-s"> from_logits: Whether to interpret `y_pred` as a tensor of</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> [logit](https://en.wikipedia.org/wiki/Logit) values. By default,</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"><span class="pl-s"> we assume that `y_pred` contains probabilities (i.e., values in</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> [0, 1]).</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"><span class="pl-s"> label_smoothing: Float in [0, 1]. When 0, no smoothing occurs.</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> When > 0, we compute the loss between the predicted labels and a</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> smoothed version of the true labels, where the smoothing</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> squeezes the labels towards 0.5. Larger values of</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> `label_smoothing` correspond to heavier smoothing.</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> axis: The axis along which to compute crossentropy (the features</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> axis). Defaults to -1.</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"><span class="pl-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> loss. Default value is `AUTO`. `AUTO` indicates that the</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> reduction option will be determined by the usage context. For</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> used under a `tf.distribute.Strategy`, except via</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> `Model.compile()` and `Model.fit()`, using `AUTO` or</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> `SUM_OVER_BATCH_SIZE` will raise an error. Please see this</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> custom training [tutorial](</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> for more details.</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> name: Name for the op. Defaults to 'binary_crossentropy'.</span></div></div></div><div class="child-of-line-561 child-of-line-624 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"> """</span></div></div></div><div class="child-of-line-561 child-of-line-624 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-en">super</span>().<span class="pl-c1">__init__</span>(</div></div></div><div class="child-of-line-561 child-of-line-624 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-s1">binary_crossentropy</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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-s1">from_logits</span><span class="pl-c1">=</span><span class="pl-s1">from_logits</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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-s1">label_smoothing</span><span class="pl-c1">=</span><span class="pl-s1">label_smoothing</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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-s1">axis</span><span class="pl-c1">=</span><span class="pl-s1">axis</span>,</div></div></div><div class="child-of-line-561 child-of-line-624 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"> )</div></div></div><div class="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-s1">self</span>.<span class="pl-c1">from_logits</span> <span class="pl-c1">=</span> <span class="pl-s1">from_logits</span></div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.BinaryFocalCrossentropy"</span>)</span></div></div></div><div class="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-k">class</span> <span class="pl-v">BinaryFocalCrossentropy</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-670 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">"""Computes focal cross-entropy loss between true labels and predictions.</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> Binary cross-entropy loss is often used for binary (0 or 1) classification</span></div></div></div><div class="child-of-line-670 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"> tasks. The loss function requires the following inputs:</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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_true` (true label): This is either 0 or 1.</span></div></div></div><div class="child-of-line-670 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"> - `y_pred` (predicted value): This is the model's prediction, i.e, a single</span></div></div></div><div class="child-of-line-670 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"> floating-point value which either represents a</span></div></div></div><div class="child-of-line-670 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"> [logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf]</span></div></div></div><div class="child-of-line-670 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"> when `from_logits=True`) or a probability (i.e, value in [0., 1.] when</span></div></div></div><div class="child-of-line-670 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"> `from_logits=False`).</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it</span></div></div></div><div class="child-of-line-670 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"> helps to apply a "focal factor" to down-weight easy examples and focus more</span></div></div></div><div class="child-of-line-670 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"> on hard examples. By default, the focal tensor is computed as follows:</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> `focal_factor = (1 - output) ** gamma` for class 1</span></div></div></div><div class="child-of-line-670 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"> `focal_factor = output ** gamma` for class 0</span></div></div></div><div class="child-of-line-670 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"> where `gamma` is a focusing parameter. When `gamma=0`, this function is</span></div></div></div><div class="child-of-line-670 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"> equivalent to the binary crossentropy loss.</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> With the `compile()` API:</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> ```python</span></div></div></div><div class="child-of-line-670 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"> model.compile(</span></div></div></div><div class="child-of-line-670 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"> loss=tf.keras.losses.BinaryFocalCrossentropy(gamma=2.0, from_logits=True),</span></div></div></div><div class="child-of-line-670 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"> ....</span></div></div></div><div class="child-of-line-670 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"> )</span></div></div></div><div class="child-of-line-670 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"> ```</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> As a standalone function:</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Example 1: (batch_size = 1, number of samples = 4)</span></div></div></div><div class="child-of-line-670 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"> >>> y_true = [0, 1, 0, 0]</span></div></div></div><div class="child-of-line-670 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"> >>> y_pred = [-18.6, 0.51, 2.94, -12.8]</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=2,</span></div></div></div><div class="child-of-line-670 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"> ... from_logits=True)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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"> 0.691</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Apply class weight</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(</span></div></div></div><div class="child-of-line-670 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"> ... apply_class_balancing=True, gamma=2, from_logits=True)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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"> 0.51</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Example 2: (batch_size = 2, number of samples = 4)</span></div></div></div><div class="child-of-line-670 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"> >>> y_true = [[0, 1], [0, 0]]</span></div></div></div><div class="child-of-line-670 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"> >>> y_pred = [[-18.6, 0.51], [2.94, -12.8]]</span></div></div></div><div class="child-of-line-670 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"> >>> # Using default 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=3,</span></div></div></div><div class="child-of-line-670 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"> ... from_logits=True)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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"> 0.647</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Apply class weight</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(</span></div></div></div><div class="child-of-line-670 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"> ... apply_class_balancing=True, gamma=3, from_logits=True)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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"> 0.482</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Using 'sample_weight' attribute with focal effect</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=3,</span></div></div></div><div class="child-of-line-670 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"> ... from_logits=True)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()</span></div></div></div><div class="child-of-line-670 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"> 0.133</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Apply class weight</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(</span></div></div></div><div class="child-of-line-670 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"> ... apply_class_balancing=True, gamma=3, from_logits=True)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()</span></div></div></div><div class="child-of-line-670 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"> 0.097</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> >>> # Using 'sum' reduction` type.</span></div></div></div><div class="child-of-line-670 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"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=4,</span></div></div></div><div class="child-of-line-670 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"> ... from_logits=True,</span></div></div></div><div class="child-of-line-670 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-s"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-670 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-s"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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-s"> 1.222</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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-s"> >>> # Apply class weight</span></div></div></div><div class="child-of-line-670 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-s"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(</span></div></div></div><div class="child-of-line-670 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-s"> ... apply_class_balancing=True, gamma=4, from_logits=True,</span></div></div></div><div class="child-of-line-670 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-s"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-670 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-s"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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-s"> 0.914</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"><span class="pl-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-670 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-s"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(</span></div></div></div><div class="child-of-line-670 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-s"> ... gamma=5, from_logits=True,</span></div></div></div><div class="child-of-line-670 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-s"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-670 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"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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"> array([0.0017 1.1561], dtype=float32)</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"><span class="pl-s"> >>> # Apply class weight</span></div></div></div><div class="child-of-line-670 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-s"> >>> loss = tf.keras.losses.BinaryFocalCrossentropy(</span></div></div></div><div class="child-of-line-670 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-s"> ... apply_class_balancing=True, gamma=5, from_logits=True,</span></div></div></div><div class="child-of-line-670 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"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> >>> loss(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> array([0.0004 0.8670], dtype=float32)</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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-s"> Args:</span></div></div></div><div class="child-of-line-670 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-s"> apply_class_balancing: A bool, whether to apply weight balancing on the</span></div></div></div><div class="child-of-line-670 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-s"> binary classes 0 and 1.</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> alpha: A weight balancing factor for class 1, default is `0.25` as</span></div></div></div><div class="child-of-line-670 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-s"> mentioned in reference [Lin et al., 2018](</span></div></div></div><div class="child-of-line-670 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-s"> https://arxiv.org/pdf/1708.02002.pdf). The weight for class 0 is</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> `1.0 - alpha`.</span></div></div></div><div class="child-of-line-670 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-s"> gamma: A focusing parameter used to compute the focal factor, default is</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> `2.0` as mentioned in the reference</span></div></div></div><div class="child-of-line-670 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-s"> [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf).</span></div></div></div><div class="child-of-line-670 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-s"> from_logits: Whether to interpret `y_pred` as a tensor of</span></div></div></div><div class="child-of-line-670 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-s"> [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> assume that `y_pred` are probabilities (i.e., values in `[0, 1]`).</span></div></div></div><div class="child-of-line-670 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-s"> label_smoothing: Float in `[0, 1]`. When `0`, no smoothing occurs.</span></div></div></div><div class="child-of-line-670 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-s"> When > `0`, we compute the loss between the predicted labels and a</span></div></div></div><div class="child-of-line-670 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-s"> smoothed version of the true labels, where the smoothing squeezes</span></div></div></div><div class="child-of-line-670 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-s"> the labels towards `0.5`. Larger values of `label_smoothing`</span></div></div></div><div class="child-of-line-670 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-s"> correspond to heavier smoothing.</span></div></div></div><div class="child-of-line-670 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-s"> axis: The axis along which to compute crossentropy (the features axis).</span></div></div></div><div class="child-of-line-670 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-s"> Defaults to `-1`.</span></div></div></div><div class="child-of-line-670 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-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> loss. Default value is `AUTO`. `AUTO` indicates that the reduction</span></div></div></div><div class="child-of-line-670 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-s"> option will be determined by the usage context. For almost all cases</span></div></div></div><div class="child-of-line-670 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-s"> this defaults to `SUM_OVER_BATCH_SIZE`. When used under a</span></div></div></div><div class="child-of-line-670 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-s"> `tf.distribute.Strategy`, except via `Model.compile()` and</span></div></div></div><div class="child-of-line-670 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-s"> `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE`</span></div></div></div><div class="child-of-line-670 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-s"> will raise an error. Please see this custom training [tutorial](</span></div></div></div><div class="child-of-line-670 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-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> for more details.</span></div></div></div><div class="child-of-line-670 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"><span class="pl-s"> name: Name for the op. Defaults to 'binary_focal_crossentropy'.</span></div></div></div><div class="child-of-line-670 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-s"> """</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-670 child-of-line-805 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>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">apply_class_balancing</span><span class="pl-c1">=</span><span class="pl-c1">False</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">alpha</span><span class="pl-c1">=</span><span class="pl-c1">0.25</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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"> <span class="pl-s1">gamma</span><span class="pl-c1">=</span><span class="pl-c1">2.0</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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"> <span class="pl-s1">from_logits</span><span class="pl-c1">=</span><span class="pl-c1">False</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">label_smoothing</span><span class="pl-c1">=</span><span class="pl-c1">0.0</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-s1">axis</span><span class="pl-c1">=</span><span class="pl-c1">-</span><span class="pl-c1">1</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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"> <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"binary_focal_crossentropy"</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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"> ):</div></div></div><div class="child-of-line-670 child-of-line-805 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-s">"""Initializes `BinaryFocalCrossentropy` instance."""</span></div></div></div><div class="child-of-line-670 child-of-line-805 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"> <span class="pl-en">super</span>().<span class="pl-c1">__init__</span>(</div></div></div><div class="child-of-line-670 child-of-line-805 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"> <span class="pl-s1">binary_focal_crossentropy</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-s1">apply_class_balancing</span><span class="pl-c1">=</span><span class="pl-s1">apply_class_balancing</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">alpha</span><span class="pl-c1">=</span><span class="pl-s1">alpha</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">gamma</span><span class="pl-c1">=</span><span class="pl-s1">gamma</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-s1">from_logits</span><span class="pl-c1">=</span><span class="pl-s1">from_logits</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-s1">label_smoothing</span><span class="pl-c1">=</span><span class="pl-s1">label_smoothing</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-s1">axis</span><span class="pl-c1">=</span><span class="pl-s1">axis</span>,</div></div></div><div class="child-of-line-670 child-of-line-805 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-670 child-of-line-805 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-s1">self</span>.<span class="pl-c1">from_logits</span> <span class="pl-c1">=</span> <span class="pl-s1">from_logits</span></div></div></div><div class="child-of-line-670 child-of-line-805 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">apply_class_balancing</span> <span class="pl-c1">=</span> <span class="pl-s1">apply_class_balancing</span></div></div></div><div class="child-of-line-670 child-of-line-805 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"> <span class="pl-s1">self</span>.<span class="pl-c1">alpha</span> <span class="pl-c1">=</span> <span class="pl-s1">alpha</span></div></div></div><div class="child-of-line-670 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-s1">self</span>.<span class="pl-c1">gamma</span> <span class="pl-c1">=</span> <span class="pl-s1">gamma</span></div></div></div><div class="child-of-line-670 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"> </div></div></div><div class="child-of-line-670 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"> <span class="pl-k">def</span> <span class="pl-en">get_config</span>(<span class="pl-s1">self</span>):</div></div></div><div class="child-of-line-670 child-of-line-833 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-s1">config</span> <span class="pl-c1">=</span> {</div></div></div><div class="child-of-line-670 child-of-line-833 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-s">"apply_class_balancing"</span>: <span class="pl-s1">self</span>.<span class="pl-c1">apply_class_balancing</span>,</div></div></div><div class="child-of-line-670 child-of-line-833 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-s">"alpha"</span>: <span class="pl-s1">self</span>.<span class="pl-c1">alpha</span>,</div></div></div><div class="child-of-line-670 child-of-line-833 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-s">"gamma"</span>: <span class="pl-s1">self</span>.<span class="pl-c1">gamma</span>,</div></div></div><div class="child-of-line-670 child-of-line-833 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-670 child-of-line-833 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-s1">base_config</span> <span class="pl-c1">=</span> <span class="pl-en">super</span>().<span class="pl-c1">get_config</span>()</div></div></div><div class="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-k">return</span> <span class="pl-en">dict</span>(<span class="pl-en">list</span>(<span class="pl-s1">base_config</span>.<span class="pl-c1">items</span>()) <span class="pl-c1">+</span> <span class="pl-en">list</span>(<span class="pl-s1">config</span>.<span class="pl-c1">items</span>()))</div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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"><span class="pl-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.CategoricalCrossentropy"</span>)</span></div></div></div><div class="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">class</span> <span class="pl-v">CategoricalCrossentropy</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-844 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"> <span class="pl-s">"""Computes the crossentropy loss between the labels and predictions.</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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-s"> Use this crossentropy loss function when there are two or more label</span></div></div></div><div class="child-of-line-844 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-s"> classes. We expect labels to be provided in a `one_hot` representation. If</span></div></div></div><div class="child-of-line-844 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-s"> you want to provide labels as integers, please use</span></div></div></div><div class="child-of-line-844 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-s"> `SparseCategoricalCrossentropy` loss. There should be `# classes` floating</span></div></div></div><div class="child-of-line-844 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-s"> point values per feature.</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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-s"> In the snippet below, there is `# classes` floating pointing values per</span></div></div></div><div class="child-of-line-844 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-s"> example. The shape of both `y_pred` and `y_true` are</span></div></div></div><div class="child-of-line-844 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-s"> `[batch_size, num_classes]`.</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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"><span class="pl-s"> Standalone usage:</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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-s"> >>> y_true = [[0, 1, 0], [0, 0, 1]]</span></div></div></div><div class="child-of-line-844 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"><span class="pl-s"> >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]</span></div></div></div><div class="child-of-line-844 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-s"> >>> # Using 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-844 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-s"> >>> cce = tf.keras.losses.CategoricalCrossentropy()</span></div></div></div><div class="child-of-line-844 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-s"> >>> cce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-844 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-s"> 1.177</span></div></div></div><div class="child-of-line-844 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-844 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"><span class="pl-s"> >>> # Calling with 'sample_weight'.</span></div></div></div><div class="child-of-line-844 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-s"> >>> cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()</span></div></div></div><div class="child-of-line-844 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-s"> 0.814</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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"><span class="pl-s"> >>> # Using 'sum' reduction type.</span></div></div></div><div class="child-of-line-844 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-s"> >>> cce = tf.keras.losses.CategoricalCrossentropy(</span></div></div></div><div class="child-of-line-844 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-s"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-844 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"> >>> cce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-844 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"><span class="pl-s"> 2.354</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-844 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"><span class="pl-s"> >>> cce = tf.keras.losses.CategoricalCrossentropy(</span></div></div></div><div class="child-of-line-844 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"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-844 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"><span class="pl-s"> >>> cce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-844 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"> array([0.0513, 2.303], dtype=float32)</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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"> Usage with the `compile()` API:</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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"> ```python</span></div></div></div><div class="child-of-line-844 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"> model.compile(optimizer='sgd',</span></div></div></div><div class="child-of-line-844 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"><span class="pl-s"> loss=tf.keras.losses.CategoricalCrossentropy())</span></div></div></div><div class="child-of-line-844 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"> ```</span></div></div></div><div class="child-of-line-844 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"> """</span></div></div></div><div class="child-of-line-844 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"> </div></div></div><div class="child-of-line-844 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-k">def</span> <span class="pl-en">__init__</span>(</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">self</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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"> <span class="pl-s1">from_logits</span><span class="pl-c1">=</span><span class="pl-c1">False</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">label_smoothing</span><span class="pl-c1">=</span><span class="pl-c1">0.0</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">axis</span><span class="pl-c1">=</span><span class="pl-c1">-</span><span class="pl-c1">1</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">losses_utils</span>.<span class="pl-c1">ReductionV2</span>.<span class="pl-c1">AUTO</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">name</span><span class="pl-c1">=</span><span class="pl-s">"categorical_crossentropy"</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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"> ):</div></div></div><div class="child-of-line-844 child-of-line-890 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">"""Initializes `CategoricalCrossentropy` instance.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> </div></div></div><div class="child-of-line-844 child-of-line-890 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"> Args:</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> from_logits: Whether `y_pred` is expected to be a logits tensor. By</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> default, we assume that `y_pred` encodes a probability</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> distribution.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> label_smoothing: Float in [0, 1]. When > 0, label values are</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> smoothed, meaning the confidence on label values are relaxed.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"><span class="pl-s"> For example, if `0.1`, use `0.1 / num_classes` for non-target</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> labels and `0.9 + 0.1 / num_classes` for target labels.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> axis: The axis along which to compute crossentropy (the features</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> axis). Defaults to -1.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> reduction: Type of `tf.keras.losses.Reduction` to apply to loss.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> Default value is `AUTO`. `AUTO` indicates that the reduction</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> option will be determined by the usage context. For almost all</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"><span class="pl-s"> `tf.distribute.Strategy`, except via `Model.compile()` and</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE`</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> will raise an error. Please see this custom training [tutorial](</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> https://www.tensorflow.org/tutorials/distribute/custom_training)</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"><span class="pl-s"> for more details.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> name: Optional name for the instance.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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-s"> Defaults to 'categorical_crossentropy'.</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> """</span></div></div></div><div class="child-of-line-844 child-of-line-890 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"> <span class="pl-en">super</span>().<span class="pl-c1">__init__</span>(</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">categorical_crossentropy</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">name</span><span class="pl-c1">=</span><span class="pl-s1">name</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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"> <span class="pl-s1">reduction</span><span class="pl-c1">=</span><span class="pl-s1">reduction</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">from_logits</span><span class="pl-c1">=</span><span class="pl-s1">from_logits</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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"> <span class="pl-s1">label_smoothing</span><span class="pl-c1">=</span><span class="pl-s1">label_smoothing</span>,</div></div></div><div class="child-of-line-844 child-of-line-890 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-s1">axis</span><span class="pl-c1">=</span><span class="pl-s1">axis</span>,</div></div></div><div class="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"> )</div></div></div><div class="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"> </div></div></div><div class="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"> </div></div></div><div class="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-en">@<span class="pl-en">keras_export</span>(<span class="pl-s">"keras.losses.CategoricalFocalCrossentropy"</span>)</span></div></div></div><div class="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-k">class</span> <span class="pl-v">CategoricalFocalCrossentropy</span>(<span class="pl-v">LossFunctionWrapper</span>):</div></div></div><div class="child-of-line-933 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"> <span class="pl-s">"""Computes the alpha balanced focal crossentropy loss.</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> Use this crossentropy loss function when there are two or more label</span></div></div></div><div class="child-of-line-933 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"> classes and if you want to handle class imbalance without using</span></div></div></div><div class="child-of-line-933 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"> `class_weights`. We expect labels to be provided in a `one_hot`</span></div></div></div><div class="child-of-line-933 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"> representation.</span></div></div></div><div class="child-of-line-933 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-933 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"> According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it</span></div></div></div><div class="child-of-line-933 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"> helps to apply a focal factor to down-weight easy examples and focus more on</span></div></div></div><div class="child-of-line-933 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"> hard examples. The general formula for the focal loss (FL)</span></div></div></div><div class="child-of-line-933 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"> is as follows:</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> `FL(p_t) = (1 − p_t)^gamma * log(p_t)`</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> where `p_t` is defined as follows:</span></div></div></div><div class="child-of-line-933 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"> `p_t = output if y_true == 1, else 1 - output`</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> `(1 − p_t)^gamma` is the `modulating_factor`, where `gamma` is a focusing</span></div></div></div><div class="child-of-line-933 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"><span class="pl-s"> parameter. When `gamma` = 0, there is no focal effect on the cross entropy.</span></div></div></div><div class="child-of-line-933 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"> `gamma` reduces the importance given to simple examples in a smooth manner.</span></div></div></div><div class="child-of-line-933 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-933 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-s"> The authors use alpha-balanced variant of focal loss (FL) in the paper:</span></div></div></div><div class="child-of-line-933 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-s"> `FL(p_t) = −alpha * (1 − p_t)^gamma * log(p_t)`</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"><span class="pl-s"> where `alpha` is the weight factor for the classes. If `alpha` = 1, the</span></div></div></div><div class="child-of-line-933 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-s"> loss won't be able to handle class imbalance properly as all</span></div></div></div><div class="child-of-line-933 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-s"> classes will have the same weight. This can be a constant or a list of</span></div></div></div><div class="child-of-line-933 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"> constants. If alpha is a list, it must have the same length as the number</span></div></div></div><div class="child-of-line-933 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-s"> of classes.</span></div></div></div><div class="child-of-line-933 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-933 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-s"> The formula above can be generalized to:</span></div></div></div><div class="child-of-line-933 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-s"> `FL(p_t) = alpha * (1 − p_t)^gamma * CrossEntropy(y_true, y_pred)`</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"><span class="pl-s"> where minus comes from `CrossEntropy(y_true, y_pred)` (CE).</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> Extending this to multi-class case is straightforward:</span></div></div></div><div class="child-of-line-933 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"> `FL(p_t) = alpha * (1 − p_t)^gamma * CategoricalCE(y_true, y_pred)`</span></div></div></div><div class="child-of-line-933 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-933 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"> In the snippet below, there is `# classes` floating pointing values per</span></div></div></div><div class="child-of-line-933 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"> example. The shape of both `y_pred` and `y_true` are</span></div></div></div><div class="child-of-line-933 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"><span class="pl-s"> `[batch_size, num_classes]`.</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> Standalone usage:</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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-s"> >>> y_true = [[0., 1., 0.], [0., 0., 1.]]</span></div></div></div><div class="child-of-line-933 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-s"> >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]</span></div></div></div><div class="child-of-line-933 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-s"> >>> # Using 'auto'/'sum_over_batch_size' reduction type.</span></div></div></div><div class="child-of-line-933 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"> >>> cce = tf.keras.losses.CategoricalFocalCrossentropy()</span></div></div></div><div class="child-of-line-933 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"> >>> cce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-933 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"> 0.23315276</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"><span class="pl-s"> >>> # Calling with 'sample_weight'.</span></div></div></div><div class="child-of-line-933 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"><span class="pl-s"> >>> cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy()</span></div></div></div><div class="child-of-line-933 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-s"> 0.1632</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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"> >>> # Using 'sum' reduction type.</span></div></div></div><div class="child-of-line-933 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"> >>> cce = tf.keras.losses.CategoricalFocalCrossentropy(</span></div></div></div><div class="child-of-line-933 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"><span class="pl-s"> ... reduction=tf.keras.losses.Reduction.SUM)</span></div></div></div><div class="child-of-line-933 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"><span class="pl-s"> >>> cce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-933 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-s"> 0.46631</span></div></div></div><div class="child-of-line-933 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"> </div></div></div><div class="child-of-line-933 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-s"> >>> # Using 'none' reduction type.</span></div></div></div><div class="child-of-line-933 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-s"> >>> cce = tf.keras.losses.CategoricalFocalCrossentropy(</span></div></div></div><div class="child-of-line-933 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-s"> ... reduction=tf.keras.losses.Reduction.NONE)</span></div></div></div><div class="child-of-line-933 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-s"> >>> cce(y_true, y_pred).numpy()</span></div></div></div><div class="child-of-line-933 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" data-line-number="1000" style="position:relative"><span class="pl-s"> array([3.2058331e-05, 4.6627346e-01], dtype=float32)</span></div></div></div></div></div><div class="Box-sc-g0xbh4-0 gkZUDI"><a class="prc-Link-Link-85e08" href="https://github.com/keras-team/tf-keras/raw/refs/tags/v2.18.0/tf_keras/losses.py">View remainder of file in raw 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