Migrate the SavedModel workflow

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<devsite-thumb-rating position="header"> </devsite-thumb-rating> </div> <h1 class="devsite-page-title" tabindex="-1"> Migrate the SavedModel workflow </h1> <devsite-feature-tooltip ack-key="AckCollectionsBookmarkTooltipDismiss" analytics-category="Site-Wide Custom Events" analytics-action-show="Callout Profile displayed" analytics-action-close="Callout Profile dismissed" analytics-label="Create Collection Callout" class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> Stay organized with collections Save and categorize content based on your preferences. </devsite-feature-tooltip> <div class="devsite-page-title-meta"><devsite-view-release-notes></devsite-view-release-notes></div> <devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded > </devsite-toc> <div class="devsite-article-body clearfix ">  <div itemscope itemtype="http://developers.google.com/ReferenceObject"> <meta itemprop="name" content="Migrate the SavedModel workflow" /> <meta itemprop="path" content="Guide & Tutorials" /> <meta itemprop="property" content="tf.Graph"/> <meta itemprop="property" content="tf.Module"/> <meta itemprop="property" content="tf.TensorSpec"/> <meta itemprop="property" content="tf.Variable"/> <meta itemprop="property" content="tf.compat.v1.Session"/> <meta itemprop="property" content="tf.compat.v1.placeholder"/> <meta itemprop="property" content="tf.compat.v1.saved_model.Builder"/> <meta itemprop="property" content="tf.compat.v1.saved_model.load"/> <meta itemprop="property" content="tf.compat.v1.saved_model.predict_signature_def"/> <meta itemprop="property" content="tf.compat.v1.saved_model.simple_save"/> <meta itemprop="property" content="tf.compat.v1.train.Saver"/> <meta itemprop="property" content="tf.compat.v1.train.get_global_step"/> <meta itemprop="property" 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src="https://www.tensorflow.org/images/colab_logo_32px.png"> Run in Google Colab</a> </td> <td> <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/guide/migrate/saved_model.ipynb"> <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png"> View source on GitHub</a> </td> <td> <a href="https://storage.googleapis.com/tensorflow_docs/docs/site/en/guide/migrate/saved_model.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png">Download notebook</a> </td> </table> Once you have migrated your model from TensorFlow 1's graphs and sessions to TensorFlow 2 APIs, such as <a href="https://www.tensorflow.org/api_docs/python/tf/function"><code translate="no" dir="ltr">tf.function</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/Module"><code translate="no" dir="ltr">tf.Module</code></a>, and <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model"><code translate="no" dir="ltr">tf.keras.Model</code></a>, you can migrate the model saving and loading code. This notebook provides examples of how you can save and load in the SavedModel format in TensorFlow 1 and TensorFlow 2. Here is a quick overview of the related API changes for migration from TensorFlow 1 to TensorFlow 2: <table> <thead> <tr> <th></th> <th>TensorFlow 1</th> <th>Migration to TensorFlow 2</th> </tr> </thead> <tbody> <tr> <td>Saving</td> <td><a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/Builder"><code translate="no" dir="ltr">tf.compat.v1.saved_model.Builder</code></a> <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/simple_save"><code translate="no" dir="ltr">tf.compat.v1.saved_model.simple_save</code></a></td> <td><a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/save"><code translate="no" dir="ltr">tf.saved_model.save</code></a> Keras: <a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model"><code translate="no" dir="ltr">tf.keras.models.save_model</code></a></td> </tr> <tr> <td>Loading</td> <td><a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/load"><code translate="no" dir="ltr">tf.compat.v1.saved_model.load</code></a></td> <td><a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/load"><code translate="no" dir="ltr">tf.saved_model.load</code></a> Keras: <a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model"><code translate="no" dir="ltr">tf.keras.models.load_model</code></a></td> </tr> <tr> <td>Signatures: a set of input and output tensors that can be used to run the </td> <td>Generated using the <code translate="no" dir="ltr">*.signature_def</code> utils (e.g. <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/predict_signature_def"><code translate="no" dir="ltr">tf.compat.v1.saved_model.predict_signature_def</code></a>)</td> <td>Write a <a href="https://www.tensorflow.org/api_docs/python/tf/function"><code translate="no" dir="ltr">tf.function</code></a> and export it using the <code translate="no" dir="ltr">signatures</code> argument in <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/save"><code translate="no" dir="ltr">tf.saved_model.save</code></a>.</td> </tr> <tr> <td>Classification and regression: special types of signatures</td> <td>Generated with <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/classification_signature_def"><code translate="no" dir="ltr">tf.compat.v1.saved_model.classification_signature_def</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/regression_signature_def"><code translate="no" dir="ltr">tf.compat.v1.saved_model.regression_signature_def</code></a>, and certain Estimator exports.</td> <td>These two signature types have been removed from TensorFlow 2. If the serving library requires these method names, <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/signature_def_utils/MethodNameUpdater"><code translate="no" dir="ltr">tf.compat.v1.saved_model.signature_def_utils.MethodNameUpdater</code></a>.</td> </tr> </tbody> </table> For a more in-depth explanation of the mapping, refer to the <a href="#changes_from_tf1_to_tf2">Changes from TensorFlow 1 to TensorFlow 2</a> section below. <h2 id="setup" data-text="Setup" tabindex="-1">Setup</h2> The examples below show how to export and load the same dummy TensorFlow model (defined as <code translate="no" dir="ltr">add_two</code> below) to a SavedModel format using the TensorFlow 1 and TensorFlow 2 APIs. Start by setting up the imports and utility functions: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">import tensorflow as tf import tensorflow.compat.v1 as tf1 import shutil def remove_dir(path): try: shutil.rmtree(path) except: pass def add_two(input): return input + 2 </code></pre></devsite-code> <h2 id="tensorflow_1_save_and_export_a_savedmodel" data-text="TensorFlow 1: Save and export a SavedModel" tabindex="-1">TensorFlow 1: Save and export a SavedModel</h2> In TensorFlow 1, you use the <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/Builder"><code translate="no" dir="ltr">tf.compat.v1.saved_model.Builder</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/simple_save"><code translate="no" dir="ltr">tf.compat.v1.saved_model.simple_save</code></a>, and <code translate="no" dir="ltr">tf.estimator.Estimator.export_saved_model</code> APIs to build, save, and export the TensorFlow graph and session: <h3 id="1_save_the_graph_as_a_savedmodel_with_savedmodelbuilder" data-text="1. Save the graph as a SavedModel with SavedModelBuilder" tabindex="-1">1. Save the graph as a SavedModel with SavedModelBuilder</h3> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="GDScript"><code translate="no" dir="ltr">remove_dir("saved-model-builder") with tf.Graph().as_default() as g: with tf1.Session() as sess: input = tf1.placeholder(tf.float32, shape=[]) output = add_two(input) print("add two output: ", sess.run(output, {input: 3.})) # Save with SavedModelBuilder builder = tf1.saved_model.Builder('saved-model-builder') sig_def = tf1.saved_model.predict_signature_def( inputs={'input': input}, outputs={'output': output}) builder.add_meta_graph_and_variables( sess, tags=["serve"], signature_def_map={ tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: sig_def }) builder.save() </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Matlab"><code translate="no" dir="ltr">!saved_model_cli run --dir saved-model-builder --tag_set serve \ --signature_def serving_default --input_exprs input=10 </code></pre></devsite-code> <h3 id="2_build_a_savedmodel_for_serving" data-text="2. Build a SavedModel for serving" tabindex="-1">2. Build a SavedModel for serving</h3> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Carbon"><code translate="no" dir="ltr">remove_dir("simple-save") with tf.Graph().as_default() as g: with tf1.Session() as sess: input = tf1.placeholder(tf.float32, shape=[]) output = add_two(input) print("add_two output: ", sess.run(output, {input: 3.})) tf1.saved_model.simple_save( sess, 'simple-save', inputs={'input': input}, outputs={'output': output}) </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Matlab"><code translate="no" dir="ltr">!saved_model_cli run --dir simple-save --tag_set serve \ --signature_def serving_default --input_exprs input=10 </code></pre></devsite-code> <h3 id="3_export_the_estimator_inference_graph_as_a_savedmodel" data-text="3. Export the Estimator inference graph as a SavedModel" tabindex="-1">3. Export the Estimator inference graph as a SavedModel</h3> In the definition of the Estimator <code translate="no" dir="ltr">model_fn</code> (defined below), you can define signatures in your model by returning <code translate="no" dir="ltr">export_outputs</code> in the <code translate="no" dir="ltr">tf.estimator.EstimatorSpec</code>. There are different types of outputs: <ul> <li><code translate="no" dir="ltr">tf.estimator.export.ClassificationOutput</code></li> <li><code translate="no" dir="ltr">tf.estimator.export.RegressionOutput</code></li> <li><code translate="no" dir="ltr">tf.estimator.export.PredictOutput</code></li> </ul> These will produce classification, regression, and prediction signature types, respectively. When the estimator is exported with <code translate="no" dir="ltr">tf.estimator.Estimator.export_saved_model</code>, these signatures will be saved with the model. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="MySQL"><code translate="no" dir="ltr">def model_fn(features, labels, mode): output = add_two(features['input']) step = tf1.train.get_global_step() return tf.estimator.EstimatorSpec( mode, predictions=output, train_op=step.assign_add(1), loss=tf.constant(0.), export_outputs={ tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: \ tf.estimator.export.PredictOutput({'output': output})}) est = tf.estimator.Estimator(model_fn, 'estimator-checkpoints') # Train for one step to create a checkpoint. def train_fn(): return tf.data.Dataset.from_tensors({'input': 3.}) est.train(train_fn, steps=1) # This utility function `build_raw_serving_input_receiver_fn` takes in raw # tensor features and builds an "input serving receiver function", which # creates placeholder inputs to the model. serving_input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn( {'input': tf.constant(3.)}) # Pass in a dummy input batch. estimator_path = est.export_saved_model('exported-estimator', serving_input_fn) # Estimator's export_saved_model creates a time stamped directory. Move this # to a set path so it can be inspected with `saved_model_cli` in the cell below. !rm -rf estimator-model import shutil shutil.move(estimator_path, 'estimator-model') </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Matlab"><code translate="no" dir="ltr">!saved_model_cli run --dir estimator-model --tag_set serve \ --signature_def serving_default --input_exprs input=[10] </code></pre></devsite-code> <h2 id="tensorflow_2_save_and_export_a_savedmodel" data-text="TensorFlow 2: Save and export a SavedModel" tabindex="-1">TensorFlow 2: Save and export a SavedModel</h2> <h3 id="save_and_export_a_savedmodel_defined_with_tfmodule" data-text="Save and export a SavedModel defined with tf.Module" tabindex="-1">Save and export a SavedModel defined with tf.Module</h3> To export your model in TensorFlow 2, you must define a <a href="https://www.tensorflow.org/api_docs/python/tf/Module"><code translate="no" dir="ltr">tf.Module</code></a> or a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model"><code translate="no" dir="ltr">tf.keras.Model</code></a> to hold all of your model's variables and functions. Then, you can call <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/save"><code translate="no" dir="ltr">tf.saved_model.save</code></a> to create a SavedModel. Refer to the Saving a custom model section in the <a href="https://www.tensorflow.org/guide/saved_model">Using the SavedModel format</a> guide to learn more. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Carbon"><code translate="no" dir="ltr">class MyModel(tf.Module): @tf.function def __call__(self, input): return add_two(input) model = MyModel() @tf.function def serving_default(input): return {'output': model(input)} signature_function = serving_default.get_concrete_function( tf.TensorSpec(shape=[], dtype=tf.float32)) tf.saved_model.save( model, 'tf2-save', signatures={ tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_function}) </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Matlab"><code translate="no" dir="ltr">!saved_model_cli run --dir tf2-save --tag_set serve \ --signature_def serving_default --input_exprs input=10 </code></pre></devsite-code> <h3 id="save_and_export_a_savedmodel_defined_with_keras" data-text="Save and export a SavedModel defined with Keras" tabindex="-1">Save and export a SavedModel defined with Keras</h3> <aside class="deprecated">Deprecated: For Keras objects, it's recommended to use the new high-level <code translate="no" dir="ltr">.keras</code> format and <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#export"><code translate="no" dir="ltr">tf.keras.Model.export</code></a>, as demonstrated in the guide <a href="https://www.tensorflow.org/guide/keras/save_and_serialize">here</a>. The low-level SavedModel format continues to be supported for existing code.</aside> The Keras APIs for saving and exporting—<a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#save"><code translate="no" dir="ltr">Model.save</code></a> or <a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model"><code translate="no" dir="ltr">tf.keras.models.save_model</code></a>—can export a SavedModel from a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model"><code translate="no" dir="ltr">tf.keras.Model</code></a>. Check out the <a href="https://www.tensorflow.org/guide/..guide/keras/save_and_serialize">Save and load Keras models</a> for more details. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="scdoc"><code translate="no" dir="ltr">inp = tf.keras.Input(3) out = add_two(inp) model = tf.keras.Model(inputs=inp, outputs=out) @tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.float32)]) def serving_default(input): return {'output': model(input)} model.save('keras-model', save_format='tf', signatures={ tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: serving_default}) </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Matlab"><code translate="no" dir="ltr">!saved_model_cli run --dir keras-model --tag_set serve \ --signature_def serving_default --input_exprs input=10 </code></pre></devsite-code> <h2 id="loading_a_savedmodel" data-text="Loading a SavedModel" tabindex="-1">Loading a SavedModel</h2> A SavedModel saved with any of the above APIs can be loaded using either TensorFlow 1 or TensorFlow 2 APIs. A TensorFlow 1 SavedModel can generally be used for inference when loaded into TensorFlow 2, but training (generating gradients) is only possible if the SavedModel contains resource variables. You can check the dtype of the variables—if the variable dtype contains "_ref", then it is a reference variable. A TensorFlow 2 SavedModel can be loaded and executed from TensorFlow 1 as long as the SavedModel is saved with signatures. The sections below contain code samples showing how to load the SavedModels saved in the previous sections, and call the exported signature. <h3 id="tensorflow_1_load_a_savedmodel_with_tfsaved_modelload" data-text="TensorFlow 1: Load a SavedModel with tf.saved_model.load" tabindex="-1">TensorFlow 1: Load a SavedModel with tf.saved_model.load</h3> In TensorFlow 1, you can import a SavedModel directly into the current graph and session using <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/load"><code translate="no" dir="ltr">tf.saved_model.load</code></a>. You can call <code translate="no" dir="ltr">Session.run</code> on the tensor input and output names: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="GDScript"><code translate="no" dir="ltr">def load_tf1(path, input): print('Loading from', path) with tf.Graph().as_default() as g: with tf1.Session() as sess: meta_graph = tf1.saved_model.load(sess, ["serve"], path) sig_def = meta_graph.signature_def[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] input_name = sig_def.inputs['input'].name output_name = sig_def.outputs['output'].name print(' Output with input', input, ': ', sess.run(output_name, feed_dict={input_name: input})) load_tf1('saved-model-builder', 5.) load_tf1('simple-save', 5.) load_tf1('estimator-model', [5.]) # Estimator's input must be batched. load_tf1('tf2-save', 5.) load_tf1('keras-model', 5.) </code></pre></devsite-code> <h3 id="tensorflow_2_load_a_model_saved_with_tfsaved_model" data-text="TensorFlow 2: Load a model saved with tf.saved_model" tabindex="-1">TensorFlow 2: Load a model saved with tf.saved_model</h3> In TensorFlow 2, objects are loaded into a Python object that stores the variables and functions. This is compatible with models saved from TensorFlow 1. Check out the <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/load"><code translate="no" dir="ltr">tf.saved_model.load</code></a> API docs and <a href="https://www.tensorflow.org/guide/saved_model#loading_and_using_a_custom_model">Loading and using a custom model</a> section from the <a href="https://www.tensorflow.org/guide/..guide/saved_model">Using the SavedModel format</a> guide for details. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="GDScript"><code translate="no" dir="ltr">def load_tf2(path, input): print('Loading from', path) loaded = tf.saved_model.load(path) out = loaded.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]( tf.constant(input))['output'] print(' Output with input', input, ': ', out) load_tf2('saved-model-builder', 5.) load_tf2('simple-save', 5.) load_tf2('estimator-model', [5.]) # Estimator's input must be batched. load_tf2('tf2-save', 5.) load_tf2('keras-model', 5.) </code></pre></devsite-code> Models saved with the TensorFlow 2 API can also access <a href="https://www.tensorflow.org/api_docs/python/tf/function"><code translate="no" dir="ltr">tf.function</code></a>s and variables that are attached to the model (instead of those exported as signatures). For example: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="GDScript"><code translate="no" dir="ltr">loaded = tf.saved_model.load('tf2-save') print('restored __call__:', loaded.__call__) print('output with input 5.', loaded(5)) </code></pre></devsite-code> <h3 id="tensorflow_2_load_a_model_saved_with_keras" data-text="TensorFlow 2: Load a model saved with Keras" tabindex="-1">TensorFlow 2: Load a model saved with Keras</h3> <aside class="deprecated">Deprecated: For Keras objects, it's recommended to use the new high-level <code translate="no" dir="ltr">.keras</code> format and <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#export"><code translate="no" dir="ltr">tf.keras.Model.export</code></a>, as demonstrated in the guide <a href="https://www.tensorflow.org/guide/keras/save_and_serialize">here</a>. The low-level SavedModel format continues to be supported for existing code.</aside> The Keras loading API—<a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model"><code translate="no" dir="ltr">tf.keras.models.load_model</code></a>—allows you to reload a saved model back into a Keras Model object. Note that this only allows you to load SavedModels saved with Keras (<a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#save"><code translate="no" dir="ltr">Model.save</code></a> or <a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model"><code translate="no" dir="ltr">tf.keras.models.save_model</code></a>). Models saved with <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/save"><code translate="no" dir="ltr">tf.saved_model.save</code></a> should be loaded with <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/load"><code translate="no" dir="ltr">tf.saved_model.load</code></a>. You can load a Keras model saved with <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#save"><code translate="no" dir="ltr">Model.save</code></a> using <a href="https://www.tensorflow.org/api_docs/python/tf/saved_model/load"><code translate="no" dir="ltr">tf.saved_model.load</code></a> but you will only get the TensorFlow graph. Refer to the <a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model"><code translate="no" dir="ltr">tf.keras.models.load_model</code></a> API docs and <a href="https://www.tensorflow.org/guide/keras/save_and_serialize#savedmodel_format">Save and load Keras models</a> guide for details. <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="GDScript"><code translate="no" dir="ltr">loaded_model = tf.keras.models.load_model('keras-model') loaded_model.predict_on_batch(tf.constant([1, 3, 4])) </code></pre></devsite-code> <h2 id="graphdef_and_metagraphdef" data-text="GraphDef and MetaGraphDef" tabindex="-1">GraphDef and MetaGraphDef</h2> <a name=\"graphdef_and_metagraphdef\"> </a> There is no straightforward way to load a raw <code translate="no" dir="ltr">GraphDef</code> or <code translate="no" dir="ltr">MetaGraphDef</code> to TF2. However, you can convert the TF1 code that imports the graph into a TF2 <a href="https://tensorflow.org/guide/concrete_function"><code translate="no" dir="ltr">concrete_function</code></a> using <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/wrap_function"><code translate="no" dir="ltr">v1.wrap_function</code></a>. First, save a MetaGraphDef: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="GDScript"><code translate="no" dir="ltr"># Save a simple multiplication computation: with tf.Graph().as_default() as g: x = tf1.placeholder(tf.float32, shape=[], name='x') v = tf.Variable(3.0, name='v') y = tf.multiply(x, v, name='y') with tf1.Session() as sess: sess.run(v.initializer) print(sess.run(y, feed_dict={x: 5})) s = tf1.train.Saver() s.export_meta_graph('multiply.pb', as_text=True) s.save(sess, 'multiply_values.ckpt') </code></pre></devsite-code> Using TF1 APIs, you can use <code translate="no" dir="ltr">tf1.train.import_meta_graph</code> to import the graph and restore the values: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="scdoc"><code translate="no" dir="ltr">with tf.Graph().as_default() as g: meta = tf1.train.import_meta_graph('multiply.pb') x = g.get_tensor_by_name('x:0') y = g.get_tensor_by_name('y:0') with tf1.Session() as sess: meta.restore(sess, 'multiply_values.ckpt') print(sess.run(y, feed_dict={x: 5})) </code></pre></devsite-code> There are no TF2 APIs for loading the graph, but you can still import it into a concrete function that can be executed in eager mode: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">def import_multiply(): # Any graph-building code is allowed here. tf1.train.import_meta_graph('multiply.pb') # Creates a tf.function with all the imported elements in the function graph. wrapped_import = tf1.wrap_function(import_multiply, []) import_graph = wrapped_import.graph x = import_graph.get_tensor_by_name('x:0') y = import_graph.get_tensor_by_name('y:0') # Restore the variable values. tf1.train.Saver(wrapped_import.variables).restore( sess=None, save_path='multiply_values.ckpt') # Create a concrete function by pruning the wrap_function (similar to sess.run). multiply_fn = wrapped_import.prune(feeds=x, fetches=y) # Run this function multiply_fn(tf.constant(5.)) # inputs to concrete functions must be Tensors. </code></pre></devsite-code> <h2 id="changes_from_tensorflow_1_to_tensorflow_2" data-text="Changes from TensorFlow 1 to TensorFlow 2" tabindex="-1">Changes from TensorFlow 1 to TensorFlow 2</h2> <a id=\"changes_from_tf1_to_tf2\"> </a> This section lists out key saving and loading terms from TensorFlow 1, their TensorFlow 2 equivalents, and what has changed. <h3 id="savedmodel" data-text="SavedModel" tabindex="-1">SavedModel</h3> <a href="https://www.tensorflow.org/guide/saved_model">SavedModel</a> is a format that stores a complete TensorFlow program with parameters and computation. It contains signatures used by serving platforms to run the model. The file format itself has not changed significantly, so SavedModels can be loaded and served using either TensorFlow 1 or TensorFlow 2 APIs. Differences between TensorFlow 1 and TensorFlow 2 The serving and inference use cases have not been updated in TensorFlow 2, aside from the API changes—the improvement was introduced in the ability to reuse and compose models loaded from SavedModel. In TensorFlow 2, the program is represented by objects like <a href="https://www.tensorflow.org/api_docs/python/tf/Variable"><code translate="no" dir="ltr">tf.Variable</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/Module"><code translate="no" dir="ltr">tf.Module</code></a>, or higher-level Keras models (<a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model"><code translate="no" dir="ltr">tf.keras.Model</code></a>) and layers (<a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers"><code translate="no" dir="ltr">tf.keras.layers</code></a>). There are no more global variables that have values stored in a session, and the graph now exists in different <a href="https://www.tensorflow.org/api_docs/python/tf/function"><code translate="no" dir="ltr">tf.function</code></a>s. Consequently, during a model export, SavedModel saves each component and function graphs separately. When you write a TensorFlow program with the TensorFlow Python APIs, you must build an object to manage the variables, functions, and other resources. Generally, this is accomplished by using the Keras APIs, but you can also build the object by creating or subclassing <a href="https://www.tensorflow.org/api_docs/python/tf/Module"><code translate="no" dir="ltr">tf.Module</code></a>. Keras models (<a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model"><code translate="no" dir="ltr">tf.keras.Model</code></a>) and <a href="https://www.tensorflow.org/api_docs/python/tf/Module"><code translate="no" dir="ltr">tf.Module</code></a> automatically track variables and functions attached to them. SavedModel saves these connections between modules, variables, and functions, so that they can be restored when loading. <h3 id="signatures" data-text="Signatures" tabindex="-1">Signatures</h3> Signatures are the endpoints of a SavedModel—they tell the user how to run the model and what inputs are needed. In TensorFlow 1, signatures are created by listing the input and output tensors. In TensorFlow 2, signatures are generated by passing in concrete functions. (Read more about TensorFlow functions in the <a href="https://www.tensorflow.org/guide/intro_to_graphs">Introduction to graphs and tf.function</a> guide, particularly the Polymorphism: one Function, many graphs section.) In short, a concrete function is generated from a <a href="https://www.tensorflow.org/api_docs/python/tf/function"><code translate="no" dir="ltr">tf.function</code></a>: <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># Option 1: Specify an input signature. @tf.function(input_signature=[...]) def fn(...): ... return outputs tf.saved_model.save(model, path, signatures={ 'name': fn }) </code></pre></devsite-code><div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"># Option 2: Call `get_concrete_function` @tf.function def fn(...): ... return outputs tf.saved_model.save(model, path, signatures={ 'name': fn.get_concrete_function(...) }) </code></pre></devsite-code> <h3 id="sessionrun" data-text="Session.run" tabindex="-1"><code translate="no" dir="ltr">Session.run</code></h3> In TensorFlow 1, you could call <code translate="no" dir="ltr">Session.run</code> with the imported graph as long as you already know the tensor names. This allows you to retrieve the restored variable values, or run parts of the model that were not exported in the signatures. In TensorFlow 2, you can directly access a variable, such as a weights matrix (<code translate="no" dir="ltr">kernel</code>): <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr">model = tf.Module() model.dense_layer = tf.keras.layers.Dense(...) tf.saved_model.save('my_saved_model') loaded = tf.saved_model.load('my_saved_model') loaded.dense_layer.kernel </code></pre></devsite-code> or call <a href="https://www.tensorflow.org/api_docs/python/tf/function"><code translate="no" dir="ltr">tf.function</code></a>s attached to the model object: for example, <code translate="no" dir="ltr">loaded.__call__</code>. Unlike TF1, there is no way to extract parts of a function and access intermediate values. You must export all of the needed functionality in the saved object. <h2 id="tensorflow_serving_migration_notes" data-text="TensorFlow Serving migration notes" tabindex="-1">TensorFlow Serving migration notes</h2> SavedModel was originally created to work with <a href="https://www.tensorflow.org/tfx/guide/serving">TensorFlow Serving</a>. This platform offers different types of prediction requests: classify, regress, and predict. The TensorFlow 1 API allows you to create these types of signatures with the utils: <ul> <li><a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/classification_signature_def"><code translate="no" dir="ltr">tf.compat.v1.saved_model.classification_signature_def</code></a></li> <li><a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/regression_signature_def"><code translate="no" dir="ltr">tf.compat.v1.saved_model.regression_signature_def</code></a></li> <li><a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/predict_signature_def"><code translate="no" dir="ltr">tf.compat.v1.saved_model.predict_signature_def</code></a></li> </ul> <a href="https://www.tensorflow.org/tfx/serving/signature_defs#classification_signaturedef">Classification</a> (<code translate="no" dir="ltr">classification_signature_def</code>) and <a href="https://www.tensorflow.org/tfx/serving/signature_defs#regression_signaturedef">regression</a> (<code translate="no" dir="ltr">regression_signature_def</code>) restrict the inputs and outputs, so the inputs must be a <code translate="no" dir="ltr">tf.Example</code>, and the outputs must be <code translate="no" dir="ltr">classes</code>, <code translate="no" dir="ltr">scores</code> or <code translate="no" dir="ltr">prediction</code>. Meanwhile, <a href="https://www.tensorflow.org/tfx/serving/signature_defs#predict_signaturedef">the predict signature</a> (<code translate="no" dir="ltr">predict_signature_def</code>) has no restrictions. SavedModels exported with the TensorFlow 2 API are compatible with TensorFlow Serving, but will only contain prediction signatures. The classification and regression signatures have been removed. If you require the use of the classification and regression signatures, you may modify the exported SavedModel using <a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/saved_model/signature_def_utils/MethodNameUpdater"><code translate="no" dir="ltr">tf.compat.v1.saved_model.signature_def_utils.MethodNameUpdater</code></a>. <h2 id="next_steps" data-text="Next steps" tabindex="-1">Next steps</h2> To learn more about SavedModels in TensorFlow 2, check out the following guides: <ul> <li><a href="https://www.tensorflow.org/guide/saved_model">Using the SavedModel format</a></li> <li><a href="https://www.tensorflow.org/guide/keras/save_and_serialize">Save and load Keras models</a></li> </ul> If you are using TensorFlow Hub, you may find these guides useful: <ul> <li><a href="https://www.tensorflow.org/hub/model_compatibility">TensorFlow Hub: Model compatibility for TensorFlow 1/TensorFlow 2</a></li> <li><a href="https://www.tensorflow.org/hub/migration_tf2">Migrating from TensorFlow 1 to TensorFlow 2 with TensorFlow Hub</a></li> </ul> </div> <devsite-thumb-rating position="footer"> </devsite-thumb-rating> <div class="devsite-floating-action-buttons"> </div> </article> <devsite-content-footer class="nocontent"> Except as otherwise noted, the content of this page is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 License</a>, and code samples are licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0 License</a>. 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