280 lines
9.7 KiB
Python
280 lines
9.7 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Keras model saving code."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.eager import context
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from tensorflow.python.feature_column import feature_column_lib
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import test_util
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from tensorflow.python.keras import combinations
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from tensorflow.python.keras import losses
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.engine import sequential
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from tensorflow.python.keras.layers import core
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from tensorflow.python.keras.saving import model_config
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from tensorflow.python.keras.saving import save
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.ops import lookup_ops
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import loader_impl
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if sys.version_info >= (3, 6):
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import pathlib # pylint:disable=g-import-not-at-top
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try:
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import h5py # pylint:disable=g-import-not-at-top
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except ImportError:
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h5py = None
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class TestSaveModel(test.TestCase, parameterized.TestCase):
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def setUp(self):
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super(TestSaveModel, self).setUp()
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self.model = testing_utils.get_small_sequential_mlp(1, 2, 3)
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self.subclassed_model = testing_utils.get_small_subclass_mlp(1, 2)
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def assert_h5_format(self, path):
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if h5py is not None:
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self.assertTrue(h5py.is_hdf5(path),
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'Model saved at path {} is not a valid hdf5 file.'
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.format(path))
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def assert_saved_model(self, path):
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loader_impl.parse_saved_model(path)
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@test_util.run_v2_only
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def test_save_format_defaults(self):
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path = os.path.join(self.get_temp_dir(), 'model_path')
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save.save_model(self.model, path)
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self.assert_saved_model(path)
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@test_util.run_v2_only
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def test_save_hdf5(self):
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path = os.path.join(self.get_temp_dir(), 'model')
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save.save_model(self.model, path, save_format='h5')
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self.assert_h5_format(path)
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with self.assertRaisesRegexp(
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NotImplementedError,
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'requires the model to be a Functional model or a Sequential model.'):
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save.save_model(self.subclassed_model, path, save_format='h5')
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@test_util.run_v2_only
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def test_save_tf(self):
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path = os.path.join(self.get_temp_dir(), 'model')
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save.save_model(self.model, path, save_format='tf')
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self.assert_saved_model(path)
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with self.assertRaisesRegexp(ValueError, 'input shapes have not been set'):
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save.save_model(self.subclassed_model, path, save_format='tf')
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self.subclassed_model.predict(np.random.random((3, 5)))
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save.save_model(self.subclassed_model, path, save_format='tf')
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self.assert_saved_model(path)
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@test_util.run_v2_only
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def test_save_load_tf_string(self):
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path = os.path.join(self.get_temp_dir(), 'model')
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save.save_model(self.model, path, save_format='tf')
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save.load_model(path)
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@test_util.run_v2_only
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def test_save_load_tf_pathlib(self):
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if sys.version_info >= (3, 6):
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path = pathlib.Path(self.get_temp_dir()) / 'model'
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save.save_model(self.model, path, save_format='tf')
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save.load_model(path)
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_saving_with_dense_features(self):
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cols = [
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feature_column_lib.numeric_column('a'),
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feature_column_lib.indicator_column(
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feature_column_lib.categorical_column_with_vocabulary_list(
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'b', ['one', 'two']))
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]
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input_layers = {
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'a': keras.layers.Input(shape=(1,), name='a'),
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'b': keras.layers.Input(shape=(1,), name='b', dtype='string')
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}
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fc_layer = feature_column_lib.DenseFeatures(cols)(input_layers)
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output = keras.layers.Dense(10)(fc_layer)
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model = keras.models.Model(input_layers, output)
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model.compile(
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loss=keras.losses.MSE,
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optimizer='rmsprop',
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metrics=[keras.metrics.categorical_accuracy])
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config = model.to_json()
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loaded_model = model_config.model_from_json(config)
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inputs_a = np.arange(10).reshape(10, 1)
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inputs_b = np.arange(10).reshape(10, 1).astype('str')
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with self.cached_session():
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# Initialize tables for V1 lookup.
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if not context.executing_eagerly():
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self.evaluate(lookup_ops.tables_initializer())
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self.assertLen(loaded_model.predict({'a': inputs_a, 'b': inputs_b}), 10)
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_saving_with_sequence_features(self):
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cols = [
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feature_column_lib.sequence_numeric_column('a'),
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feature_column_lib.indicator_column(
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feature_column_lib.sequence_categorical_column_with_vocabulary_list(
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'b', ['one', 'two']))
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]
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input_layers = {
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'a':
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keras.layers.Input(shape=(None, 1), sparse=True, name='a'),
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'b':
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keras.layers.Input(
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shape=(None, 1), sparse=True, name='b', dtype='string')
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}
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fc_layer, _ = feature_column_lib.SequenceFeatures(cols)(input_layers)
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# TODO(tibell): Figure out the right dtype and apply masking.
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# sequence_length_mask = array_ops.sequence_mask(sequence_length)
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# x = keras.layers.GRU(32)(fc_layer, mask=sequence_length_mask)
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x = keras.layers.GRU(32)(fc_layer)
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output = keras.layers.Dense(10)(x)
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model = keras.models.Model(input_layers, output)
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model.compile(
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loss=keras.losses.MSE,
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optimizer='rmsprop',
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metrics=[keras.metrics.categorical_accuracy])
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config = model.to_json()
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loaded_model = model_config.model_from_json(config)
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batch_size = 10
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timesteps = 1
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values_a = np.arange(10, dtype=np.float32)
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indices_a = np.zeros((10, 3), dtype=np.int64)
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indices_a[:, 0] = np.arange(10)
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inputs_a = sparse_tensor.SparseTensor(indices_a, values_a,
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(batch_size, timesteps, 1))
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values_b = np.zeros(10, dtype=np.str)
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indices_b = np.zeros((10, 3), dtype=np.int64)
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indices_b[:, 0] = np.arange(10)
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inputs_b = sparse_tensor.SparseTensor(indices_b, values_b,
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(batch_size, timesteps, 1))
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with self.cached_session():
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# Initialize tables for V1 lookup.
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if not context.executing_eagerly():
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self.evaluate(lookup_ops.tables_initializer())
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self.assertLen(
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loaded_model.predict({
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'a': inputs_a,
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'b': inputs_b
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}, steps=1), batch_size)
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_saving_h5_for_rnn_layers(self):
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# See https://github.com/tensorflow/tensorflow/issues/35731 for details.
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inputs = keras.Input([10, 91], name='train_input')
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rnn_layers = [
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keras.layers.LSTMCell(size, recurrent_dropout=0, name='rnn_cell%d' % i)
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for i, size in enumerate([512, 512])
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]
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rnn_output = keras.layers.RNN(
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rnn_layers, return_sequences=True, name='rnn_layer')(inputs)
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pred_feat = keras.layers.Dense(91, name='prediction_features')(rnn_output)
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pred = keras.layers.Softmax()(pred_feat)
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model = keras.Model(inputs=[inputs], outputs=[pred, pred_feat])
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path = os.path.join(self.get_temp_dir(), 'model_path.h5')
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model.save(path)
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# Make sure the variable name is unique.
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self.assertNotEqual(rnn_layers[0].kernel.name,
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rnn_layers[1].kernel.name)
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self.assertIn('rnn_cell1', rnn_layers[1].kernel.name)
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_saving_optimizer_weights(self):
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class MyModel(keras.Model):
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def __init__(self):
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super(MyModel, self).__init__()
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self.layer = keras.layers.Dense(1)
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def call(self, x):
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return self.layer(x)
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path = os.path.join(self.get_temp_dir(), 'weights_path')
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x, y = np.ones((10, 10)), np.ones((10, 1))
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model = MyModel()
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model.compile('rmsprop', loss='bce')
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model.train_on_batch(x, y)
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model.reset_metrics()
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model.save_weights(path, save_format='tf')
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batch_loss = model.train_on_batch(x, y)
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new_model = MyModel()
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new_model.compile('rmsprop', loss='bce')
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new_model.train_on_batch(x, y)
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new_model.reset_metrics()
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new_model.load_weights(path)
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new_batch_loss = new_model.train_on_batch(x, y)
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self.assertAllClose(batch_loss, new_batch_loss)
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_saving_model_with_custom_object(self):
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with generic_utils.custom_object_scope():
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@generic_utils.register_keras_serializable()
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class CustomLoss(losses.MeanSquaredError):
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pass
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model = sequential.Sequential(
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[core.Dense(units=1, input_shape=(1,))])
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model.compile(optimizer='sgd', loss=CustomLoss())
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model.fit(np.zeros([10, 1]), np.zeros([10, 1]))
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temp_dir = self.get_temp_dir()
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filepath = os.path.join(temp_dir, 'saving')
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model.save(filepath)
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# Make sure the model can be correctly load back.
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_ = save.load_model(filepath, compile=True)
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if __name__ == '__main__':
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test.main()
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