Add serialization backwards compatibility tests
PiperOrigin-RevId: 308156126 Change-Id: I2a0f3b8267ac3feff3695865f971ce527ab431c3
This commit is contained in:
Thomas O'Malley
TensorFlower Gardener
parent
fe2a529935
commit
3ce8efe987
@ -13,6 +13,22 @@ package(
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exports_files(["LICENSE"])
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tf_py_test(
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name = "get_config_test",
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srcs = ["get_config_test.py"],
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python_version = "PY3",
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shard_count = 4,
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tags = [
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"no_pip",
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],
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deps = [
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":get_config_samples",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python/keras",
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"@absl_py//absl/testing:parameterized",
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],
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)
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tf_py_test(
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name = "add_loss_correctness_test",
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srcs = ["add_loss_correctness_test.py"],
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@ -272,3 +288,10 @@ tf_py_test(
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"//third_party/py/numpy",
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],
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)
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py_library(
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name = "get_config_samples",
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srcs = ["get_config_samples.py"],
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srcs_version = "PY2AND3",
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deps = [],
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)
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491
tensorflow/python/keras/tests/get_config_samples.py
Normal file
491
tensorflow/python/keras/tests/get_config_samples.py
Normal file
@ -0,0 +1,491 @@
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# Copyright 2020 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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# pylint: disable=protected-access
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"""Sample `get_config` results for testing backwards compatibility."""
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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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# inputs = tf.keras.Input(10)
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# x = tf.keras.layers.Dense(10, activation='relu')(inputs)
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# outputs = tf.keras.layers.Dense(1)(x)
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# model = tf.keras.Model(inputs, outputs)
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FUNCTIONAL_DNN = {
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'input_layers': [['input_1', 0, 0]],
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'layers': [{
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'class_name': 'InputLayer',
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'config': {
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'batch_input_shape': (None, 10),
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'dtype': 'float32',
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'name': 'input_1',
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'ragged': False,
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'sparse': False
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},
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'inbound_nodes': [],
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'name': 'input_1'
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'relu',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense',
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'trainable': True,
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'units': 10,
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'use_bias': True
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},
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'inbound_nodes': [[['input_1', 0, 0, {}]]],
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'name': 'dense'
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense_1',
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'trainable': True,
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'units': 1,
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'use_bias': True
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},
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'inbound_nodes': [[['dense', 0, 0, {}]]],
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'name': 'dense_1'
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}],
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'name': 'model',
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'output_layers': [['dense_1', 0, 0]]
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}
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# inputs = tf.keras.Input((256, 256, 3))
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# x = tf.keras.layers.Conv2D(filters=3, kernel_size=(3, 3))(inputs)
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# x = tf.keras.layers.Flatten()(x)
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# outputs = tf.keras.layers.Dense(1)(x)
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# model = tf.keras.Model(inputs, outputs)
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FUNCTIONAL_CNN = {
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'input_layers': [['input_2', 0, 0]],
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'layers': [{
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'class_name': 'InputLayer',
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'config': {
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'batch_input_shape': (None, 256, 256, 3),
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'dtype': 'float32',
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'name': 'input_2',
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'ragged': False,
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'sparse': False
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},
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'inbound_nodes': [],
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'name': 'input_2'
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}, {
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'class_name': 'Conv2D',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'data_format': 'channels_last',
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'dilation_rate': (1, 1),
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'dtype': 'float32',
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'filters': 3,
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'kernel_size': (3, 3),
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'name': 'conv2d',
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'padding': 'valid',
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'strides': (1, 1),
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'trainable': True,
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'use_bias': True
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},
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'inbound_nodes': [[['input_2', 0, 0, {}]]],
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'name': 'conv2d'
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}, {
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'class_name': 'Flatten',
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'config': {
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'data_format': 'channels_last',
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'dtype': 'float32',
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'name': 'flatten',
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'trainable': True
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},
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'inbound_nodes': [[['conv2d', 0, 0, {}]]],
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'name': 'flatten'
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense_2',
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'trainable': True,
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'units': 1,
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'use_bias': True
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},
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'inbound_nodes': [[['flatten', 0, 0, {}]]],
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'name': 'dense_2'
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}],
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'name': 'model_1',
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'output_layers': [['dense_2', 0, 0]]
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}
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# inputs = tf.keras.Input((10, 3))
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# x = tf.keras.layers.LSTM(10)(inputs)
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# outputs = tf.keras.layers.Dense(1)(x)
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# model = tf.keras.Model(inputs, outputs)
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FUNCTIONAL_LSTM = {
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'input_layers': [['input_5', 0, 0]],
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'layers': [{
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'class_name': 'InputLayer',
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'config': {
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'batch_input_shape': (None, 10, 3),
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'dtype': 'float32',
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'name': 'input_5',
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'ragged': False,
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'sparse': False
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},
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'inbound_nodes': [],
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'name': 'input_5'
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}, {
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'class_name': 'LSTM',
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'config': {
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'activation': 'tanh',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dropout': 0.0,
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'dtype': 'float32',
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'go_backwards': False,
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'implementation': 2,
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'lstm_2',
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'recurrent_activation': 'sigmoid',
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'recurrent_constraint': None,
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'recurrent_dropout': 0.0,
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'recurrent_initializer': {
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'class_name': 'Orthogonal',
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'config': {
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'gain': 1.0,
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'seed': None
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}
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},
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'recurrent_regularizer': None,
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'return_sequences': False,
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'return_state': False,
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'stateful': False,
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'time_major': False,
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'trainable': True,
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'unit_forget_bias': True,
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'units': 10,
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'unroll': False,
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'use_bias': True
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},
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'inbound_nodes': [[['input_5', 0, 0, {}]]],
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'name': 'lstm_2'
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense_4',
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'trainable': True,
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'units': 1,
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'use_bias': True
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},
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'inbound_nodes': [[['lstm_2', 0, 0, {}]]],
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'name': 'dense_4'
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}],
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'name': 'model_3',
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'output_layers': [['dense_4', 0, 0]]
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}
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# model = tf.keras.Sequential()
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# model.add(tf.keras.layers.Dense(10))
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# model.add(tf.keras.layers.Dense(1))
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SEQUENTIAL_DNN = {
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'layers': [{
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense_2',
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'trainable': True,
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'units': 10,
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'use_bias': True
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}
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense_3',
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'trainable': True,
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'units': 1,
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'use_bias': True
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}
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}],
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'name': 'sequential_1'
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}
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# model = tf.keras.Sequential()
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# model.add(tf.keras.layers.Conv2D(32, (3, 3)))
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# model.add(tf.keras.layers.Flatten())
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# model.add(tf.keras.layers.Dense(1))
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SEQUENTIAL_CNN = {
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'layers': [{
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'class_name': 'Conv2D',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'data_format': 'channels_last',
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'dilation_rate': (1, 1),
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'dtype': 'float32',
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'filters': 32,
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'kernel_size': (3, 3),
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'name': 'conv2d_1',
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'padding': 'valid',
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'strides': (1, 1),
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'trainable': True,
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'use_bias': True
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}
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}, {
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'class_name': 'Flatten',
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'config': {
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'data_format': 'channels_last',
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'dtype': 'float32',
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'name': 'flatten_1',
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'trainable': True
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}
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'dense_6',
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'trainable': True,
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'units': 1,
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'use_bias': True
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}
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}],
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'name': 'sequential_4'
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}
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# model = tf.keras.Sequential()
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# model.add(tf.keras.layers.LSTM(10))
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# model.add(tf.keras.layers.Dense(1))
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SEQUENTIAL_LSTM = {
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'layers': [{
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'class_name': 'LSTM',
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'config': {
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'activation': 'tanh',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dropout': 0.0,
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'dtype': 'float32',
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'go_backwards': False,
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'implementation': 2,
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'kernel_constraint': None,
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'kernel_initializer': {
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'class_name': 'GlorotUniform',
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'config': {
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'seed': None
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}
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},
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'kernel_regularizer': None,
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'name': 'lstm',
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'recurrent_activation': 'sigmoid',
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'recurrent_constraint': None,
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'recurrent_dropout': 0.0,
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'recurrent_initializer': {
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'class_name': 'Orthogonal',
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'config': {
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'gain': 1.0,
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'seed': None
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}
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},
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'recurrent_regularizer': None,
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'return_sequences': False,
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'return_state': False,
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'stateful': False,
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'time_major': False,
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'trainable': True,
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'unit_forget_bias': True,
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'units': 10,
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'unroll': False,
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'use_bias': True
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}
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}, {
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'class_name': 'Dense',
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'config': {
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'activation': 'linear',
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'activity_regularizer': None,
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'bias_constraint': None,
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {}
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},
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'bias_regularizer': None,
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'dtype': 'float32',
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'kernel_constraint': None,
|
||||
'kernel_initializer': {
|
||||
'class_name': 'GlorotUniform',
|
||||
'config': {
|
||||
'seed': None
|
||||
}
|
||||
},
|
||||
'kernel_regularizer': None,
|
||||
'name': 'dense_4',
|
||||
'trainable': True,
|
||||
'units': 1,
|
||||
'use_bias': True
|
||||
}
|
||||
}],
|
||||
'name': 'sequential_2'
|
||||
}
|
||||
58
tensorflow/python/keras/tests/get_config_test.py
Normal file
58
tensorflow/python/keras/tests/get_config_test.py
Normal file
@ -0,0 +1,58 @@
|
||||
# Copyright 2020 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.
|
||||
#,============================================================================
|
||||
"""Tests for `get_config` backwards compatibility."""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.python.keras import keras_parameterized
|
||||
from tensorflow.python.keras.engine import sequential
|
||||
from tensorflow.python.keras.engine import training
|
||||
from tensorflow.python.keras.tests import get_config_samples
|
||||
from tensorflow.python.platform import test
|
||||
|
||||
|
||||
@keras_parameterized.run_all_keras_modes
|
||||
class TestGetConfigBackwardsCompatible(keras_parameterized.TestCase):
|
||||
|
||||
def test_functional_dnn(self):
|
||||
model = training.Model.from_config(get_config_samples.FUNCTIONAL_DNN)
|
||||
self.assertLen(model.layers, 3)
|
||||
|
||||
def test_functional_cnn(self):
|
||||
model = training.Model.from_config(get_config_samples.FUNCTIONAL_CNN)
|
||||
self.assertLen(model.layers, 4)
|
||||
|
||||
def test_functional_lstm(self):
|
||||
model = training.Model.from_config(get_config_samples.FUNCTIONAL_LSTM)
|
||||
self.assertLen(model.layers, 3)
|
||||
|
||||
def test_sequential_dnn(self):
|
||||
model = sequential.Sequential.from_config(get_config_samples.SEQUENTIAL_DNN)
|
||||
self.assertLen(model.layers, 2)
|
||||
|
||||
def test_sequential_cnn(self):
|
||||
model = sequential.Sequential.from_config(get_config_samples.SEQUENTIAL_CNN)
|
||||
self.assertLen(model.layers, 3)
|
||||
|
||||
def test_sequential_lstm(self):
|
||||
model = sequential.Sequential.from_config(
|
||||
get_config_samples.SEQUENTIAL_LSTM)
|
||||
self.assertLen(model.layers, 2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test.main()
|
||||
Loading…
Reference in New Issue
Block a user