147 lines
4.0 KiB
Python
147 lines
4.0 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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"""A simple functional keras model with one layer."""
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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 numpy as np
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from tensorflow.python import keras
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.keras.distribute import model_collection_base
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from tensorflow.python.keras.optimizer_v2 import gradient_descent
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from tensorflow.python.module import module
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from tensorflow.python.ops import variables
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_BATCH_SIZE = 10
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def _get_data_for_simple_models():
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x_train = constant_op.constant(np.random.rand(1000, 3), dtype=dtypes.float32)
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y_train = constant_op.constant(np.random.rand(1000, 5), dtype=dtypes.float32)
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x_predict = constant_op.constant(
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np.random.rand(1000, 3), dtype=dtypes.float32)
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return x_train, y_train, x_predict
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class SimpleFunctionalModel(model_collection_base.ModelAndInput):
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"""A simple functional model and its inputs."""
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def get_model(self, **kwargs):
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output_name = 'output_1'
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x = keras.layers.Input(shape=(3,), dtype=dtypes.float32)
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y = keras.layers.Dense(5, dtype=dtypes.float32, name=output_name)(x)
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model = keras.Model(inputs=x, outputs=y)
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optimizer = gradient_descent.SGD(learning_rate=0.001)
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model.compile(
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loss='mse',
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metrics=['mae'],
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optimizer=optimizer)
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return model
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def get_data(self):
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return _get_data_for_simple_models()
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def get_batch_size(self):
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return _BATCH_SIZE
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class SimpleSequentialModel(model_collection_base.ModelAndInput):
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"""A simple sequential model and its inputs."""
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def get_model(self, **kwargs):
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output_name = 'output_1'
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model = keras.Sequential()
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y = keras.layers.Dense(
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5, dtype=dtypes.float32, name=output_name, input_dim=3)
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model.add(y)
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optimizer = gradient_descent.SGD(learning_rate=0.001)
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model.compile(
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loss='mse',
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metrics=['mae'],
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optimizer=optimizer)
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return model
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def get_data(self):
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return _get_data_for_simple_models()
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def get_batch_size(self):
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return _BATCH_SIZE
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class _SimpleModel(keras.Model):
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def __init__(self):
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super(_SimpleModel, self).__init__()
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self._dense_layer = keras.layers.Dense(5, dtype=dtypes.float32)
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def call(self, inputs):
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return self._dense_layer(inputs)
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class SimpleSubclassModel(model_collection_base.ModelAndInput):
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"""A simple subclass model and its data."""
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def get_model(self, **kwargs):
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model = _SimpleModel()
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optimizer = gradient_descent.SGD(learning_rate=0.001)
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model.compile(
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loss='mse',
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metrics=['mae'],
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cloning=False,
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optimizer=optimizer)
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return model
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def get_data(self):
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return _get_data_for_simple_models()
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def get_batch_size(self):
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return _BATCH_SIZE
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class _SimpleModule(module.Module):
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def __init__(self):
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self.v = variables.Variable(3.0)
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@def_function.function
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def __call__(self, x):
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return self.v * x
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class SimpleTFModuleModel(model_collection_base.ModelAndInput):
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"""A simple model based on tf.Module and its data."""
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def get_model(self, **kwargs):
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model = _SimpleModule()
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return model
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def get_data(self):
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return _get_data_for_simple_models()
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def get_batch_size(self):
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return _BATCH_SIZE
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