159 lines
5.4 KiB
Python
159 lines
5.4 KiB
Python
# Copyright 2018 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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"""Keras models for use in Model subclassing tests."""
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import keras
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from keras.testing_infra import test_utils
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class SimpleConvTestModel(keras.Model):
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def __init__(self, num_classes=10):
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super().__init__(name="test_model")
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self.num_classes = num_classes
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self.conv1 = keras.layers.Conv2D(32, (3, 3), activation="relu")
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self.flatten = keras.layers.Flatten()
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self.dense1 = keras.layers.Dense(num_classes, activation="softmax")
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def call(self, x):
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x = self.conv1(x)
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x = self.flatten(x)
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return self.dense1(x)
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def get_multi_io_subclass_model(use_bn=False, use_dp=False, num_classes=(2, 3)):
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"""Creates MultiIOModel for the tests of subclass model."""
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shared_layer = keras.layers.Dense(32, activation="relu")
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branch_a = [shared_layer]
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if use_dp:
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branch_a.append(keras.layers.Dropout(0.5))
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branch_a.append(keras.layers.Dense(num_classes[0], activation="softmax"))
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branch_b = [shared_layer]
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if use_bn:
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branch_b.append(keras.layers.BatchNormalization())
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branch_b.append(keras.layers.Dense(num_classes[1], activation="softmax"))
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model = test_utils._MultiIOSubclassModel(
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branch_a, branch_b, name="test_model"
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)
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return model
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class NestedTestModel1(keras.Model):
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"""A model subclass nested inside a model subclass."""
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def __init__(self, num_classes=2):
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super().__init__(name="nested_model_1")
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self.num_classes = num_classes
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self.dense1 = keras.layers.Dense(32, activation="relu")
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self.dense2 = keras.layers.Dense(num_classes, activation="relu")
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self.bn = keras.layers.BatchNormalization()
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self.test_net = test_utils.SmallSubclassMLP(
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num_hidden=32, num_classes=4, use_bn=True, use_dp=True
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)
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def call(self, inputs):
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x = self.dense1(inputs)
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x = self.bn(x)
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x = self.test_net(x)
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return self.dense2(x)
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class NestedTestModel2(keras.Model):
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"""A model subclass with a functional-API graph network inside."""
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def __init__(self, num_classes=2):
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super().__init__(name="nested_model_2")
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self.num_classes = num_classes
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self.dense1 = keras.layers.Dense(32, activation="relu")
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self.dense2 = keras.layers.Dense(num_classes, activation="relu")
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self.bn = self.bn = keras.layers.BatchNormalization()
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self.test_net = self.get_functional_graph_model(32, 4)
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@staticmethod
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def get_functional_graph_model(input_dim, num_classes):
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# A simple functional-API model (a.k.a. graph network)
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inputs = keras.Input(shape=(input_dim,))
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x = keras.layers.Dense(32, activation="relu")(inputs)
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x = keras.layers.BatchNormalization()(x)
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outputs = keras.layers.Dense(num_classes)(x)
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return keras.Model(inputs, outputs)
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def call(self, inputs):
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x = self.dense1(inputs)
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x = self.bn(x)
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x = self.test_net(x)
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return self.dense2(x)
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def get_nested_model_3(input_dim, num_classes):
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# A functional-API model with a subclassed model inside.
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# NOTE: this requires the inner subclass to implement
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# `compute_output_shape`.
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inputs = keras.Input(shape=(input_dim,))
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x = keras.layers.Dense(32, activation="relu")(inputs)
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x = keras.layers.BatchNormalization()(x)
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class Inner(keras.Model):
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def __init__(self):
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super().__init__()
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self.dense1 = keras.layers.Dense(32, activation="relu")
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self.dense2 = keras.layers.Dense(5, activation="relu")
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self.bn = keras.layers.BatchNormalization()
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def call(self, inputs):
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x = self.dense1(inputs)
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x = self.dense2(x)
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return self.bn(x)
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test_model = Inner()
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x = test_model(x)
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outputs = keras.layers.Dense(num_classes)(x)
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return keras.Model(inputs, outputs, name="nested_model_3")
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class CustomCallModel(keras.Model):
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def __init__(self):
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super().__init__()
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self.dense1 = keras.layers.Dense(1, activation="relu")
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self.dense2 = keras.layers.Dense(1, activation="softmax")
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def call(self, first, second, fiddle_with_output="no", training=True):
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combined = self.dense1(first) + self.dense2(second)
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if fiddle_with_output == "yes":
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return 10.0 * combined
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else:
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return combined
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class TrainingNoDefaultModel(keras.Model):
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def __init__(self):
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super().__init__()
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self.dense1 = keras.layers.Dense(1)
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def call(self, x, training):
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return self.dense1(x)
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class TrainingMaskingModel(keras.Model):
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def __init__(self):
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super().__init__()
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self.dense1 = keras.layers.Dense(1)
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def call(self, x, training=False, mask=None):
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return self.dense1(x)
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