9485250f44
It also allows us to simplify internal node-walking code. This may come with a subtle behavior change/simplification in the interaction between `training`, learning phase scopes and model construction. Before, it seems that constructing a network model in a learning phase scope would cause that model to permanently use that learning phase in some future code regardless of what values of `training` were passed in. The code underlying this behavior seems to have been pretty fragile and hard to guarantee. It was also quite likely unintentional or buggy in many cases. After this change, setting a learning phase scope will continue affecting call-time behavior when `training` isn't explicitly passed in, but building a model inside of a learning phase scope won't force it to always use that learning phase. (Passing `training=...` at construction time will continue to be the recommended method of freezing behavior at call time) PiperOrigin-RevId: 308436442 Change-Id: I8cb8922a6e3cd219a1771328dda3003287978b39
161 lines
5.7 KiB
Python
161 lines
5.7 KiB
Python
# Copyright 2016 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 layer graphs construction & handling."""
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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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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras.engine import base_layer
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from tensorflow.python.keras.engine import node as node_module
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from tensorflow.python.platform import test
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class DummyTensor(object):
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def __init__(self, shape=None):
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self.shape = shape
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class DummyLayer(base_layer.Layer):
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pass
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class NetworkConstructionTest(keras_parameterized.TestCase):
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def test_chained_node_construction(self):
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# test basics
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a = DummyTensor(shape=(None, 32))
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b = DummyTensor(shape=(None, 32))
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a_layer = DummyLayer()
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node = node_module.Node(a_layer, outputs=a)
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self.assertEqual(node.outbound_layer, a_layer)
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self.assertTrue(node.is_input)
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self.assertListEqual(node.inbound_layers, [])
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self.assertListEqual(node.input_tensors, [a])
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self.assertListEqual(node.input_shapes, [(None, 32)])
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self.assertListEqual(node.output_tensors, [a])
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self.assertListEqual(node.output_shapes, [(None, 32)])
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b_layer = DummyLayer()
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node_module.Node(b_layer, outputs=b)
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dense = DummyLayer()
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a_2 = DummyTensor()
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node_a = node_module.Node(layer=dense, call_args=(a,), outputs=a_2)
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b_2 = DummyTensor()
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node_b = node_module.Node(layer=dense, call_args=(b,), outputs=b_2)
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# test the node attributes
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self.assertFalse(node_a.is_input)
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self.assertFalse(node_b.is_input)
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self.assertEqual(node_a.call_args, (a,))
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self.assertEqual(node_a.call_kwargs, {})
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self.assertEqual(node_a.outputs, a_2)
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# Test the layer wiring
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self.assertLen(dense._inbound_nodes, 2)
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self.assertLen(dense._outbound_nodes, 0)
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self.assertEqual(dense._inbound_nodes, [node_a, node_b])
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self.assertEqual(dense._inbound_nodes[0].inbound_layers, a_layer)
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self.assertEqual(dense._inbound_nodes[0].outbound_layer, dense)
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self.assertEqual(dense._inbound_nodes[1].inbound_layers, b_layer)
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self.assertEqual(dense._inbound_nodes[1].outbound_layer, dense)
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self.assertIs(dense._inbound_nodes[0].input_tensors, a)
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self.assertIs(dense._inbound_nodes[1].input_tensors, b)
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def test_multi_input_node(self):
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# test multi-input layer
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a = DummyTensor()
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b = DummyTensor()
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dense = DummyLayer()
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a_2 = DummyTensor()
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node_module.Node(layer=dense, call_args=(a,), outputs=a_2)
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b_2 = DummyTensor()
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node_module.Node(layer=dense, call_args=(b,), outputs=b_2)
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concat_layer = DummyLayer()
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merged = DummyTensor()
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node_module.Node(layer=concat_layer, call_args=([a_2, b_2],),
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outputs=merged)
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merge_layer, merge_node_index, merge_tensor_index = merged._keras_history
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self.assertEqual(merge_node_index, 0)
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self.assertEqual(merge_tensor_index, 0)
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self.assertLen(merge_layer._inbound_nodes, 1)
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self.assertLen(merge_layer._outbound_nodes, 0)
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self.assertLen(merge_layer._inbound_nodes[0].input_tensors, 2)
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self.assertEqual(merge_layer._inbound_nodes[0].input_tensors, [a_2, b_2])
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self.assertLen(merge_layer._inbound_nodes[0].inbound_layers, 2)
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def test_arg_and_kwarg_mix(self):
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input_layer = DummyLayer()
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input_layer_2 = DummyLayer()
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a = DummyTensor()
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node_a = node_module.Node(layer=input_layer, outputs=a)
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b = DummyTensor()
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node_b = node_module.Node(layer=input_layer_2, outputs=b)
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arg_2 = DummyTensor()
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arg_3 = DummyTensor()
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node_c = node_module.Node(layer=input_layer, outputs=arg_3)
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kwarg_x = DummyTensor()
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kwarg_y = DummyTensor()
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node_d = node_module.Node(layer=input_layer, outputs=kwarg_y)
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merge_layer = DummyLayer()
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merged = DummyTensor()
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node = node_module.Node(layer=merge_layer,
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call_args=([a, b], arg_2, arg_3),
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call_kwargs={'x': kwarg_x, 'y': kwarg_y},
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outputs=merged)
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merge_layer, merge_node_index, merge_tensor_index = merged._keras_history
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# Check the saved call args/kwargs
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self.assertEqual(([a, b], arg_2, arg_3), node.call_args)
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self.assertEqual({'x': kwarg_x, 'y': kwarg_y}, node.call_kwargs)
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# Only the inputs that were produced by input nodes should appear in
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# keras_tensors
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self.assertEqual({a, b, arg_3, kwarg_y}, set(node.keras_inputs))
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self.assertEqual(set(node.parent_nodes), {node_a, node_b, node_c, node_d})
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# Check the layer wirings
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self.assertEqual(merge_node_index, 0)
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self.assertEqual(merge_tensor_index, 0)
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self.assertLen(merge_layer._inbound_nodes, 1)
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self.assertLen(merge_layer._outbound_nodes, 0)
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self.assertLen(input_layer._outbound_nodes, 3)
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self.assertLen(input_layer_2._outbound_nodes, 1)
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# The 'backwards compatibility' attributes should only check the
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# first call argument
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self.assertLen(merge_layer._inbound_nodes[0].input_tensors, 2)
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self.assertEqual(merge_layer._inbound_nodes[0].input_tensors, [a, b])
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self.assertLen(merge_layer._inbound_nodes[0].inbound_layers, 2)
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if __name__ == '__main__':
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test.main()
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