113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
# 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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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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import tensorflow as tf
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class TestKerasModelClass(tf.keras.Model):
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"""A simple tensorflow keras Model class definition."""
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def __init__(self, width):
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super(TestKerasModelClass, self).__init__()
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self.width = width
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def build(self, input_shape):
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self.weight = self.add_weight(
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name="test_keras_var",
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shape=(self.width,),
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dtype=tf.float32,
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trainable=True,
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)
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def call(self, inputs):
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return self.weight * inputs
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class GradientsTest(tf.test.TestCase):
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def _TestVariablesGradient(self, inputs, test_model, vars_to_grad):
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"""Returns gradients of `test_model` with respect to `vars_to_grad`."""
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test_model_re = tf.recompute_grad(test_model)
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with tf.GradientTape(persistent=True) as tape:
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tape.watch(vars_to_grad)
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out_re = test_model_re(inputs)
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out = test_model(inputs)
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grads_re = tape.gradient(out_re, vars_to_grad)
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grads = tape.gradient(out, vars_to_grad)
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return grads_re, grads
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def testKerasRecompute(self):
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"""Checks that recompute_grad works for a simple Keras Model."""
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test_model = TestKerasModelClass(10)
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test_input = tf.constant(tf.zeros((10, 10), dtype=np.float32))
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# Ensures keras model is initialized.
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test_model(test_input) # pylint: disable=not-callable
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grads_re, grads = self._TestVariablesGradient(test_input, test_model,
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test_input)
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grads_re = self.evaluate(grads_re)
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grads = self.evaluate(grads)
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for g, g_re in zip(grads, grads_re):
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self.assertAllClose(g, g_re)
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grads_re, grads = self._TestVariablesGradient(test_input, test_model,
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test_model.variables)
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grads_re = self.evaluate(grads_re)
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grads = self.evaluate(grads)
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for g, g_re in zip(grads, grads_re):
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self.assertAllClose(g, g_re)
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def testLSTMBatchJacobian(self):
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class HasLSTM(tf.keras.Model):
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def __init__(self):
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super(HasLSTM, self).__init__()
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self.lstm = tf.keras.layers.LSTM(units=5)
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self.dense = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)
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def call(self, x):
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return self.dense(self.lstm(x))
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m = HasLSTM()
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def jacobian(x):
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with tf.GradientTape() as tape:
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tape.watch(x)
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y = m(x) # pylint: disable=not-callable
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return tape.batch_jacobian(y, x)
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inp = tf.nn.l2_normalize(tf.ones([1, 2, 3]), axis=[1, 2])
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eager_result = jacobian(inp)
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function_result = tf.function(jacobian)(inp)
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self.assertAllClose(eager_result, function_result)
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backprop_result, numeric_result = tf.test.compute_gradient(
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m, [inp], delta=1e-3)
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self.assertAllClose(numeric_result, backprop_result, rtol=1e-2)
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self.assertAllClose(tf.reshape(numeric_result, [-1]),
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tf.reshape(eager_result, [-1]), rtol=1e-2)
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if __name__ == "__main__":
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tf.test.main()
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