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| # Copyright 2016 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 grl_ops.""" | |
| #from models.domain_adaptation.domain_separation import grl_op_grads # pylint: disable=unused-import | |
| #from models.domain_adaptation.domain_separation import grl_op_shapes # pylint: disable=unused-import | |
| import tensorflow as tf | |
| import grl_op_grads | |
| import grl_ops | |
| FLAGS = tf.app.flags.FLAGS | |
| class GRLOpsTest(tf.test.TestCase): | |
| def testGradientReversalOp(self): | |
| with tf.Graph().as_default(): | |
| with self.test_session(): | |
| # Test that in forward prop, gradient reversal op acts as the | |
| # identity operation. | |
| examples = tf.constant([5.0, 4.0, 3.0, 2.0, 1.0]) | |
| output = grl_ops.gradient_reversal(examples) | |
| expected_output = examples | |
| self.assertAllEqual(output.eval(), expected_output.eval()) | |
| # Test that shape inference works as expected. | |
| self.assertAllEqual(output.get_shape(), expected_output.get_shape()) | |
| # Test that in backward prop, gradient reversal op multiplies | |
| # gradients by -1. | |
| examples = tf.constant([[1.0]]) | |
| w = tf.get_variable(name='w', shape=[1, 1]) | |
| b = tf.get_variable(name='b', shape=[1]) | |
| init_op = tf.global_variables_initializer() | |
| init_op.run() | |
| features = tf.nn.xw_plus_b(examples, w, b) | |
| # Construct two outputs: features layer passes directly to output1, but | |
| # features layer passes through a gradient reversal layer before | |
| # reaching output2. | |
| output1 = features | |
| output2 = grl_ops.gradient_reversal(features) | |
| gold = tf.constant([1.0]) | |
| loss1 = gold - output1 | |
| loss2 = gold - output2 | |
| opt = tf.train.GradientDescentOptimizer(learning_rate=0.01) | |
| grads_and_vars_1 = opt.compute_gradients(loss1, | |
| tf.trainable_variables()) | |
| grads_and_vars_2 = opt.compute_gradients(loss2, | |
| tf.trainable_variables()) | |
| self.assertAllEqual(len(grads_and_vars_1), len(grads_and_vars_2)) | |
| for i in range(len(grads_and_vars_1)): | |
| g1 = grads_and_vars_1[i][0] | |
| g2 = grads_and_vars_2[i][0] | |
| # Verify that gradients of loss1 are the negative of gradients of | |
| # loss2. | |
| self.assertAllEqual(tf.negative(g1).eval(), g2.eval()) | |
| if __name__ == '__main__': | |
| tf.test.main() | |