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| # Copyright 2017 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 LSTM tensorflow blocks.""" | |
| from __future__ import division | |
| import numpy as np | |
| import tensorflow as tf | |
| import block_base | |
| import blocks_std | |
| import blocks_lstm | |
| class BlocksLSTMTest(tf.test.TestCase): | |
| def CheckUnary(self, y, op_type): | |
| self.assertEqual(op_type, y.op.type) | |
| self.assertEqual(1, len(y.op.inputs)) | |
| return y.op.inputs[0] | |
| def CheckBinary(self, y, op_type): | |
| self.assertEqual(op_type, y.op.type) | |
| self.assertEqual(2, len(y.op.inputs)) | |
| return y.op.inputs | |
| def testLSTM(self): | |
| lstm = blocks_lstm.LSTM(10) | |
| lstm.hidden = tf.zeros(shape=[10, 10], dtype=tf.float32) | |
| lstm.cell = tf.zeros(shape=[10, 10], dtype=tf.float32) | |
| x = tf.placeholder(dtype=tf.float32, shape=[10, 11]) | |
| y = lstm(x) | |
| o, tanhc = self.CheckBinary(y, 'Mul') | |
| self.assertEqual(self.CheckUnary(o, 'Sigmoid').name, 'LSTM/split:3') | |
| self.assertIs(lstm.cell, self.CheckUnary(tanhc, 'Tanh')) | |
| fc, ij = self.CheckBinary(lstm.cell, 'Add') | |
| f, _ = self.CheckBinary(fc, 'Mul') | |
| self.assertEqual(self.CheckUnary(f, 'Sigmoid').name, 'LSTM/split:0') | |
| i, j = self.CheckBinary(ij, 'Mul') | |
| self.assertEqual(self.CheckUnary(i, 'Sigmoid').name, 'LSTM/split:1') | |
| j = self.CheckUnary(j, 'Tanh') | |
| self.assertEqual(j.name, 'LSTM/split:2') | |
| def testLSTMBiasInit(self): | |
| lstm = blocks_lstm.LSTM(9) | |
| x = tf.placeholder(dtype=tf.float32, shape=[15, 7]) | |
| lstm(x) | |
| b = lstm._nn._bias | |
| with self.test_session(): | |
| tf.global_variables_initializer().run() | |
| bias_var = b._bias.eval() | |
| comp = ([1.0] * 9) + ([0.0] * 27) | |
| self.assertAllEqual(bias_var, comp) | |
| def testConv2DLSTM(self): | |
| lstm = blocks_lstm.Conv2DLSTM(depth=10, | |
| filter_size=[1, 1], | |
| hidden_filter_size=[1, 1], | |
| strides=[1, 1], | |
| padding='SAME') | |
| lstm.hidden = tf.zeros(shape=[10, 11, 11, 10], dtype=tf.float32) | |
| lstm.cell = tf.zeros(shape=[10, 11, 11, 10], dtype=tf.float32) | |
| x = tf.placeholder(dtype=tf.float32, shape=[10, 11, 11, 1]) | |
| y = lstm(x) | |
| o, tanhc = self.CheckBinary(y, 'Mul') | |
| self.assertEqual(self.CheckUnary(o, 'Sigmoid').name, 'Conv2DLSTM/split:3') | |
| self.assertIs(lstm.cell, self.CheckUnary(tanhc, 'Tanh')) | |
| fc, ij = self.CheckBinary(lstm.cell, 'Add') | |
| f, _ = self.CheckBinary(fc, 'Mul') | |
| self.assertEqual(self.CheckUnary(f, 'Sigmoid').name, 'Conv2DLSTM/split:0') | |
| i, j = self.CheckBinary(ij, 'Mul') | |
| self.assertEqual(self.CheckUnary(i, 'Sigmoid').name, 'Conv2DLSTM/split:1') | |
| j = self.CheckUnary(j, 'Tanh') | |
| self.assertEqual(j.name, 'Conv2DLSTM/split:2') | |
| def testConv2DLSTMBiasInit(self): | |
| lstm = blocks_lstm.Conv2DLSTM(9, 1, 1, [1, 1], 'SAME') | |
| x = tf.placeholder(dtype=tf.float32, shape=[1, 7, 7, 7]) | |
| lstm(x) | |
| b = lstm._bias | |
| with self.test_session(): | |
| tf.global_variables_initializer().run() | |
| bias_var = b._bias.eval() | |
| comp = ([1.0] * 9) + ([0.0] * 27) | |
| self.assertAllEqual(bias_var, comp) | |
| if __name__ == '__main__': | |
| tf.test.main() | |