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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()
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