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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 creating different number of masks in rnn_cells."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.model_pruning.python import pruning
from tensorflow.contrib.model_pruning.python.layers import rnn_cells
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import rnn_cell as tf_rnn_cells
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class RnnCellsTest(test.TestCase):
def setUp(self):
super(RnnCellsTest, self).setUp()
self.batch_size = 8
self.dim = 10
def testMaskedBasicLSTMCell(self):
expected_num_masks = 1
expected_num_rows = 2 * self.dim
expected_num_cols = 4 * self.dim
with self.test_session():
inputs = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
c = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
h = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
state = tf_rnn_cells.LSTMStateTuple(c, h)
lstm_cell = rnn_cells.MaskedBasicLSTMCell(self.dim)
lstm_cell(inputs, state)
self.assertEqual(len(pruning.get_masks()), expected_num_masks)
self.assertEqual(len(pruning.get_masked_weights()), expected_num_masks)
self.assertEqual(len(pruning.get_thresholds()), expected_num_masks)
self.assertEqual(len(pruning.get_weights()), expected_num_masks)
for mask in pruning.get_masks():
self.assertEqual(mask.shape, (expected_num_rows, expected_num_cols))
for weight in pruning.get_weights():
self.assertEqual(weight.shape, (expected_num_rows, expected_num_cols))
def testMaskedLSTMCell(self):
expected_num_masks = 1
expected_num_rows = 2 * self.dim
expected_num_cols = 4 * self.dim
with self.test_session():
inputs = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
c = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
h = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
state = tf_rnn_cells.LSTMStateTuple(c, h)
lstm_cell = rnn_cells.MaskedLSTMCell(self.dim)
lstm_cell(inputs, state)
self.assertEqual(len(pruning.get_masks()), expected_num_masks)
self.assertEqual(len(pruning.get_masked_weights()), expected_num_masks)
self.assertEqual(len(pruning.get_thresholds()), expected_num_masks)
self.assertEqual(len(pruning.get_weights()), expected_num_masks)
for mask in pruning.get_masks():
self.assertEqual(mask.shape, (expected_num_rows, expected_num_cols))
for weight in pruning.get_weights():
self.assertEqual(weight.shape, (expected_num_rows, expected_num_cols))
if __name__ == '__main__':
test.main()
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