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"""Tests for RNN cells."""
# pylint: disable=g-bad-import-order,unused-import
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
class RNNCellTest(tf.test.TestCase):
def testBasicRNNCell(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 2])
g, _ = rnn_cell.BasicRNNCell(2)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g], {x.name: np.array([[1., 1.]]),
m.name: np.array([[0.1, 0.1]])})
self.assertEqual(res[0].shape, (1, 2))
def testGRUCell(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 2])
g, _ = rnn_cell.GRUCell(2)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g], {x.name: np.array([[1., 1.]]),
m.name: np.array([[0.1, 0.1]])})
# Smoke test
self.assertAllClose(res[0], [[0.175991, 0.175991]])
def testBasicLSTMCell(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 8])
g, out_m = rnn_cell.MultiRNNCell([rnn_cell.BasicLSTMCell(2)] * 2)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g, out_m], {x.name: np.array([[1., 1.]]),
m.name: 0.1 * np.ones([1, 8])})
self.assertEqual(len(res), 2)
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.24024698, 0.24024698]])
expected_mem = np.array([[0.68967271, 0.68967271,
0.44848421, 0.44848421,
0.39897051, 0.39897051,
0.24024698, 0.24024698]])
self.assertAllClose(res[1], expected_mem)
def testLSTMCell(self):
with self.test_session() as sess:
num_units = 8
num_proj = 6
state_size = num_units + num_proj
batch_size = 3
input_size = 2
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([batch_size, input_size])
m = tf.zeros([batch_size, state_size])
output, state = rnn_cell.LSTMCell(
num_units=num_units, input_size=input_size, num_proj=num_proj)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([output, state],
{x.name: np.array([[1., 1.], [2., 2.], [3., 3.]]),
m.name: 0.1 * np.ones((batch_size, state_size))})
self.assertEqual(len(res), 2)
# The numbers in results were not calculated, this is mostly just a
# smoke test.
self.assertEqual(res[0].shape, (batch_size, num_proj))
self.assertEqual(res[1].shape, (batch_size, state_size))
# Different inputs so different outputs and states
for i in range(1, batch_size):
self.assertTrue(
float(np.linalg.norm((res[0][0,:] - res[0][i,:]))) > 1e-6)
self.assertTrue(
float(np.linalg.norm((res[1][0,:] - res[1][i,:]))) > 1e-6)
def testOutputProjectionWrapper(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 3])
cell = rnn_cell.OutputProjectionWrapper(rnn_cell.GRUCell(3), 2)
g, new_m = cell(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g, new_m], {x.name: np.array([[1., 1., 1.]]),
m.name: np.array([[0.1, 0.1, 0.1]])})
self.assertEqual(res[1].shape, (1, 3))
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.231907, 0.231907]])
def testInputProjectionWrapper(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 3])
cell = rnn_cell.InputProjectionWrapper(rnn_cell.GRUCell(3), 2)
g, new_m = cell(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g, new_m], {x.name: np.array([[1., 1.]]),
m.name: np.array([[0.1, 0.1, 0.1]])})
self.assertEqual(res[1].shape, (1, 3))
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.154605, 0.154605, 0.154605]])
def testDropoutWrapper(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 3])
m = tf.zeros([1, 3])
keep = tf.zeros([1]) + 1
g, new_m = rnn_cell.DropoutWrapper(rnn_cell.GRUCell(3),
keep, keep)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g, new_m], {x.name: np.array([[1., 1., 1.]]),
m.name: np.array([[0.1, 0.1, 0.1]])})
self.assertEqual(res[1].shape, (1, 3))
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.154605, 0.154605, 0.154605]])
def testEmbeddingWrapper(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 1], dtype=tf.int32)
m = tf.zeros([1, 2])
g, new_m = rnn_cell.EmbeddingWrapper(rnn_cell.GRUCell(2), 3)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run([g, new_m], {x.name: np.array([[1]]),
m.name: np.array([[0.1, 0.1]])})
self.assertEqual(res[1].shape, (1, 2))
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.17139, 0.17139]])
def testMultiRNNCell(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
x = tf.zeros([1, 2])
m = tf.zeros([1, 4])
_, ml = rnn_cell.MultiRNNCell([rnn_cell.GRUCell(2)] * 2)(x, m)
sess.run([tf.variables.initialize_all_variables()])
res = sess.run(ml, {x.name: np.array([[1., 1.]]),
m.name: np.array([[0.1, 0.1, 0.1, 0.1]])})
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res, [[0.175991, 0.175991,
0.13248, 0.13248]])
if __name__ == "__main__":
tf.test.main()
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