def test_static_dropout_lstm_cell():
sess = tf.Session()
x = np.random.randn(1, 10, 50).astype(np.float32)
with sess.graph.as_default():
with tf.variable_scope("DropoutIsOn"):
rnn_drop_cell = lstm_cell_w_dropout(100,
0.9999999999,
training=True)
rnn_drop, _ = tf.nn.dynamic_rnn(rnn_drop_cell,
x,
sequence_length=np.array(
[10], dtype=np.int),
dtype=tf.float32)
with tf.variable_scope("DropoutIsOff"):
rnn_no_drop_cell = lstm_cell_w_dropout(100,
0.9999999999,
training=False)
rnn_no_drop, _ = tf.nn.dynamic_rnn(rnn_no_drop_cell,
x,
sequence_length=np.array(
[10], dtype=np.int),
dtype=tf.float32)
sess.run(tf.global_variables_initializer())
out_ten = sess.run(rnn_drop)
assert len(out_ten[np.nonzero(out_ten)].squeeze()) < 20
out_ten = sess.run(rnn_no_drop)
assert len(out_ten[np.nonzero(out_ten)].squeeze()) > 20
def pool(self, word_embeddings, dsz, init, **kwargs):
"""LSTM with dropout yielding a final-state as output
:param word_embeddings: The input word embeddings
:param dsz: The input word embedding depth
:param init: The tensorflow initializer to use (currently ignored)
:param kwargs: See below
:Keyword Arguments:
* *hsz* -- (``int``) The number of hidden units (defaults to `100`)
* *cmotsz* -- (``int``) An alias for `hsz`
:return:
"""
hsz = kwargs.get('rnnsz', kwargs.get('hsz', 100))
if type(hsz) is list:
hsz = hsz[0]
char_rnnfwd = lstm_cell_w_dropout(hsz, self.pkeep)
rnnout, final_state = tf.nn.dynamic_rnn(char_rnnfwd,
word_embeddings,
dtype=tf.float32,
sequence_length=self.lengths)
output_state = final_state.h
combine = tf.reshape(output_state, [-1, hsz])
return combine
def test_static_dropout_lstm_cell():
with tf.device('/cpu:0'):
sess = tf.Session()
x = np.random.randn(1, 10, 50).astype(np.float32)
with sess.graph.as_default():
with tf.variable_scope("DropoutIsOn"):
rnn_drop_cell = lstm_cell_w_dropout(100, 0.9999999999, training=True)
rnn_drop, _ = tf.nn.dynamic_rnn(rnn_drop_cell, x, sequence_length=np.array([10], dtype=np.int), dtype=tf.float32)
with tf.variable_scope("DropoutIsOff"):
rnn_no_drop_cell = lstm_cell_w_dropout(100, 0.9999999999, training=False)
rnn_no_drop, _ = tf.nn.dynamic_rnn(rnn_no_drop_cell, x, sequence_length=np.array([10], dtype=np.int), dtype=tf.float32)
sess.run(tf.global_variables_initializer())
out_ten = sess.run(rnn_drop)
assert len(out_ten[np.nonzero(out_ten)].squeeze()) < 20
out_ten = sess.run(rnn_no_drop)
assert len(out_ten[np.nonzero(out_ten)].squeeze()) > 20
def test_placeholder_dropout_lstm_cell():
sess = tf.Session()
x = np.random.randn(1, 10, 50).astype(np.float32)
with sess.graph.as_default():
train_flag = tf.placeholder_with_default(False, shape=(), name='TEST_TRAIN_FLAG')
with tf.variable_scope("DropoutMightBeOn"):
rnn_cell = lstm_cell_w_dropout(100, 0.9999999999, training=train_flag)
rnn, _ = tf.nn.dynamic_rnn(rnn_cell, x, sequence_length=np.array([10], dtype=np.int), dtype=tf.float32)
sess.run(tf.global_variables_initializer())
out_ten = sess.run(rnn, {train_flag: True})
assert len(out_ten[np.nonzero(out_ten)].squeeze()) < 20
out_ten = sess.run(rnn)
assert len(out_ten[np.nonzero(out_ten)].squeeze()) > 20
def test_placeholder_dropout_lstm_cell():
with tf.device('/cpu:0'):
sess = tf.Session()
x = np.random.randn(1, 10, 50).astype(np.float32)
with sess.graph.as_default():
train_flag = tf.placeholder_with_default(False, shape=(), name='TEST_TRAIN_FLAG')
with tf.variable_scope("DropoutMightBeOn"):
rnn_cell = lstm_cell_w_dropout(100, 0.9999999999, training=train_flag)
rnn, _ = tf.nn.dynamic_rnn(rnn_cell, x, sequence_length=np.array([10], dtype=np.int), dtype=tf.float32)
sess.run(tf.global_variables_initializer())
out_ten = sess.run(rnn, {train_flag: True})
assert len(out_ten[np.nonzero(out_ten)].squeeze()) < 20
out_ten = sess.run(rnn)
assert len(out_ten[np.nonzero(out_ten)].squeeze()) > 20