def rnn_cell(rnn_cell_size, dropout_keep_prob, residual, is_training=True):
"""Builds an LSTMBlockCell based on the given parameters."""
dropout_keep_prob = dropout_keep_prob if is_training else 1.0
cells = []
for i in range(len(rnn_cell_size)):
cell = contrib_rnn.LSTMBlockCell(rnn_cell_size[i])
if residual:
cell = rnn.ResidualWrapper(cell)
if i == 0 or rnn_cell_size[i] != rnn_cell_size[i - 1]:
cell = contrib_rnn.InputProjectionWrapper(
cell, rnn_cell_size[i])
cell = rnn.DropoutWrapper(cell, input_keep_prob=dropout_keep_prob)
cells.append(cell)
return rnn.MultiRNNCell(cells)
def testInputProjectionWrapper(self):
with self.cached_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 = contrib_rnn.InputProjectionWrapper(rnn_cell.GRUCell(3),
num_proj=3)
g, new_m = cell(x, m)
sess.run([tf.global_variables_initializer()])
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 make_rnn_cell(rnn_layer_sizes,
dropout_keep_prob=1.0,
attn_length=0,
base_cell=rnn.BasicLSTMCell,
residual_connections=False):
"""Makes a RNN cell from the given hyperparameters.
Args:
rnn_layer_sizes: A list of integer sizes (in units) for each layer of the
RNN.
dropout_keep_prob: The float probability to keep the output of any given
sub-cell.
attn_length: The size of the attention vector.
base_cell: The base rnn.RNNCell to use for sub-cells.
residual_connections: Whether or not to use residual connections (via
rnn.ResidualWrapper).
Returns:
A rnn.MultiRNNCell based on the given hyperparameters.
"""
cells = []
for i in range(len(rnn_layer_sizes)):
cell = base_cell(rnn_layer_sizes[i])
if attn_length and not cells:
# Add attention wrapper to first layer.
cell = contrib_rnn.AttentionCellWrapper(cell,
attn_length,
state_is_tuple=True)
if residual_connections:
cell = rnn.ResidualWrapper(cell)
if i == 0 or rnn_layer_sizes[i] != rnn_layer_sizes[i - 1]:
cell = contrib_rnn.InputProjectionWrapper(
cell, rnn_layer_sizes[i])
cell = rnn.DropoutWrapper(cell, output_keep_prob=dropout_keep_prob)
cells.append(cell)
cell = rnn.MultiRNNCell(cells)
return cell