def _LSTMLayer(self, prev_layer, direction, dim, summarize, depth, name):
"""Adds an LSTM layer with the given pre-parsed attributes.
Always maps 4-D to 4-D regardless of summarize.
Args:
prev_layer: Input tensor.
direction: 'forward' 'backward' or 'bidirectional'
dim: 'x' or 'y', dimension to consider as time.
summarize: True if we are to return only the last timestep.
depth: Output depth.
name: Some string naming the op.
Returns:
Output tensor.
"""
# If the target dimension is y, we need to transpose.
if dim == 'x':
lengths = self.GetLengths(2, 1)
inputs = prev_layer
else:
lengths = self.GetLengths(1, 1)
inputs = tf.transpose(prev_layer, [0, 2, 1, 3], name=name + '_ytrans_in')
input_batch = shapes.tensor_dim(inputs, 0)
num_slices = shapes.tensor_dim(inputs, 1)
num_steps = shapes.tensor_dim(inputs, 2)
input_depth = shapes.tensor_dim(inputs, 3)
# Reshape away the other dimension.
inputs = tf.reshape(
inputs, [-1, num_steps, input_depth], name=name + '_reshape_in')
# We need to replicate the lengths by the size of the other dimension, and
# any changes that have been made to the batch dimension.
tile_factor = tf.to_float(input_batch *
num_slices) / tf.to_float(tf.shape(lengths)[0])
lengths = tf.tile(lengths, [tf.cast(tile_factor, tf.int32)])
lengths = tf.cast(lengths, tf.int64)
outputs = nn_ops.rnn_helper(
inputs,
lengths,
cell_type='lstm',
num_nodes=depth,
direction=direction,
name=name,
stddev=0.1)
# Output depth is doubled if bi-directional.
if direction == 'bidirectional':
output_depth = depth * 2
else:
output_depth = depth
# Restore the other dimension.
if summarize:
outputs = tf.slice(
outputs, [0, num_steps - 1, 0], [-1, 1, -1], name=name + '_sum_slice')
outputs = tf.reshape(
outputs, [input_batch, num_slices, 1, output_depth],
name=name + '_reshape_out')
else:
outputs = tf.reshape(
outputs, [input_batch, num_slices, num_steps, output_depth],
name=name + '_reshape_out')
if dim == 'y':
outputs = tf.transpose(outputs, [0, 2, 1, 3], name=name + '_ytrans_out')
return outputs
def _LSTMLayer(self, prev_layer, direction, dim, summarize, depth, name):
"""Adds an LSTM layer with the given pre-parsed attributes.
Always maps 4-D to 4-D regardless of summarize.
Args:
prev_layer: Input tensor.
direction: 'forward' 'backward' or 'bidirectional'
dim: 'x' or 'y', dimension to consider as time.
summarize: True if we are to return only the last timestep.
depth: Output depth.
name: Some string naming the op.
Returns:
Output tensor.
"""
# If the target dimension is y, we need to transpose.
if dim == 'x':
lengths = self.GetLengths(2, 1)
inputs = prev_layer
else:
lengths = self.GetLengths(1, 1)
inputs = tf.transpose(prev_layer, [0, 2, 1, 3],
name=name + '_ytrans_in')
input_batch = shapes.tensor_dim(inputs, 0)
num_slices = shapes.tensor_dim(inputs, 1)
num_steps = shapes.tensor_dim(inputs, 2)
input_depth = shapes.tensor_dim(inputs, 3)
# Reshape away the other dimension.
inputs = tf.reshape(inputs, [-1, num_steps, input_depth],
name=name + '_reshape_in')
# We need to replicate the lengths by the size of the other dimension, and
# any changes that have been made to the batch dimension.
tile_factor = tf.to_float(input_batch * num_slices) / tf.to_float(
tf.shape(lengths)[0])
lengths = tf.tile(lengths, [tf.cast(tile_factor, tf.int32)])
lengths = tf.cast(lengths, tf.int64)
outputs = nn_ops.rnn_helper(inputs,
lengths,
cell_type='lstm',
num_nodes=depth,
direction=direction,
name=name,
stddev=0.1)
# Output depth is doubled if bi-directional.
if direction == 'bidirectional':
output_depth = depth * 2
else:
output_depth = depth
# Restore the other dimension.
if summarize:
outputs = tf.slice(outputs, [0, num_steps - 1, 0], [-1, 1, -1],
name=name + '_sum_slice')
outputs = tf.reshape(outputs,
[input_batch, num_slices, 1, output_depth],
name=name + '_reshape_out')
else:
outputs = tf.reshape(
outputs, [input_batch, num_slices, num_steps, output_depth],
name=name + '_reshape_out')
if dim == 'y':
outputs = tf.transpose(outputs, [0, 2, 1, 3],
name=name + '_ytrans_out')
return outputs