def _rnn_template(incoming, cell, dropout=None, return_seq=False,
return_state=False, initial_state=None, dynamic=False,
scope=None, name="LSTM"):
""" RNN Layer Template. """
sequence_length = None
if dynamic:
sequence_length = retrieve_seq_length_op(
incoming if isinstance(incoming, tf.Tensor) else tf.pack(incoming))
input_shape = utils.get_incoming_shape(incoming)
with tf.variable_op_scope([incoming], scope, name) as scope:
name = scope.name
_cell = cell
# Apply dropout
if dropout:
if type(dropout) in [tuple, list]:
in_keep_prob = dropout[0]
out_keep_prob = dropout[1]
elif isinstance(dropout, float):
in_keep_prob, out_keep_prob = dropout, dropout
else:
raise Exception("Invalid dropout type (must be a 2-D tuple of "
"float)")
cell = DropoutWrapper(cell, in_keep_prob, out_keep_prob)
inference = incoming
# If a tensor given, convert it to a per timestep list
if type(inference) not in [list, np.array]:
ndim = len(input_shape)
assert ndim >= 3, "Input dim should be at least 3."
axes = [1, 0] + list(range(2, ndim))
inference = tf.transpose(inference, (axes))
inference = tf.unpack(inference)
outputs, state = _rnn(cell, inference, dtype=tf.float32,
initial_state=initial_state, scope=name,
sequence_length=sequence_length)
# Retrieve RNN Variables
c = tf.GraphKeys.LAYER_VARIABLES + '/' + scope.name
for v in [_cell.W, _cell.b]:
if hasattr(v, "__len__"):
for var in v: tf.add_to_collection(c, var)
else:
tf.add_to_collection(c, v)
# Track activations.
tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, outputs[-1])
if dynamic:
outputs = tf.transpose(tf.pack(outputs), [1, 0, 2])
o = advanced_indexing_op(outputs, sequence_length)
else:
o = outputs if return_seq else outputs[-1]
# Track output tensor.
tf.add_to_collection(tf.GraphKeys.LAYER_TENSOR + '/' + name, o)
return (o, state) if return_state else o
def _rnn_template(incoming,
cell,
dropout=None,
return_seq=False,
return_state=False,
initial_state=None,
dynamic=False,
scope=None,
name="LSTM"):
""" RNN Layer Template. """
sequence_length = None
if dynamic:
sequence_length = retrieve_seq_length_op(
incoming if isinstance(incoming, tf.Tensor) else tf.pack(incoming))
input_shape = utils.get_incoming_shape(incoming)
with tf.variable_scope(scope, name, values=[incoming]) as scope:
name = scope.name
_cell = cell
# Apply dropout
if dropout:
if type(dropout) in [tuple, list]:
in_keep_prob = dropout[0]
out_keep_prob = dropout[1]
elif isinstance(dropout, float):
in_keep_prob, out_keep_prob = dropout, dropout
else:
raise Exception("Invalid dropout type (must be a 2-D tuple of "
"float)")
cell = DropoutWrapper(cell, in_keep_prob, out_keep_prob)
inference = incoming
# If a tensor given, convert it to a per timestep list
if type(inference) not in [list, np.array]:
ndim = len(input_shape)
assert ndim >= 3, "Input dim should be at least 3."
axes = [1, 0] + list(range(2, ndim))
inference = tf.transpose(inference, (axes))
inference = tf.unpack(inference)
outputs, state = _rnn(cell,
inference,
dtype=tf.float32,
initial_state=initial_state,
scope=name,
sequence_length=sequence_length)
# Retrieve RNN Variables
c = tf.GraphKeys.LAYER_VARIABLES + '/' + scope.name
for v in [_cell.W, _cell.b]:
if hasattr(v, "__len__"):
for var in v:
tf.add_to_collection(c, var)
else:
tf.add_to_collection(c, v)
# Track activations.
tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, outputs[-1])
if dynamic:
if return_seq:
o = outputs
else:
outputs = tf.transpose(tf.pack(outputs), [1, 0, 2])
o = advanced_indexing_op(outputs, sequence_length)
else:
o = outputs if return_seq else outputs[-1]
# Track output tensor.
tf.add_to_collection(tf.GraphKeys.LAYER_TENSOR + '/' + name, o)
return (o, state) if return_state else o