def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the MultiSkipGRUCell
:param batch_size: number of samples per batch
:return: list of tensors and SkipGRUStateTuple
"""
initial_states = []
for idx in range(self._num_layers - 1):
with tf.variable_scope('layer_%d' % (idx + 1)):
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(
batch_size, self._num_units[idx])
initial_states.append(initial_h)
with tf.variable_scope('layer_%d' % self._num_layers):
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(
batch_size, self._num_units[-1])
with tf.variable_scope('initial_update_prob'):
initial_update_prob = rnn_ops.create_initial_state(
batch_size,
1,
trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope('initial_cum_update_prob'):
initial_cum_update_prob = rnn_ops.create_initial_state(
batch_size,
1,
trainable=False,
initializer=tf.zeros_initializer())
initial_states.append(
SkipGRUStateTuple(initial_h, initial_update_prob,
initial_cum_update_prob))
return initial_states
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the SkipLSTMCell
:param batch_size: number of samples per batch
:return: SkipLSTMStateTuple
"""
print("SkipLSTMCell_trainable_initial_state_called", batch_size)
with tf.variable_scope(f'initial_c_{self.layer}'):
initial_c = rnn_ops.create_initial_state(batch_size,
self._num_units)
with tf.variable_scope(f'initial_h_{self.layer}'):
initial_h = rnn_ops.create_initial_state(batch_size,
self._num_units)
with tf.variable_scope(f'initial_update_prob_{self.layer}'):
initial_update_prob = rnn_ops.create_initial_state(
batch_size,
1,
trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope(f'initial_cum_update_prob_{self.layer}'):
initial_cum_update_prob = rnn_ops.create_initial_state(
batch_size,
1,
trainable=False,
initializer=tf.zeros_initializer())
return SkipLSTMStateTuple(initial_c, initial_h, initial_update_prob,
initial_cum_update_prob)
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the BasicGRUCell
:param batch_size: number of samples per batch
:return: tensor with shape [batch_size, self.state_size]
"""
with tf.variable_scope('basic_initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size,
self._num_units)
return initial_h
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the SkipGRUCell
:param batch_size: number of samples per batch
:return: SkipGRUStateTuple
"""
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size,
self._num_units)
with tf.variable_scope('initial_update_prob'):
initial_update_prob = rnn_ops.create_initial_state(
batch_size,
1,
trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope('initial_cum_update_prob'):
initial_cum_update_prob = rnn_ops.create_initial_state(
batch_size,
1,
trainable=False,
initializer=tf.zeros_initializer())
return SkipGRUStateTuple(initial_h, initial_update_prob,
initial_cum_update_prob)