def __call__(self, inputs, state, scope=None):
""" """
with tf.variable_scope(scope or type(self).__name__):
cell_tm1, hidden_tm1 = tf.split(state, 2, axis=1)
input_list = [inputs, hidden_tm1]
with tf.variable_scope('Gates'):
gates = linear(inputs_list,
self.output_size,
add_bias=True,
n_splits=2,
moving_params=self.moving_params)
update_act, reset_act = gates
update_gate = gate(update_act-self.forget_bias)
reset_gate = gate(reset_act)
reset_state = reset_gate * hidden_tm1
input_list = [inputs, reset_state]
with tf.variable_scope('Candidate'):
hidden_act = linear(input_list,
self.output_size,
add_bias=True,
moving_params=self.moving_params)
hidden_tilde = self.recur_func(hidden_act)
cell_t = update_gate * cell_tm1 + (1-update_gate) * hidden_tilde
return cell_t, tf.concat([cell_t, cell_t], 1)
def __call__(self, inputs, state, scope=None):
""" """
with tf.variable_scope(scope or type(self).__name__):
cell_tm1, hidden_tm1 = tf.split(state, 2, axis=1)
input_list = [inputs, hidden_tm1]
lin = linear(input_list,
self.output_size,
add_bias=True,
n_splits=3,
moving_params=self.moving_params)
cell_act, update_act, output_act = lin
cell_tilde_t = cell_act
update_gate = gate(update_act - self.forget_bias)
output_gate = gate(output_act)
cell_t = update_gate * cell_tilde_t + (1 - update_gate) * cell_tm1
hidden_tilde_t = self.recur_func(cell_t)
hidden_t = hidden_tilde_t * output_gate
return hidden_t, tf.concat([cell_t, hidden_t], 1)