def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__, initializer=self._initializer):
# Split the hidden state into blocks (each U, V, W are shared across blocks).
U = tf.get_variable('U', [self._num_units_per_block, self._num_units_per_block])
V = tf.get_variable('V', [self._num_units_per_block, self._num_units_per_block])
W = tf.get_variable('W', [self._num_units_per_block, self._num_units_per_block])
b = tf.get_variable('biasU',[self._num_units_per_block])
state = tf.split(state, self._num_blocks, 1)
next_states = []
for j, state_j in enumerate(state): # Hidden State (j)
key_j = self._keys[j]
gate_j = self.get_gate(state_j, key_j, inputs)
candidate_j = self.get_candidate(state_j, key_j, inputs, U, V, W, b)
# Equation 4: h_j <- h_j + g_j * h_j^~
# Perform an update of the hidden state (memory).
state_j_next = state_j + tf.expand_dims(gate_j, -1) * candidate_j
# # Forget previous memories by normalization.
# Equation 5: h_j <- h_j / \norm{h_j}
state_j_next = tf.nn.l2_normalize(state_j_next, -1) # TODO: Is epsilon necessary?
# Forget previous memories by normalization.
# state_j_next_norm = tf.norm(tensor=state_j_next,
# ord='euclidean',
# axis=-1,
# keep_dims=True)
# state_j_next_norm = tf.where(
# tf.greater(state_j_next_norm, 0.0),
# state_j_next_norm,
# tf.ones_like(state_j_next_norm))
# state_j_next = state_j_next / state_j_next_norm
next_states.append(state_j_next)
state_next = tf.concat(next_states, 1)
return state_next, state_next
评论列表
文章目录
