def __call__(self, is_train, scope=None):
activation = get_keras_activation(self.activation)
recurrent_activation = get_keras_activation(self.recurrent_activation)
kernel_initializer = get_keras_initialization(self.kernel_initializer)
recurrent_initializer = get_keras_initialization(self.recurrent_initializer)
if activation is None or kernel_initializer is None \
or recurrent_initializer is None or recurrent_activation is None:
raise ValueError()
cell = InitializedLSTMCell(self.num_units, kernel_initializer,
recurrent_initializer, activation,
recurrent_activation, self.forget_bias,
self.keep_recurrent_probs, is_train, scope)
return cell
def __call__(self, is_train, scope=None):
activation = get_keras_activation(self.activation)
recurrent_initializer = get_keras_initialization(self.recurrent_initializer)
kernel_initializer = get_keras_initialization(self.kernel_initializer)
candidate_initializer = get_keras_initialization(self.candidate_initializer)
return GRUCell(self.num_units, tf.constant_initializer(self.bais_init),
kernel_initializer, recurrent_initializer, candidate_initializer, activation)
def _distance_logits(self, x, keys):
init = get_keras_initialization(self.init)
key_w = tf.get_variable("key_w",
shape=(keys.shape.as_list()[-1],
self.projected_size),
initializer=init,
dtype=tf.float32)
key_logits = tf.tensordot(keys, key_w,
axes=[[2], [0]
]) # (batch, key_len, projected_size)
if self.shared_project:
x_w = key_w
else:
x_w = tf.get_variable("x_w",
shape=(x.shape.as_list()[-1],
self.projected_size),
initializer=init,
dtype=tf.float32)
x_logits = tf.tensordot(x, x_w,
axes=[[2],
[0]]) # (batch, x_len, projected_size)
summed = tf.expand_dims(x_logits, axis=2) + tf.expand_dims(
key_logits, axis=1) # (batch, key_len, x_len, poject_size)
summed = get_keras_activation(self.activation)(summed)
combine_w = tf.get_variable("combine_w",
shape=self.projected_size,
initializer=init,
dtype=tf.float32)
return tf.tensordot(summed, combine_w,
axes=[[3], [0]]) # (batch, key_len, x_len)