thin_stack.py 文件源码

def _create_state(self):
        """Prepare stateful variables modified during the recurrence."""

        # Both the queue and the stack are flattened stack_size * batch_size
        # tensors. `stack_size` many blocks of `batch_size` values
        stack_shape = (self.stack_size * self.batch_size, self.model_dim)
        self.stack = tf.Variable(tf.zeros(stack_shape, dtype=tf.float32),
                                 trainable=False, name="stack")
        self.queue = tf.Variable(tf.zeros((self.stack_size * self.batch_size,), dtype=tf.float32),
                                 trainable=False, name="queue")

        self.buff_cursors = tf.Variable(tf.zeros((self.batch_size,), dtype=tf.float32),
                                          trainable=False, name="buff_cursors")
        self.cursors = tf.Variable(tf.ones((self.batch_size,), dtype=tf.float32) * - 1,
                                   trainable=False, name="cursors")

        # TODO make parameterizable
        self.tracking_value = tf.Variable(tf.zeros((self.batch_size, self.tracking_dim), dtype=tf.float32),
                                          trainable=False, name="tracking_value")

        # Create an Op which will (re-)initialize the auxiliary variables
        # declared above.
        self._aux_vars = [self.stack, self.queue, self.buff_cursors, self.cursors,
                          self.tracking_value]
        self.variable_initializer = tf.initialize_variables(self._aux_vars)