data_loader.py 文件源码

def _create_input_queue(self, queue_capacity_factor=16):
    self.input_ops, self.target_ops = {}, {}
    self.queue_ops, self.enqueue_ops = {}, {}
    self.x, self.y, self.seq_length, self.mask = {}, {}, {}, {}

    for name in self.data_num.keys():
      self.input_ops[name] = tf.placeholder(tf.float32, shape=[None, None])
      self.target_ops[name] = tf.placeholder(tf.int32, shape=[None])

      min_after_dequeue = 1000
      capacity = min_after_dequeue + 3 * self.batch_size

      self.queue_ops[name] = tf.RandomShuffleQueue(
          capacity=capacity,
          min_after_dequeue=min_after_dequeue,
          dtypes=[tf.float32, tf.int32],
          shapes=[[self.max_length, 2,], [self.max_length]],
          seed=self.random_seed,
          name="random_queue_{}".format(name))
      self.enqueue_ops[name] = \
          self.queue_ops[name].enqueue([self.input_ops[name], self.target_ops[name]])

      inputs, labels = self.queue_ops[name].dequeue()

      seq_length = tf.shape(inputs)[0]
      if self.use_terminal_symbol:
        mask = tf.ones([seq_length + 1], dtype=tf.float32) # terminal symbol
      else:
        mask = tf.ones([seq_length], dtype=tf.float32)

      self.x[name], self.y[name], self.seq_length[name], self.mask[name] = \
          tf.train.batch(
              [inputs, labels, seq_length, mask],
              batch_size=self.batch_size,
              capacity=capacity,
              dynamic_pad=True,
              name="batch_and_pad")