addressing.py 文件源码

def _usage_after_read(self, prev_usage, free_gate, read_weights):
    """Calcualtes the new usage after reading and freeing from memory.

    Args:
      prev_usage: tensor of shape `[batch_size, memory_size]`.
      free_gate: tensor of shape `[batch_size, num_reads]` with entries in the
          range [0, 1] indicating the amount that locations read from can be
          freed.
      read_weights: tensor of shape `[batch_size, num_reads, memory_size]`.

    Returns:
      New usage, a tensor of shape `[batch_size, memory_size]`.
    """
    with tf.name_scope('usage_after_read'):
      free_gate = tf.expand_dims(free_gate, -1)
      free_read_weights = free_gate * read_weights
      phi = tf.reduce_prod(1 - free_read_weights, [1], name='phi')
      return prev_usage * phi