nn.py 文件源码

def MLP(self, inputs, output_size, func=None, keep_prob=None, n_splits=1):
    """"""

    n_dims = len(inputs.get_shape().as_list())
    batch_size = tf.shape(inputs)[0]
    bucket_size = tf.shape(inputs)[1]
    input_size = inputs.get_shape().as_list()[-1]
    output_shape = tf.pack([batch_size] + [bucket_size]*(n_dims-2) + [output_size])
    shape_to_set = [tf.Dimension(None)]*(n_dims-1) + [tf.Dimension(output_size)]
    if func is None:
      func = self.mlp_func

    if self.moving_params is None:
      if keep_prob is None:
        keep_prob = self.mlp_keep_prob
    else:
      keep_prob = 1
    if keep_prob < 1:
      noise_shape = tf.pack([batch_size] + [1]*(n_dims-2) + [input_size])
      inputs = tf.nn.dropout(inputs, keep_prob, noise_shape=noise_shape)

    linear = linalg.linear(inputs,
                        output_size,
                        n_splits=n_splits * (1+(func.__name__ in ('gated_tanh', 'gated_identity'))),
                        add_bias=True,
                        moving_params=self.moving_params)
    if func.__name__ in ('gated_tanh', 'gated_identity'):
      linear = [tf.concat(n_dims-1, [lin1, lin2]) for lin1, lin2 in zip(linear[:len(linear)//2], linear[len(linear)//2:])]
    if n_splits == 1:
      linear = [linear]
    for i, split in enumerate(linear):
      split = func(split)
      split.set_shape(shape_to_set)
      linear[i] = split
    if n_splits == 1:
      return linear[0]
    else:
      return linear

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