nn.py 文件源码

def MLP(self, inputs, 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_size = self.mlp_size
    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 self.moving_params is None:
      if self.drop_gradually:
        s = self.global_sigmoid
        keep_prob = s + (1-s)*self.mlp_keep_prob
      else:
        keep_prob = self.mlp_keep_prob
    else:
      keep_prob = 1
    if isinstance(keep_prob, tf.Tensor) or 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,
                        add_bias=True,
                        moving_params=self.moving_params)
    if n_splits == 1:
      linear = [linear]
    for i, split in enumerate(linear):
      split = self.mlp_func(split)
      split.set_shape(shape_to_set)
      linear[i] = split
    if self.moving_params is None:
      with tf.variable_scope('Linear', reuse=True):
        matrix = tf.get_variable('Weights')
        I = tf.diag(tf.ones([self.mlp_size]))
        for W in tf.split(1, n_splits, matrix):
          WTWmI = tf.matmul(W, W, transpose_a=True) - I
          tf.add_to_collection('ortho_losses', tf.nn.l2_loss(WTWmI))
      for split in linear:
        tf.add_to_collection('covar_losses', self.covar_loss(split))
    if n_splits == 1:
      return linear[0]
    else:
      return linear

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