nn.py 文件源码

def conditional_bilinear_classifier(self, inputs1, inputs2, n_classes, probs, add_bias1=True, add_bias2=True):
    """"""

    input_shape = tf.shape(inputs1)
    batch_size = input_shape[0]
    bucket_size = input_shape[1]
    input_size = inputs1.get_shape().as_list()[-1]
    input_shape_to_set = [tf.Dimension(None), tf.Dimension(None), input_size+1]
    output_shape = tf.pack([batch_size, bucket_size, n_classes, bucket_size])
    if len(probs.get_shape().as_list()) == 2:
      probs = tf.to_float(tf.one_hot(tf.to_int64(probs), bucket_size, 1, 0))
    else:
      probs = tf.stop_gradient(probs)

    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, input_size])
      inputs1 = tf.nn.dropout(inputs1, keep_prob, noise_shape=noise_shape)
      inputs2 = tf.nn.dropout(inputs2, keep_prob, noise_shape=noise_shape)

    inputs1 = tf.concat(2, [inputs1, tf.ones(tf.pack([batch_size, bucket_size, 1]))])
    inputs1.set_shape(input_shape_to_set)
    inputs2 = tf.concat(2, [inputs2, tf.ones(tf.pack([batch_size, bucket_size, 1]))])
    inputs2.set_shape(input_shape_to_set)

    bilin = linalg.bilinear(inputs1, inputs2,
                     n_classes,
                     add_bias1=add_bias1,
                     add_bias2=add_bias2,
                     initializer=tf.zeros_initializer,
                     moving_params=self.moving_params)
    weighted_bilin = tf.batch_matmul(bilin, tf.expand_dims(probs, 3))

    return weighted_bilin, bilin

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