autoencoder.py 文件源码

def _get_layer(self, layer_input, size_last_layer, size_current_layer):
        """
        Returns a layer with a batch normalized input, depending on the `batch_norm flag`

        @param layer_input is the value used as an input to the layer.
        @param size_last_layer is the size of the last layer (used in weight) or the size of the input
        @param size_current_layer is the size of the current layer (used in weight and bias)
        """
        weight = tf.Variable(tf.random_normal([size_last_layer, size_current_layer]))
        bias = tf.Variable(tf.random_normal([size_current_layer]))

        if not self.batch_norm:
            return self.activation_func(tf.add(tf.matmul(layer_input, weight), bias))


        layer_input = tf.contrib.layers.batch_norm(layer_input,
                                                   center=True, scale=True,
                                                   is_training=self.is_training,
                                                   scope='bn{}-{}'.format(size_last_layer, size_current_layer))

        return self.activation_func(tf.add(tf.matmul(layer_input, weight), bias))