import tensorflow as tf

          'weights', [num_channels_in, num_out_channels],
          self.data_type,
          tf.random_normal_initializer(stddev=np.sqrt(init_factor /
                                                      (num_channels_in))))
      biases = tf.get_variable('biases', [num_out_channels],
                               self.data_type,
                               tf.constant_initializer(0.0))
      logits = tf.matmul(input_layer, kernel) + biases
      if activation == 'relu':
        affine1 = tf.nn.relu(logits, name=name)
      elif activation == 'linear' or activation is None:
        affine1 = logits
