267d7e81977f23998078f39afd48e9a97c3acf5a,scripts/tf_cnn_benchmarks/convnet_builder.py,ConvNetBuilder,apool,#ConvNetBuilder#Any#Any#Any#Any#Any#Any#Any#,182

Before Change

    else:
      self.top_size = num_channels_in
    name = "apool" + str(self.counts["apool"])
    self.counts["apool"] += 1
    pool = pooling_layers.average_pooling2d(
        input_layer, [k_height, k_width], [d_height, d_width],
        padding=mode,

After Change

            input_layer=None,
            num_channels_in=None):
    Construct an average pooling layer.
    return self._pool("apool", pooling_layers.average_pooling2d, k_height,
                      k_width, d_height, d_width, mode, input_layer,
                      num_channels_in)

  def reshape(self, shape, input_layer=None):
    if input_layer is None:
      input_layer = self.top_layer

Instances