global-average-pooling

Global Average Pooling Implemented in TensorFlow

At this point, this repository is in development.
I made ResNet with global average pooling instead of traditional fully-connected layer.
But the model will be replaced by simpler model for you to understand GAP easily.

GAP Example Code

The input tensor to GAP is (4, 4, 128).
In this example, I used 1 x 1 convolution to reduce filter size and then compute average pooling to 4 x 4 size. Lastly, the output tensor of the average pooling layer is flattened by tf.reduce_mean.

The Network In Network paper is not obvious. So.. please let me know if something is wrong.

gap_filter = resnet.create_variable('filter', shape=(1, 1, 128, 10))
h = tf.nn.conv2d(h, filter=gap_filter, strides=[1, 1, 1, 1], padding='SAME')
h = tf.nn.avg_pool(h, ksize=[1, 4, 4, 256], strides=[1, 1, 1, 1], padding='VALID')
h = tf.reduce_mean(h, axis=[1, 2])