Pooling

Pooling layers help with overfitting and improve performance by reducing the size of the input tensor. Typically, they are used to scale down the input, keeping important information. Pooling is a much faster mechanism for input size reduction compared with tf.nn.conv2d.

The following pooling mechanisms are supported by TensorFlow:

• Average
• Max
• Max with argmax

Each pooling operation uses rectangular windows of size ksize separated by offset strides. If strides are all ones (1, 1, 1, 1), every window is used; if strides are all twos (1, 2, 2, 1), every other window is used in each dimension; and so on.

Max pool

The following defined function provides max pooling for the input 4D tensor tf.nn.max_pool:

max_pool(
  value, ksize, strides, padding, data_format='NHWC', name=None
)

The preceding arguments are explained here:

• value: This is the 4D tensor with shape [batch, height, width, channels], type tf.float32 on which max pooling needs to be done.
• ksize: This is the list of ints that has length...