In this section, we're going to take a look at the TensorFlow function for max pooling, then we'll talk about transitioning from a pooling layer back to a fully connected layer. Finally, we'll visually look at the pooling output to verify its reduced size.
Let's pick up in our example from where we left off in the previous section. Make sure you've executed everything up to the pound pooling layer before starting this exercise.
Recall we've put a 10x10 image through a 3x3 convolution and rectified linear activation. Now, let's add a 2x2 max pooling layer that comes after our convolutional layer.
p1 = tf.nn.max_pool(h1, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='VALID')The key to this is tf.nn.max_pool. The first argument is just the output of our previous convolutional layer, h1. Next we have the strange ksize. This really just defines the window size of our pooling. In this case, 2x2. The first 1 refers to how many data points to pull over at once...