After two rounds of convolution and pooling, our tensors have gotten relatively small and deep. After pool_2, the output dimension is (n, 6, 6, 32).
We have, in these convolutional layers, hopefully extracted relevant image features that this 6 x 6 x 32 tensor represents. To classify images, using these features, we will connect this tensor to a few fully connected layers, before we go to our final output layer.
In this example, I'll use a 512-neuron fully connected layer, a 256-neuron fully connected layer, and finally, the 10-neuron output layer. I'll also be using dropout to help prevent overfitting, but only a very little bit! The code for this process is given as follows for your reference:
from keras.layers import ...
