Leveraging a fully connected layer for classification

Then, we simply add a few more layers of convolution, batch normalization, and dropouts, progressively building our network until we reach the final layers. Just like in the MNIST example, we will leverage densely connected layers to implement the classification mechanism in our network. Before we can do this, we must flatten our input from the previous layer (16 x 16 x 32) to a 1D vector of dimension (8,192). We do this because dense layer-based classifiers prefer to receive 1D vectors, unlike the output from our previous layer. We proceed by adding two densely connected layers, the first one with 128 neurons (an arbitrary choice) and the second one with just one neuron, since we are dealing with a binary classification problem. If everything goes according to plan, this one neuron will be supported by its cabinet of neurons...