Building a convolutional neural network
In this section, we're going to build a full convolutional neural network. We're going to cover the MNIST digits and transform that data to have channels construct the convolutional neural network with multiple layers, and then finally, run and train our convolutional neural network and see how it compares to the classical dense network.
Alright! Let's load up our MNIST digits, as shown in the following screenshot :
Loading MNIST digits
You can see that we're performing a similar operation to what we did for the dense neural network, except we're making a fundamental transformation to the data. Here, we're using NumPy's expand_dims call (again, passing -1, meaning the last dimension) to expand our image tensors from the 28 x 28 pixel MNIST images to actually have an additional dimension of one, which encodes the color. In this case, it's a black and white image, so it's a gray scale pixel; that information is now ready to use with the convolutional layers...
