Implementation of a sequential network

Now, let's implement one final class that will combine multiple dense layer and softmax layer objects into a single coherent feed-forward sequential neural network. This will be implemented as another class, which will subsume the other classes. Let's first start by writing the constructor—we will be able to set the max batch size here, which will affect how much memory is allocated for the use of this network – we'll store some allocated memory used for weights and input/output for each layer in the list variable, network_mem. We will also store the DenseLayer and SoftmaxLayer objects in the list network, and information about each layer in the NN in network_summary. Notice how we can also set up some training parameters here, including the delta, how many streams to use for gradient descent (we'll see this...