Description
A1 ) Add mini-batch stochastic gradient decent for both networks, add a function to calculate prediction accuracy for both networks.¶
A2) Plot the histogram of activation functions for each layer in network 1 after the training is done.
A3) Add two more hidden layers to the first network and define backpropegation accordingly
A4) add 1-2-3 more hidden layers to the network 2 and plot cost for each epoch as a line in the line plot. The color for each line should be unique based on the number of hidden layers.
Your line plot should have 4 lines, Original (2 layers), 3 layers, 4 layers and 5 layers networks. Please see the code in the attached document and modify it according to the above mentioned questions. Please do in a ipynb file (jupyter) or google colab