[Solved] CMSC421 Project 3

What youll need to do

• Implement and train a basic neural network with backpropagation
  • Well give you part of the code, you need to fill in the details
• Well give you a ZIP archive containing two files:
  • py: the class and method outlines

studenttest.py: code to train and test your neural network

• py contains some, but not all, of the necessary methods
  • Youll need to write the others yourself

activation(z):

• This is the activation function used in the feed-forward compution of the network.
• The included function is the sigmoid, but you can change this as you see fit.

Here is a graph showing visually how the sigmoid function works:

sigderiv(z):

This is the derivative of the sigmoid function

• Youll need to use it to update the values when training your neural network
• If you use an activation function other than the sigmoid, then youll need to use a different derivative than the one returned by sigderiv

Class: neuralnetwork:

__init__(self, size, seed):

• This function will initialize the weights and biases for a neural network of the size specified by the size parameter. The size parameter is a list of the form
  • inputsize, hidden layer 1 size, , hidden layer n size, output size]
• Example on next page

Example of __init__

Suppose size = [3, 4, 4, 1]

• then __init__ creates the network shown at right
• It initializes a variable weights = [ [v₁₁ v₁₂ v₁₃]

[v21 v22 v23] [v31 v32 v33]

[v41 v42 v43] ]

• each v_ij is the weight on the connection to unit i in the first hidden layer from unit j in the input layer

Methods/Classes provided in studNet.py (continued) forward(self, input):

Given a vector of input parameters of form:

[ [parameter 11, p12, , p1n] [p21, p22, , p2n] ] This method will return a 3-tuple:

• the output value(s) of each example (the variable a in the source code)

The values before activation was applied after the input was weighted (the variable pre_activations)

The values after activation for all layers (the variable activations)

• The reason it returns pre_activations and activations is because youll need them for updating the network

What you have to do:

• Implement a method called train()
  • test_train calls it to train the neural network on the data set

To do this training, you will need to perform back propagation and calculate deltas

• Weve provided method headers for train(), backpropagate(), and calcDeltas()
  • If you want to use them, youll need to write the method bodies

But you dont have to use them if you want to implement your network differently

• The only things that must stay the same for testing:
  • you MUST have the test_train method and predict as provided

test_train must return an instance of your neural network that can be used to call predict(a).