ex3_nn.py

""" Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks
Instructions
------------
This file contains code that helps you get started on the
linear exercise. You will need to complete the following functions
in this exericse:
   lrCostFunction (logistic regression cost function) - completed
   oneVsAll - completed
   predictOneVsAll - completed
   predict - completed
For this exercise, you will not need to change any code in this file,
or any other files other than those mentioned above.
"""

# Imports
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as io
import ex2helper as helper2
import ex3helper as helper


def main():
    # Setup the parameters you will use for this exercise
    input_layer_size = 400  # 20x20 Input Images of Digits
    hidden_layer_size = 25  # 25 hidden units
    num_labels = 10         # 10 labels, from 1 to 10
    # (note that we have mapped "0" to label 10)

    # =========== Part 1: Loading and Visualizing Data =============
    #  We start the exercise by first loading and visualizing the dataset.
    #  You will be working with a dataset that contains handwritten digits.

    # Load Training Data
    print('Loading and Visualizing Data ...')

    mat = io.loadmat('./data/ex3data1.mat')
    X = mat['X']
    y = np.squeeze(mat['y'])

    m = y.shape[0]

    # Randomly select 100 data points to display
    perm = np.random.permutation(m)
    sel = X[perm[0:100], :]

    # display data as image
    helper.displayData(sel)
    plt.show()

    input('\nPart 1 completed. Program paused. Press enter to continue: ')

    #  ================ Part 2: Loading Pameters ================
    # In this part of the exercise, we load some pre-initialized
    # neural network parameters.

    print('\nLoading Saved Neural Network Parameters ...')

    # Load the weights into variables Theta1 and Theta2
    mat = io.loadmat('./data/ex3weights.mat')
    theta1 = mat['Theta1']
    theta2 = mat['Theta2']

    #  ================= Part 3: Implement Predict =================
    #  After training the neural network, we would like to use it to predict
    #  the labels. You will now implement the "predict" function to use the
    #  neural network to predict the labels of the training set. This lets
    #  you compute the training set accuracy.

    p = helper.predict(theta1, theta2, X)
    predictions = np.zeros(p.shape)
    predictions[np.where(p == y)] = 1

    print('Train Set Accuracy: {:.1f}%'.format(np.mean(predictions) * 100))

    input('\nPart 3 completed. Program paused. Press enter to continue: ')

    # Randomly select 100 data points to display
    perm = np.random.permutation(m)
    for i in range(0, m):
        print('\n    Displaying Example Image...\n')
        example = X[perm[i], :]
        example = example[np.newaxis, :]

        helper.displayData(example)
        plt.show()
        p = helper.predict(theta1, theta2, example)
        print('    Neural Network Prediction: {}'.format(p[0] % 10))
        print('    Correct Answer: {}\n'.format(y[perm[i]] % 10))

        answer = input('Paused - press enter to continue, q to exit:')
        if(answer == 'q'):
            break

if __name__ == '__main__':
    main()