Digit Recognition with Shallow network by @akshaybahadur21

digit_nn.py

import numpy as np
import matplotlib.pyplot as plt

def softmax(z):
    z -= np.max(z)
    sm = (np.exp(z).T / np.sum(np.exp(z),axis=1))
    return sm


def layers(X, Y):
    """

    :param X:
    :param Y:
    :return:
    """
    n_x = X.shape[0]
    n_y = Y.shape[0]
    return n_x, n_y


def initialize_nn(n_x, n_h, n_y):
    """

    :param n_x:
    :param n_h:
    :param n_y:
    :return:
    """
    np.random.seed(2)
    W1 = np.random.randn(n_h, n_x) * 0.01
    b1 = np.random.rand(n_h, 1)
    W2 = np.random.rand(n_y, n_h)
    b2 = np.random.rand(n_y, 1)
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}

    return parameters


def forward_prop(X, parameters):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']

    Z1 = np.dot(W1, X) + b1
    A1 = np.tanh(Z1)
    Z2 = np.dot(W2, A1) + b2
    A2 = softmax(Z2.T)

    cache = {"Z1": Z1,
             "A1": A1,
             "Z2": Z2,
             "A2": A2}

    return A2, cache


def compute_cost(A2, Y, parameters):
    m = Y.shape[1]
    W1 = parameters['W1']
    W2 = parameters['W2']
    logprobs = np.multiply(np.log(A2), Y)
    cost = - np.sum(logprobs) / m
    cost = np.squeeze(cost)

    return cost


def back_prop(parameters, cache, X, Y):
    m = Y.shape[1]
    W1 = parameters['W1']
    W2 = parameters['W2']
    A1 = cache['A1']
    A2 = cache['A2']


    dZ2 = A2 - Y
    dW2 = (1 / m) * np.dot(dZ2, A1.T)
    db2 = (1 / m) * np.sum(dZ2, axis=1, keepdims=True)

    dZ1 = np.multiply(np.dot(W2.T, dZ2), 1 - np.square(A1))
    dW1 = (1 / m) * np.dot(dZ1, X.T)
    db1 = (1 / m) * np.sum(dZ1, axis=1, keepdims=True)

    grads = {"dW1": dW1,
             "db1": db1,
             "dW2": dW2,
             "db2": db2}

    return grads


def update_params(parameters, grads, alpha):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']

    dW1 = grads['dW1']
    db1 = grads['db1']
    dW2 = grads['dW2']
    db2 = grads['db2']

    W1 = W1 - alpha * dW1
    b1 = b1 - alpha * db1
    W2 = W2 - alpha * dW2
    b2 = b2 - alpha * db2

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    return parameters


def model_nn(X, Y,Y_real,test_x,test_y, n_h, num_iters, alpha, print_cost):
    np.random.seed(3)
    n_x,n_y = layers(X, Y)
    parameters = initialize_nn(n_x, n_h, n_y)
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']

    costs = []
    for i in range(0, num_iters):

        A2, cache = forward_prop(X, parameters)

        cost = compute_cost(A2, Y, parameters)
        grads = back_prop(parameters, cache, X, Y)
        if (i > 1500):
            alpha1 = 0.95*alpha
            parameters = update_params(parameters, grads, alpha1)
        else:
            parameters = update_params(parameters, grads, alpha)

        if i % 100 == 0:
            costs.append(cost)
        if print_cost and i % 100 == 0:
            print("Cost after iteration for %i: %f" % (i, cost))



    predictions = predict_nn(parameters, X)
    print("Train accuracy: {} %", sum(predictions == Y_real) / (float(len(Y_real))) * 100)
    predictions=predict_nn(parameters,test_x)
    print("Train accuracy: {} %", sum(predictions == test_y) / (float(len(test_y))) * 100)



    #plt.plot(costs)
    #plt.ylabel('cost')
    #plt.xlabel('iterations (per hundreds)')
    #plt.title("Learning rate =" + str(alpha))
    #plt.show()

    return parameters


def predict_nn(parameters, X):
    A2, cache = forward_prop(X, parameters)
    predictions = np.argmax(A2, axis=0)
    return predictions