building_deep_neural_network.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jan  7 11:13:44 2018

@author: Veeranjaneyulu Toka
"""
import numpy as np
from dnn_utils_v2 import sigmoid, relu, relu_backward, sigmoid_backward
import h5py
import matplotlib.pyplot as plt

#parameter initailization for two layer network
def initialize_parameters(n_x, n_h, n_y):
    np.random.seed(1)
    
    W1 = np.random.randn(n_h, n_x)*0.01
    b1 = np.zeros((n_h, 1))
    W2 = np.random.randn(n_y, n_h)*0.01
    b2 = np.zeros((n_y, 1))
    
    assert(W1.shape == (n_h, n_x))
    assert(b1.shape == (n_h, 1))
    assert(W2.shape == (n_y, n_h))
    assert(b2.shape == (n_y, 1))
    
    parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2}
    return parameters

#parameter initialization for multi-layer network
def initialize_parameters_deep(layer_dims):
    np.random.seed(3)
    parameters = {}
    L = len(layer_dims)
    
    for l in range(1, L):
        parameters['W'+str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1])*0.01
        parameters['b'+str(l)] = np.zeros((layer_dims[l],1))
        
        assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))
        assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
        
    return parameters        

def linear_forward(A, W, b):
    Z = np.dot(W, A)+b
    
    assert(Z.shape == (W.shape[0], A.shape[1]))
    cache = (A, W, b)
    return Z, cache

def linear_activation_forward(A_prev, W, b, activation):
    if activation == "sigmoid":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = sigmoid(Z)
    elif activation == "relu":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = relu(Z)
        
    assert(A.shape == (W.shape[0], A_prev.shape[1]))
    cache = (linear_cache, activation_cache)
    return A, cache
    
def L_model_forward(X, parameters):
    caches = []
    A = X
    L = len(parameters) // 2
    
    for l in range(1, L):
        A_prev = A
        A, cache = linear_activation_forward(A_prev, parameters["W"+str(l)], parameters["b"+str(l)], activation = "relu")
        caches.append(cache)
        
    AL, cache = linear_activation_forward(A, parameters["W"+str(L)], parameters["b"+str(L)], activation = "sigmoid")
    caches.append(cache)
    
    assert(AL.shape == (1, X.shape[1]))
    return AL, caches

def compute_cost(AL, Y):
    m = Y.shape[1]
    cost = (-1/m)*np.sum(np.multiply(Y, np.log(AL)) + np.multiply((1-Y), np.log(1-AL)))
    cost = np.squeeze(cost)
    assert(cost.shape==())
    return cost

def linear_backward(dZ, cache):
    A_prev, W, b = cache
    m = A_prev.shape[1]
    
    dW = (1/m)*np.dot(dZ, A_prev.T)
    db = (1/m)*np.sum(dZ, axis=1, keepdims=True)
    dA_prev = np.dot(W.T, dZ)
    
    assert (dA_prev.shape == A_prev.shape)
    assert (dW.shape == W.shape)
    assert (db.shape == b.shape)
    
    return dA_prev, dW, db

def linear_activation_backward(dA, cache, activation):
    linear_cache, activation_cache = cache
    
    if activation == "relu":
        dZ = relu_backward(dA, activation_cache)
        dA_prev, dW, db = linear_backward(dZ, linear_cache)
        
    elif activation == "sigmoid":
        dZ = sigmoid_backward(dA, activation_cache)
        dA_prev, dW, db = linear_backward(dZ, linear_cache)
    
    return dA_prev, dW, db    

def L_model_backward(AL, Y, caches):
    grads = {}
    L = len(caches) # the number of layers
    m = AL.shape[1]
    Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
    
    # Initializing the backpropagation
    dAL = -(np.divide(Y, AL) - np.divide((1-Y), (1-AL)))
    
    current_cache = caches[L-1]
    grads["dA" + str(L)], grads["dW" + str(L)], grads["db" + str(L)] = linear_activation_backward(dAL, current_cache, activation='sigmoid')
    
    for l in reversed(range(L-1)):
        current_cache = caches[l]
        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA"+str(l+2)], current_cache, activation="relu")
        grads["dA" + str(l + 1)] = dA_prev_temp
        grads["dW" + str(l + 1)] = dW_temp
        grads["db" + str(l + 1)] = db_temp

    return grads    

def update_parameters(parameters, grads, learning_rate):
    L = len(parameters) // 2 # number of layers in the neural network

    # Update rule for each parameter. Use a for loop.
    for l in range(L):
        parameters["W"+str(l+1)] = parameters["W"+str(l+1)] - learning_rate*grads["dW"+str(l+1)]
        parameters["b"+str(l+1)] = parameters["b"+str(l+1)] - learning_rate*grads["db"+str(l+1)]
    return parameters    

def load_data():
    train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:])
    train_set_y_orig = np.array(train_dataset["train_set_y"][:])
    
    test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:])
    test_set_y_orig = np.array(test_dataset["test_set_y"][:])
    
    classes = np.array(test_dataset["list_classes"][:])
    
    train_set_y_orig = train_set_y_orig.reshape(1, train_set_y_orig.shape[0])
    test_set_y_orig = test_set_y_orig.reshape(1, test_set_y_orig.shape[0])
    
    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

def predict(X, y, parameters):  
    m = X.shape[1]
    n = len(parameters) // 2 # number of layers in the neural network
    p = np.zeros((1,m))
    
    # Forward propagation
    probas, caches = L_model_forward(X, parameters)

    
    # convert probas to 0/1 predictions
    for i in range(0, probas.shape[1]):
        if probas[0,i] > 0.5:
            p[0,i] = 1
        else:
            p[0,i] = 0
    
    #print results
    #print ("predictions: " + str(p))
    #print ("true labels: " + str(y))
    print("Accuracy: "  + str(np.sum((p == y)/m)))
        
    return p

def two_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):    
    np.random.seed(1)
    grads = {}
    costs = []                              # to keep track of the cost
    m = X.shape[1]                           # number of examples
    (n_x, n_h, n_y) = layers_dims
    
    # Initialize parameters dictionary, by calling one of the functions you'd previously implemented
    parameters = initialize_parameters(n_x, n_h, n_y)
    
    # Get W1, b1, W2 and b2 from the dictionary parameters.
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]
    
    # Loop (gradient descent)
    for i in range(0, num_iterations):

        # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: "X, W1, b1". Output: "A1, cache1, A2, cache2".
        A1, cache1 = linear_activation_forward(X, W1, b1, activation='relu')
        A2, cache2 = linear_activation_forward(A1, W2, b2, activation='sigmoid')
        
        # Compute cost
        cost = compute_cost(A2, Y)
        
        # Initializing backward propagation
        dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))
        
        # Backward propagation. Inputs: "dA2, cache2, cache1". Outputs: "dA1, dW2, db2; also dA0 (not used), dW1, db1".
        dA1, dW2, db2 = linear_activation_backward(dA2, cache2, activation='sigmoid')
        dA0, dW1, db1 = linear_activation_backward(dA1, cache1, activation='relu')
        
        # Set grads['dWl'] to dW1, grads['db1'] to db1, grads['dW2'] to dW2, grads['db2'] to db2
        grads['dW1'] = dW1
        grads['db1'] = db1
        grads['dW2'] = dW2
        grads['db2'] = db2
        
        # Update parameters.
        parameters = update_parameters(parameters, grads, learning_rate)

        # Retrieve W1, b1, W2, b2 from parameters
        W1 = parameters["W1"]
        b1 = parameters["b1"]
        W2 = parameters["W2"]
        b2 = parameters["b2"]
        
        # Print the cost every 100 training example
        if print_cost and i % 100 == 0:
            print("Cost after iteration {}: {}".format(i, np.squeeze(cost)))
        if print_cost and i % 100 == 0:
            costs.append(cost)
       
    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()
    
    return parameters    

def L_layer_model(X, Y, layers_dims, learning_rate=0.0075, num_iterations=3000, print_cost=False):
    np.random.seed(1)
    costs = []
    
    parameters = initialize_parameters_deep(layers_dims)
    
    for i in range(0, num_iterations):
        AL, caches = L_model_forward(X, parameters)
        
        cost = compute_cost(AL, Y)
        
        grads = L_model_backward(AL, Y, caches)
        
        parameters = update_parameters(parameters, grads, learning_rate)
        
        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
        if print_cost and i % 100 == 0:
            costs.append(cost)
    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()
    
    return parameters            

def main():
    train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
    
    # Example of a picture
#    index = 10
#    plt.imshow(train_x_orig[index])
#    print ("y = " + str(train_y[0,index]) + ". It's a " + classes[train_y[0,index]].decode("utf-8") +  " picture.")    

    train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T
    test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
    
    train_x = train_x_flatten / 255.
    test_x = test_x_flatten / 255.

    #two layer model    
#    n_x = 12288     # num_px * num_px * 3
#    n_h = 7
#    n_y = 1    
#    parameters = two_layer_model(train_x, train_y, layers_dims=(n_x, n_h, n_y), num_iterations=2500, print_cost=True)    

    #L layer model
    layers_dims = [12288, 20, 7, 5, 1]    
    parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations=2500, print_cost=True)
    
    predictions_train = predict(train_x, train_y, parameters)
    predictions_test = predict(test_x, test_y, parameters)
    
if __name__ == "__main__":
    main()