logistic_regression_NN_mindset.py

# -*- coding: utf-8 -*-
"""
Created on Mon Dec 11 12:35:02 2017

@author: Veeranjaneyulu Toka
"""

import numpy as np
import matplotlib.pyplot as plt

from lr_utils import load_dataset

train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()    

m_train = train_set_x_orig.shape[0]
m_test = test_set_x_orig.shape[0]
num_px = train_set_x_orig.shape[1]

train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T

train_set_x = train_set_x_flatten / 255.
test_set_x = test_set_x_flatten / 255.

def display_norm_image():
    #display the image at a particular index
    index = 25
    plt.imshow(train_set_x_orig[index])
    print("y = " + str(train_set_y[:, index]) + ", it's a '" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") + "' picture.")

#compute sigmoid
def sigmoid(z):
    s = 1/(1+np.exp(-z))
    return s

#verify sigmoid functionality
def veify_sigmoid():
    print ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2]))))

#initialize weights and bias with zeros
def initialize_with_zeros(dim):
    w = np.zeros((dim, 1))
    b = 0
    assert(w.shape == (dim, 1))
    assert(isinstance(b, float) or isinstance(b, int))
    return w, b

#verify initialization
def verify_initialization():
    dim = 2
    w, b = initialize_with_zeros(dim)
    print ("w = " + str(w))
    print ("b = " + str(b))

#compute FP and BP
def propagate(w, b, X, Y):
    m = X.shape[1]
    A = sigmoid(np.dot(w.T, X)+b)
    cost = (-1/m)*(np.sum(Y*np.log(A) + (1-Y)*np.log(1-A)))
    dw = (1/m)*np.dot(X, (A-Y).T)
    db = (1/m)*np.sum(A - Y)
    assert(dw.shape == w.shape)
    assert(db.dtype == float)
    cost = np.squeeze(cost)
    assert(cost.shape == ())
    grads = {"dw":dw, "db":db}
    return grads, cost

#verify FP and BP
def veify_propagate():
    w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])
    grads, cost = propagate(w, b, X, Y)
    print ("dw = " + str(grads["dw"]))
    print ("db = " + str(grads["db"]))
    print ("cost = " + str(cost))
                                              
# update weights and bias with GD
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost=False):
    costs = []
    for i in range(num_iterations):
        grads, cost = propagate(w, b, X, Y)
        dw = grads["dw"]
        db = grads["db"]
        w = w - learning_rate*dw
        b = b - learning_rate*db
        
        if i % 100 == 0:
            costs.append(cost)
            
        if print_cost and i % 100 == 0:
            print("Cost after iteration %i : %f" %(i, cost))
            
    params = {"w":w, "b":b}
    grads = {"dw":dw, "db":db}
    return params, grads, cost

#verify GD
def verify_optimize():    
    params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)
    print ("w = " + str(params["w"]))
    print ("b = " + str(params["b"]))
    print ("dw = " + str(grads["dw"]))
    print ("db = " + str(grads["db"]))

#predict wrt train and test
def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1, m))
    w = w.reshape(X.shape[0], 1)
    A = sigmoid(np.dot(w.T, X)+b)
    for i in range(A.shape[1]):
        Y_prediction[0][i] = 1 if A[0][i] > 0.5 else 0
    
    assert(Y_prediction.shape == (1, m))
    return Y_prediction

#create model and predict accuracy wrt train and test
def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    w, b = initialize_with_zeros(X_train.shape[0])
    
    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    
    w = parameters["w"]
    b = parameters["b"]
    
    Y_prediction_test = predict(w, b, X_test)
    Y_prediction_train = predict(w, b, X_train)
    
    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))
    
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test, 
         "Y_prediction_train" : Y_prediction_train, 
         "w" : w, 
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}
    
    return d    

def show_flatten_image(d):
    # Example of a picture that was wrongly classified.
    index = 1
    plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))
    print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[d["Y_prediction_test"][0,index]].decode("utf-8") +  "\" picture.")

def plot_curve(d):
    # Plot learning curve (with costs)
    costs = np.squeeze(d['costs'])
    plt.plot(costs)
    plt.ylabel('cost')
    plt.xlabel('iterations (per hundreds)')
    plt.title("Learning rate =" + str(d["learning_rate"]))
    plt.show()

def verify_with_diff_learning_rate():
    learning_rates = [0.01, 0.001, 0.0001]
    models = {}
    for i in learning_rates:
        print ("learning rate is: " + str(i))
        models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)
        print ('\n' + "-------------------------------------------------------" + '\n')
    
    for i in learning_rates:
        plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))
    
    plt.ylabel('cost')
    plt.xlabel('iterations')
    
    legend = plt.legend(loc='upper center', shadow=True)
    frame = legend.get_frame()
    frame.set_facecolor('0.90')
    plt.show()

def verify_with_our_image():
    ## START CODE HERE ## (PUT YOUR IMAGE NAME) 
    my_image = "my_image.jpg"   # change this to the name of your image file 
    ## END CODE HERE ##
    
    # We preprocess the image to fit your algorithm.
    fname = "images/" + my_image
    image = np.array(ndimage.imread(fname, flatten=False))
    my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T
    my_predicted_image = predict(d["w"], d["b"], my_image)
    
    plt.imshow(image)
    print("y = " + str(np.squeeze(my_predicted_image)) + ", your algorithm predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture.")

def main():
    display_norm_image()
    d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
    show_flatten_image(d)
    plot_curve(d)
    verify_with_diff_learning_rate()
    
if __name__ == "__main__":
    main()