deeplearning1 logstic regression as a Neural Networks

# -*- coding: utf-8 -*-"""Created on Sat Dec  2 19:10:44 2017@author: xiaofeixiazyh"""# import packagesimport numpy as npimport matplotlib.pyplot as pltimport h5pyimport scipyfrom PIL import Imagefrom scipy import ndimagefrom lr_utils import load_dataset# load the datatrain_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()##Example of a picture#index = 24#plt.imshow(train_set_x_orig[index])#print("y = " + str(train_set_y[:,index]) + ", it is a " + classes[np.squeeze(train_set_y[:,index])].decode("utf-8") + " picture")##print(np.squeeze(train_set_y[:,index]))#print(train_set_x_orig.shape)m_train = train_set_x_orig.shape[0]m_test = test_set_x_orig.shape[0]num_px = train_set_x_orig.shape[1]#print("Number of training example: m_train = " + str(m_train))#print("Number of testing example : m_test = " + str(m_test))#print("Height/width of each image: num_px = " + str(num_px))#print("Each image is of size:(" + str(num_px) + "," + str(num_px) + ",3)")#print("train_set_x shape: " + str(train_set_x_orig.shape))#print("train_set_y shape :" + str(train_set_y.shape))#print("test_set_x shape: " + str(test_set_x_orig.shape))#print("test_set_y shape :" + str(test_set_y.shape))#-----------------------------------------# reshape the train and test set#----------------------------------------train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0],-1).Ttest_set_x_flatten  = test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T#print("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))#print("train_set_y shape :" + str(train_set_y.shape))#print("test_set_x_flatten shape :" + str(test_set_x_flatten.shape))#print("test_set_y shape : " + str(test_set_y.shape))#print("sanity check atfer reshapeing : " + str(train_set_x_flatten[0:5,0]))# standarded the train/test data set train_set_x = train_set_x_flatten / 255test_set_x = test_set_x_flatten / 225#print("check after standarded : " + str(train_set_x[0:5,0]))# Building the parts of algorithm-----------------------------#---------------------------------------------------------#def sigmod(z):    """    Compute the sigmoid of z    Arguments:    x -- A scalar or numpy array of any size.    Return:    s -- sigmoid(z)    """    s = 1. / (1 + np.exp(-z))    return s#print("sigmod(0) = " + str(sigmod(0)))#print("sigmod(9) = " + str(sigmod(9)))def initialize_with_zeros(dim):    """    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.    Argument:    dim -- size of the w vector we want (or number of parameters in this case)    Returns:    w -- initialized vector of shape (dim, 1)    b -- initialized scalar (corresponds to the bias)    """    w = np.zeros((dim, 1))    b = 0    assert(w.shape == (dim,1))    assert(isinstance(b,float) or isinstance(b,int))    return w,b#dim = 2#w,b = initialize_with_zeros(dim)#print("w = " + str(w))#print("b = " + str(b))def propagate(w,b,X,Y):    """    Implement the cost function and its gradient for the propagation explained above    Arguments:    w -- weights, a numpy array of size (num_px * num_px * 3, 1)    b -- bias, a scalar    X -- data of size (num_px * num_px * 3, number of examples)    Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)    Return:    cost -- negative log-likelihood cost for logistic regression    dw -- gradient of the loss with respect to w, thus same shape as w    db -- gradient of the loss with respect to b, thus same shape as b    Tips:    - Write your code step by step for the propagation    """    m = X.shape[1]    A = sigmod(np.dot(w.T,X) + b)    cost = np.sum(Y*np.log(A) + (1-Y) *np.log(1-A)) / (-m)    dw = np.dot(X, (A-Y).T) / m    db = np.sum(A-Y) / m    assert(dw.shape == w.shape)    assert(db.dtype == float)    cost = np.squeeze(cost)    assert(cost.shape == ())    grads = {            "dw" : dw,            "db" : db            }    return grads, costw, b, X, Y = np.array([[1],[2]]), 2 , np.array([[1,2],[3,4]]), np.array([[1,0]])#grads, cost = propagate(w,b,X,Y)#print("dw = " + str(grads["dw"]))#print("db = " + str(grads["db"]))#print("cost = " + str(cost))#==============================================================================# 从这里开始检查#==============================================================================def optimize(w, b ,X, Y, num_iterations, learning_rate, print_cost = False):    """    This function optimizes w and b by running a gradient descent algorithm    Arguments:    w -- weights, a numpy array of size (num_px * num_px * 3, 1)    b -- bias, a scalar    X -- data of shape (num_px * num_px * 3, number of examples)    Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)    num_iterations -- number of iterations of the optimization loop    learning_rate -- learning rate of the gradient descent update rule    print_cost -- True to print the loss every 100 steps    Returns:    params -- dictionary containing the weights w and bias b    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.    Tips:    You basically need to write down two steps and iterate through them:        1) Calculate the cost and the gradient for the current parameters. Use propagate().        2) Update the parameters using gradient descent rule for w and b.    """    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 or i %100 ==0 :            print("Cost after iteration %i: %f" %(i,cost))        params = {                "w" : w,                "b" : b                }        grads = {               "dw" : dw,               "db" : db                }         return params, grads , costsparams, grads, costs = optimize(w, b, X, Y, num_iterations= 1000, 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"]))    def predict(w,b,X):    '''    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)    Arguments:    w -- weights, a numpy array of size (num_px * num_px * 3, 1)    b -- bias, a scalar    X -- data of size (num_px * num_px * 3, number of examples)    Returns:    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X    '''    m = X.shape[1]    Y_prediction = np.zeros((1,m))    w = w.reshape(X.shape[0],1)    A = sigmod(np.dot(w.T,X))    for i in range(A.shape[1]):        if (A[0,i] > 0.5):            Y_prediction[0][i] = 1        else:            Y_prediction[0][1] = 0    assert(Y_prediction.shape == (1,m) )    return Y_prediction#print("prediction = " + str(predict(w,b,X)))def model(X_train, Y_train, X_test, Y_test, num_iterations, learning_rate, print_cost = False):    """    Builds the logistic regression model by calling the function you've implemented previously    Arguments:    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()    print_cost -- Set to true to print the cost every 100 iterations    Returns:    d -- dictionary containing information about the model.    """    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 = False)    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 dimport timetic = time.process_time()num_iterations = 10000d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = num_iterations, learning_rate = 0.5, print_cost = True)toc = time.process_time()print('Use num_iterations of %i, run %f sec ' %(num_iterations, toc - tic))