深度学习作业1

# -*- coding: utf-8 -*-import numpy as npimport matplotlib.pyplot as pltimport h5pyimport scipyfrom PIL import Imagefrom scipy import ndimagedef load_dataset():    train_dataset = h5py.File('./datasets/train_catvnoncat.h5', "r")    train_set_x_orig = np.array(train_dataset["train_set_x"][:])  # your train set features    train_set_y_orig = np.array(train_dataset["train_set_y"][:])  # your train set labels    test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")    test_set_x_orig = np.array(test_dataset["test_set_x"][:])  # your test set features    test_set_y_orig = np.array(test_dataset["test_set_y"][:])  # your test set labels    classes = np.array(test_dataset["list_classes"][:])  # the list of 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## Loading the data (cat/non-cat)train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()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).Ttrain_set_x = train_set_x_flatten/255.test_set_x = test_set_x_flatten/255.#plt.imshow(train_set_x_orig[25])#plt.show()#train_set_x_orig is a numpy-array of shape (m_train, num_px, num_px, 3)#- m_train (number of training examples)#- num_px (= height = width of a training image)#m_train=train_set_x_orig.shape[3]#print (m_train)def sigmoid(z):    return 1/(1+np.exp(-z))#print (sigmoid(0))#z=w的转置*xdef 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#w,b=initialize_with_zeros(2)#print(str(w))#print(str(b))def propagate(w, b, X, Y):    m=X.shape[1]    A=sigmoid(np.dot(w.T,X)+b)    # cost=-np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m    # cost = - np.sum(np.dot(Y,np.log(A)) + np.dot((1 - Y),np.log(1 - A))) / m    cost = -(np.sum(np.dot(Y, np.log(A).T) + np.dot((1 - Y), np.log(1 - A).T))) / m  # 成本函数    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, costdef 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, costsdef 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]):        if A[0,i]<=0.5:            Y_prediction[0,i]=0        else:            Y_prediction[0,i]=1    assert(Y_prediction.shape==(1,m))    return  Y_predictiondef 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}    my_image = "cat3.jpg"    fname = "images/" + my_image    image = np.array(ndimage.imread(fname, flatten=False))    my_image = scipy.misc.imresize(image, size=(64, 64)).reshape((1, 64 * 64 * 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.")    plt.show()    return dprint ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2]))))dim = 2w, b = initialize_with_zeros(dim)print ("w = " + str(w))print ("b = " + str(b))w, 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))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"]))print ("predictions = " + str(predict(w, b, X)))d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)