# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt import h5py import scipy from PIL import Image from scipy import ndimage def 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).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. #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*x 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 #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, cost 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, costs 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]): 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_prediction 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} 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 d print ("sigmoid([0, 2]) = " + str(sigmoid(np.array([0,2])))) dim = 2 w, 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)