深层神经网络
多层神经网络是只超过两层以上的神经网络。以下是代码
import numpy as np import h5py import matplotlib.pyplot as plt import time import scipy from PIL import Image from scipy import ndimage np.random.seed(1) %matplotlib inline #sigmoid def sigmoid(Z): A = 1/(1+np.exp(-Z)) cache = Z return A, cache #relu def relu(Z): A = np.maximum(0,Z) cache = Z return A, cache #sigmoid求导 def sigmoid_backward(dA, cache): Z = cache s = 1/(1+np.exp(-Z)) dZ = dA * s * (1-s) return dZ #relu求导 def relu_backward(dA, cache): Z = cache dZ = np.array(dA, copy=True) dZ[Z <= 0] = 0 return dZ #初始化参数 def initialize_parameters_deep(layer_dims): np.random.seed(1) 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]) / np.sqrt(layer_dims[l-1]) #初始化为随机值 parameters['b' + str(l)] = np.zeros((layer_dims[l], 1)) #初始化为0 return parameters #前向传播中的线性函数 def linear_forward(A, W, b): Z = np.dot(W, A) + b cache = (A, W, b) return Z, cache #不同激活函数下前向传播 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) cache = (linear_cache, activation_cache) return A, cache #L层神经网络模型的前向传播 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 #计算成本函数 def compute_cost(AL, Y): m = Y.shape[1] cost = (1./m) * (-np.dot(Y,np.log(AL).T) - np.dot(1-Y, np.log(1-AL).T)) cost = np.squeeze(cost) return cost #反向传播中的线性函数求导 def linear_backward(dZ, cache): A_prev, W, b = cache m = A_prev.shape[1] dW = np.dot(dZ, A_prev.T)/m db = np.sum(dZ, axis=1, keepdims=True)/m dA_prev = np.dot(W.T, dZ) return dA_prev, dW, db #不同激活函数下反向传播求导 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 #L层神经网络模型的反向传播 def L_model_backward(AL, Y, caches): grads = {} L = len(caches) m = AL.shape[1] Y = Y.reshape(AL.shape) 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 #更新参数 def update_parameters(parameters, grads, learning_rate): L = len(parameters) // 2 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 #计算识别准确度 def predict(X, y, parameters): m = X.shape[1] n = len(parameters) // 2 p = np.zeros((1,m)) probas, caches = L_model_forward(X, parameters) for i in range(0, probas.shape[1]): if probas[0,i] > 0.5: p[0,i] = 1 else: p[0,i] = 0 print("预测准确度: " + str(np.sum((p == y)/m))) return p #L层神经网络模型 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 ("循环%i次后的成本值: %f" %(i, cost)) if print_cost and i % 100 == 0: costs.append(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 #导入数据 def load_dataset(): train_dataset = h5py.File("train_cat.h5","r") #读取训练数据，共209张图片 test_dataset = h5py.File("test_cat.h5", "r") #读取测试数据，共50张图片 train_set_x_orig = np.array(train_dataset["train_set_x"][:]) #原始训练集（209*64*64*3） train_set_y_orig = np.array(train_dataset["train_set_y"][:]) #原始训练集的标签集（y=0非猫,y=1是猫）（209*1） test_set_x_orig = np.array(test_dataset["test_set_x"][:]) #原始测试集（50*64*64*3 test_set_y_orig = np.array(test_dataset["test_set_y"][:]) #原始测试集的标签集（y=0非猫,y=1是猫）（50*1） train_set_y_orig = train_set_y_orig.reshape((1,train_set_y_orig.shape[0])) #原始训练集的标签集设为（1*209） test_set_y_orig = test_set_y_orig.reshape((1,test_set_y_orig.shape[0])) #原始测试集的标签集设为（1*50） classes = np.array(test_dataset["list_classes"][:]) return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes train_x_orig, train_y, test_x_orig, test_y, classes = load_dataset() m_train = train_x_orig.shape[0] #训练集中样本个数 m_test = test_x_orig.shape[0] #测试集总样本个数 num_px = test_x_orig.shape[1] #图片的像素大小 train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0],-1).T #原始训练集的设为（12288*209） test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0],-1).T #原始测试集设为（12288*50） train_x = train_x_flatten/255. #将训练集矩阵标准化 test_x = test_x_flatten/255. #将测试集矩阵标准化 layers_dims = [12288, 20, 7, 5, 1] parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True) pred_train = predict(train_x, train_y, parameters) pred_test = predict(test_x, test_y, parameters) my_image = "my_image.jpg" #改成自己的图片文件名 my_label_y = [0] #改成自己的图片的标签 image = np.array(ndimage.imread(my_image, flatten=False)) my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((num_px*num_px*3,1)) my_predicted_image = predict(my_image, my_label_y, parameters) plt.imshow(image) print ("y = " + str(np.squeeze(my_predicted_image)) + ", 你的L层神经网络模型检测到它是一张 \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") + "\" 的图片.")
