def model_01(): """ 猫图像识别,两层网络结构 :return: """ X_train, Y_train, X_test, Y_test, classes = load_data() # 猫图像数据 # 模型参数设置 layers_dims = [X_train.shape[0], 7, 1] num_iter = 2500 learning_rate = 0.0075 print_cost = True initialization = "sqrt_n" parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) # 预测及评估 prediction_train = predict(parameters, X_train) prediction_test = predict(parameters, X_test) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) print("test准确率: {}".format(evaluate(prediction_test, Y_test))) costs_draw(costs, learning_rate=learning_rate)
def model_06(): # 加载数据集 X_train, Y_train, X_test, Y_test = load_dataset() # 数据 # 设置参数 layers_dims = [X_train.shape[0], 1] num_iter = 2000 learning_rate = 0.5 print_cost = False initialization = "he" parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) # 预测及评估 prediction_train = predict(parameters, X_train) prediction_test = predict(parameters, X_test) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) print("test准确率: {}".format(evaluate(prediction_test, Y_test))) plt.title("Model with He initialization") axes = plt.gca() axes.set_xlim([-1.5, 1.5]) axes.set_ylim([-1.5, 1.5]) plot_decision_boundary(lambda x: predict(parameters, x.T), X_train, Y_train) plt.show()
def model_04(): """单隐层平面数据分类,测试不同的隐藏层size """ # 加载数据集 planar = load_planar_dataset() noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets( ) datasets = { "planar": planar, "noisy_circles": noisy_circles, "noisy_moons": noisy_moons, "blobs": blobs, "gaussian_quantiles": gaussian_quantiles } data_set = "planar" # 选择数据集 X_train, Y_train = datasets[data_set] if data_set != "planar": X_train, Y_train = X_train.T, Y_train.reshape(1, Y_train.shape[0]) if data_set == "blobs": Y_train = Y_train % 2 plt.figure(figsize=(16, 32)) hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50] # 模型参数设置 num_iter = 5000 learning_rate = 1.2 print_cost = False initialization = "random_small" for i, n_h in enumerate(hidden_layer_sizes): plt.subplot(5, 2, i + 1) plt.title('Hidden Layer of size %d' % n_h) layers_dims = [X_train.shape[0], n_h, Y_train.shape[0]] parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) plot_decision_boundary(lambda x: predict(parameters, x.T), X_train, Y_train) # 预测及评估 prediction_train = predict(parameters, X_train) accuracy = evaluate(prediction_train, Y_train) print("Accuracy for {} hidden units: {}".format(n_h, accuracy)) plt.show()
def model_03(): """ 单隐层平面数据分类 :return: """ # 加载数据集 planar = load_planar_dataset() noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets( ) datasets = { "planar": planar, "noisy_circles": noisy_circles, "noisy_moons": noisy_moons, "blobs": blobs, "gaussian_quantiles": gaussian_quantiles } data_set = "planar" # 选择数据集 X_train, Y_train = datasets[data_set] if data_set != "planar": X_train, Y_train = X_train.T, Y_train.reshape(1, Y_train.shape[0]) if data_set == "blobs": Y_train = Y_train % 2 # 绘制散点图 data_draw(X_train, Y_train) # 模型参数设置 layers_dims = [X_train.shape[0], 4, Y_train.shape[0]] num_iter = 10000 learning_rate = 1.2 print_cost = True initialization = "random_small" parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) # Plot the decision boundary plot_decision_boundary(lambda x: predict(parameters, x.T), X_train, Y_train) plt.title("Decision Boundary for hidden layer size " + str(4)) plt.show() # 预测及评估 prediction_train = predict(parameters, X_train) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) costs_draw(costs, learning_rate=learning_rate)
def different_lr(X_train, Y_train, X_test, Y_test): """ 测试不同的学习率 :return: """ learning_rates = [0.01, 0.001, 0.005, 0.0005, 0.0001] num_iter = 1500 print_cost = False initialization = "zeros" for i in learning_rates: # 设置模型参数 learning_rate = i layers_dims = [X_train.shape[0], 1] print("learning rate is: " + str(i)) # 调用模型 parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) # 预测及评估 prediction_train = predict(parameters, X_train) prediction_test = predict(parameters, X_test) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) print("test准确率: {}".format(evaluate(prediction_test, Y_test))) plt.plot(np.squeeze(costs), label=str(i)) plt.ylabel('cost') plt.xlabel('iterations (per hundreds)') legend = plt.legend(loc='upper right', shadow=True) frame = legend.get_frame() frame.set_facecolor('0.90') plt.show()
def model_00(): """ 猫图像识别,逻辑回归模型 :return: """ # 加载数据 X_train, Y_train, X_test, Y_test, classes = load_data() # 模型参数设置 layers_dims = [X_train.shape[0], 1] num_iter = 2000 learning_rate = 0.005 print_cost = True initialization = "zeros" # 调用模型 parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) costs_draw(costs, learning_rate=learning_rate) # 预测及评估 prediction_train = predict(parameters, X_train) prediction_test = predict(parameters, X_test) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) print("test准确率: {}".format(evaluate(prediction_test, Y_test))) # 预测新图像 new_image = "images/my_image.jpg" image_reshape = read_image(new_image, show_image=True) new_image_prediction = predict(parameters, image_reshape) print("新图像预测值 y = {}".format(int(np.squeeze(new_image_prediction)))) # 不同学习率 different_lr(X_train, Y_train, X_test, Y_test)
def model_reg(lambd, keep_prob, title): # 加载数据 X_train, Y_train, X_test, Y_test = load_2D_dataset() # 模型参数设置 layers_dims = [X_train.shape[0], 20, 3, 1] num_iter = 30000 learning_rate = 0.3 print_cost = False initialization = "sqrt_n" # 调用模型 parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization, lambd=lambd, keep_prob=keep_prob) costs_draw(costs, learning_rate=learning_rate) # 预测及评估 prediction_train = predict(parameters, X_train) prediction_test = predict(parameters, X_test) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) print("test准确率: {}".format(evaluate(prediction_test, Y_test))) plt.title(title) axes = plt.gca() axes.set_xlim([-0.75, 0.40]) axes.set_ylim([-0.75, 0.65]) plot_decision_boundary(lambda x: predict(parameters, x.T), X_train, Y_train)
def model_02(): """猫图像识别,四层网络结构 :return: """ X_train, Y_train, X_test, Y_test, classes = load_data() # 猫图像数据 # 模型参数设置 layers_dims = [X_train.shape[0], 20, 7, 5, 1] num_iter = 2500 learning_rate = 0.0075 print_cost = True initialization = "sqrt_n" parameters, costs = basic_model(X_train, Y_train, layers_dims=layers_dims, num_iter=num_iter, lr=learning_rate, print_cost=print_cost, initialization=initialization) # 预测及评估 prediction_train = predict(parameters, X_train) prediction_test = predict(parameters, X_test) print("Train准确率: {}".format(evaluate(prediction_train, Y_train))) print("test准确率: {}".format(evaluate(prediction_test, Y_test))) costs_draw(costs, learning_rate=learning_rate) # 预测新图像 new_image = "images/my_image.jpg" image_reshape = read_image(new_image, show_image=True) new_image_prediction = predict(parameters, image_reshape) print("新图像预测值 y = {}".format(int(np.squeeze(new_image_prediction))))
def model_05(): """使用sklearn现有的逻辑回归模型训练 """ # 加载数据集 planar = load_planar_dataset() noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets( ) datasets = { "planar": planar, "noisy_circles": noisy_circles, "noisy_moons": noisy_moons, "blobs": blobs, "gaussian_quantiles": gaussian_quantiles } data_set = "planar" # 选择数据集 X_train, Y_train = datasets[data_set] if data_set != "planar": X_train, Y_train = X_train.T, Y_train.reshape(1, Y_train.shape[0]) if data_set == "blobs": Y_train = Y_train % 2 clf = sklearn.linear_model.LogisticRegressionCV() true_y = np.squeeze(Y_train) clf.fit(X_train.T, true_y.T) # Plot the decision boundary for logistic regression plot_decision_boundary(lambda x: clf.predict(x), X_train, Y_train) plt.title("Logistic Regression") plt.show() # 预测及评估 LR_predictions = clf.predict(X_train.T) accuracy = evaluate(LR_predictions, Y_train) print("准确率:", accuracy)