输出层采用sigmoid激活，隐藏层采用tanh激活
import h5py
import numpy as npfrom project_02.code.planar_utils import load_planar_dataset, plot_decision_boundarydef sigmoid(z):s = 1 / (1 + np.exp(-z))return sdef init_parameters(n_x, n_h, n_y):"""待训练参数初始化:param n_x: 输入层神经元个数（特征数）:param n_h: 隐藏层神经元个数:param n_y: 输出层神经元个数"""np.random.seed(2)W1 = np.random.randn(n_h, n_x) * 0.01b1 = np.zeros(shape=(n_h, 1))W2 = np.random.randn(n_y, n_h) * 0.01b2 = np.zeros(shape=(n_y, 1))parameters = {"W1": W1,"b1": b1,"W2": W2,"b2": b2}return parametersdef forward_propagation(X, parameters):"""前向传播:param X: 输入特征，维度是 (横纵坐标, 样本数):param parameters: 参数w、b:return:A2 -- The sigmoid output of the second activationcache -- a dictionary containing "Z1","A1","Z2" and "A2""""W1 = parameters["W1"]b1 = parameters["b1"]W2 = parameters["W2"]b2 = parameters["b2"]Z1 = np.dot(W1, X) + b1A1 = np.tanh(Z1)Z2 = np.dot(W2, A1) + b2A2 = sigmoid(Z2)cache = {"Z1": Z1,"A1": A1,"Z2": Z2,"A2": A2}return A2, cachedef compute_cost(A2, Y):"""计算成本:param A2: -- 输出层的输出结果:param Y: -- 标签:param parameters: w,b"""m = Y.shape[1]logprobs = Y * np.log(A2) + (1 - Y) * np.log(1 - A2)cost = -np.sum(logprobs) / mreturn costdef backward_propagation(X, Y, parameters, cache):"""反向传播:param parameters: w,b:param cache: Z1, A1, Z2, Z2:param X: 输入特征:param Y: 标签"""m = X.shape[1]W1 = parameters["W1"]b1 = parameters["b1"]W2 = parameters["W2"]b2 = parameters["b2"]Z1 = cache["Z1"]A1 = cache["A1"]Z2 = cache["Z2"]A2 = cache["A2"]dZ2 = A2 - Y # [dz1,dz2,dz3... dz.m]dW2 = np.dot(dZ2, A1.T) / m # [dw1,dw2,dw3...dw_n_h]db2 = np.sum(dZ2, 1, keepdims=True) / mdZ1 = np.dot(W2.T, dZ2) * (1 - A1 ** 2) # dA1 * g'(Z1)dW1 = np.dot(dZ1, X.T) / mdb1 = np.sum(dZ1, axis=1, keepdims=True) / mgrads = {"dW1": dW1,"db1": db1,"dW2": dW2,"db2": db2}return gradsdef update_parameters(parameters, grads, learn_rate=1.2):"""梯度下降:param parameters: w,b:param grads: 梯度dw,db:param learning_rate: 学习率:return:"""W1 = parameters["W1"]b1 = parameters["b1"]W2 = parameters["W2"]b2 = parameters["b2"]dW1 = grads['dW1']db1 = grads['db1']dW2 = grads['dW2']db2 = grads['db2']W1 = W1 - learn_rate * dW1b1 = b1 - learn_rate * db1W2 = W2 - learn_rate * dW2b2 = b2 - learn_rate * db2parameters = {"W1": W1,"b1": b1,"W2": W2,"b2": b2}return parametersdef nn_model(X, Y, n_h, num_iterations=2000, print_cost=False):np.random.seed(3)n_x = X.shape[0]n_y = Y.shape[0]parameters = init_parameters(n_x, n_h, n_y)result_parameters = Nonefor i in range(0, num_iterations):A2, cache = forward_propagation(X, parameters)cost = compute_cost(A2, Y)grads = backward_propagation(X, Y, parameters, cache)result_parameters = {"W1": parameters["W1"],"b1": parameters["b1"],"W2": parameters["W2"],"b2": parameters["b2"]}parameters = update_parameters(parameters, grads, learn_rate=1.2)if print_cost and i % 1000 == 0:print(f"训练{i}次后，成本为:{cost}")return result_parametersdef predict(parameters, X):"""预测:param parameters: w,b:param X: 特征"""A2, cache = forward_propagation(X, parameters)predictions = np.round(A2)return predictionsif __name__ == "__main__":X, Y = load_planar_dataset()parameters = nn_model(X, Y, n_h=4, num_iterations=10000, print_cost=True)predictions = predict(parameters, X)print("预测准确率是: %d" % float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100) + "%")
