Exp4.rar

# coding:utf-8 import numpy as np class NueraLNet(object): def __init__(self, sizes): self.num_layers = len(sizes) self.sizes = sizes self.bias = [np.random.randn(1, y) for y in sizes[1:]] self.weights = [np.random.randn(x, y) for x, y in zip(sizes[:-1], sizes[1:])] def get_result(self, images): result = images for b, w in zip(self.bias, self.weights): result = sigmoid(np.dot(result, w) + b) return result def train_net(self, trainimage, trainresult, traintime, rate=1, minibatch=10, test_image=None, test_result=None): for i in range(traintime): minibatchimage = [trainimage[k:k+minibatch] for k in range(0, len(trainimage), minibatch)] minibatchresult = [trainresult[k:k+minibatch] for k in range(0, len(trainimage), minibatch)] for image, result in zip(minibatchimage, minibatchresult): self.update_net(image, result, rate) print("第{0}次学习结束".format(i+1)) if test_image and test_result: self.test_net(test_image, test_result) def update_net(self, training_image, training_result, rate): batch_b_error = [np.zeros(b.shape) for b in self.bias] batch_w_error = [np.zeros(w.shape) for w in self.weights] for image, result in zip(training_image, training_result): b_error, w_error = self.get_error(image, result) batch_b_error = [bbe + be for bbe, be in zip(batch_b_error, b_error)] batch_w_error = [bwe + we for bwe, we in zip(batch_w_error, w_error)] self.bias = [b - (rate/len(training_image))*bbe for b, bbe in zip(self.bias, batch_b_error)] self.weights = [w - (rate/len(training_image))*bwe for w, bwe in zip(self.weights, batch_w_error)] def get_error(self, image, result): b_error = [np.zeros(b.shape) for b in self.bias] w_error = [np.zeros(w.shape) for w in self.weights] out_data = [image] in_data = [] for b, w in zip(self.bias, self.weights): in_data.append(np.dot(out_data[-1], w) + b) out_data.append(sigmoid(in_data[-1])) b_error[-1] = ????????? w_error[-1] = ???????? for l in range(2, self.num_layers): b_error[-l] = sigmoid_prime(in_data[-l]) * \ np.dot(b_error[-l+1], self.weights[-l+1].transpose()) w_error[-l] = np.dot(out_data[-l-1].transpose(), b_error[-l]) return b_error, w_error def test_net(self, test_image, test_result): results = [(np.argmax(self.get_result(image)), result) for image, result in zip(test_image, test_result)] right = sum(int(x == y) for (x, y) in results) print("正确率：{0}/{1}".format(right, len(test_result))) return results def save_training(self): np.savez('./datafile/weights.npz', *self.weights) np.savez('./datafile/bias.npz', *self.bias) def read_training(self): length = len(self.sizes) - 1 file_weights = np.load('./datafile/weights.npz') file_bias = np.load('./datafile/bias.npz') self.weights = [] self.bias = [] for i in range(length): index = "arr_" + str(i) self.weights.append(file_weights[index]) self.bias.append(file_bias[index]) def sigmoid(x): *******????? def sigmoid_prime(x): ** ** ** * ?????