• 微信公众号:美女很有趣。 工作之余,放松一下,关注即送10G+美女照片!

手写神经网络(MNIST手写数字识别)

开发技术 开发技术 2周前 (04-07) 7次浏览
  1 # 手写神经网络——mnist手写数字数据集
  2 import numpy as np
  3 # import torch
  4 import torchvision
  5 import torchvision.transforms as transforms
  6 # from torch.utils.data import DataLoader
  7 # import cv2
  8 
  9 # input layer:784 nodes(28*28)
 10 # hidden layer:three hidden layers with 20 nodes in each layer
 11 # output layer:10 nodes
 12 class BP:
 13     def __init__(self):
 14         self.input = np.zeros((100, 784))   # 100 samples per round
 15         self.hidden_layer_1 = np.zeros((100, 20))
 16         self.hidden_layer_2 = np.zeros((100, 20))
 17         self.hidden_layer_3 = np.zeros((100, 20))
 18         self.output_layer = np.zeros((100, 10))
 19         self.w1 = 2 * np.random.random((784, 20)) - 1   # limit to (-1, 1)
 20         self.w2 = 2 * np.random.random((20, 20)) - 1
 21         self.w3 = 2 * np.random.random((20, 20)) - 1
 22         self.w4 = 2 * np.random.random((20, 10)) - 1
 23         self.error = np.zeros(10)
 24         self.learning_rate = 0.1
 25 
 26     def sigmoid(self, x):
 27         return 1 / (1 + np.exp(-x))
 28 
 29     def sigmoid_deri(self, x):
 30         return x * (1 - x)
 31 
 32     def forward_prop(self, data, label):   # label:100 X 10,data: 100 X 784
 33         self.input = data
 34         self.hidden_layer_1 = self.sigmoid(np.dot(self.input, self.w1))
 35         self.hidden_layer_2 = self.sigmoid(np.dot(self.hidden_layer_1, self.w2))
 36         self.hidden_layer_3 = self.sigmoid(np.dot(self.hidden_layer_2, self.w3))
 37         self.output_layer = self.sigmoid(np.dot(self.hidden_layer_3, self.w4))
 38         # error
 39         self.error = label - self.output_layer
 40         return self.output_layer
 41 
 42     def backward_prop(self):
 43         output_diff = self.error * self.sigmoid_deri(self.output_layer)
 44         hidden_diff_3 = np.dot(output_diff, self.w4.T) * self.sigmoid_deri(self.hidden_layer_3)
 45         hidden_diff_2 = np.dot(hidden_diff_3, self.w3.T) * self.sigmoid_deri(self.hidden_layer_2)
 46         hidden_diff_1 = np.dot(hidden_diff_2, self.w2.T) * self.sigmoid_deri(self.hidden_layer_1)
 47         # update
 48         self.w4 += self.learning_rate * np.dot(self.hidden_layer_3.T, output_diff)
 49         self.w3 += self.learning_rate * np.dot(self.hidden_layer_2.T, hidden_diff_3)
 50         self.w2 += self.learning_rate * np.dot(self.hidden_layer_1.T, hidden_diff_2)
 51         self.w1 += self.learning_rate * np.dot(self.input.T, hidden_diff_1)
 52 
 53 # from torchvision load data
 54 def load_data():
 55     datasets_train = torchvision.datasets.MNIST(root='../../data/', train=True, transform=transforms.ToTensor()) # , download=True)
 56     # print(datasets_train)
 57     datasets_test = torchvision.datasets.MNIST(root='../../data/', train=False, transform=transforms.ToTensor())
 58 
 59     data_train = datasets_train.data
 60     # print(data_train)
 61     X_train = data_train.numpy()
 62     # print(X_train)
 63     X_test = datasets_test.data.numpy()
 64     X_train = np.reshape(X_train, (60000, 784))
 65     X_test = np.reshape(X_test, (10000, 784))
 66     Y_train = datasets_train.targets.numpy()
 67     Y_test = datasets_test.targets.numpy()
 68 
 69     real_train_y = np.zeros((60000, 10))
 70     real_test_y = np.zeros((10000, 10))
 71     # each y has ten dimensions
 72     for i in range(60000):
 73         real_train_y[i, Y_train[i]] = 1
 74     for i in range(10000):
 75         real_test_y[i, Y_test[i]] = 1
 76     index = np.arange(60000)   # 返回一个有终点和起点的固定步长的排列
 77     np.random.shuffle(index)   # 打乱顺序函数
 78     # shuffle train_data
 79     X_train = X_train[index]
 80     real_train_y = real_train_y[index]
 81 
 82     X_train = np.int64(X_train > 0)
 83     X_test = np.int64(X_test > 0)
 84 
 85 
 86     return X_train, real_train_y, X_test, real_test_y
 87 
 88 
 89 def bp_network():
 90     nn = BP()
 91     X_train, Y_train, X_test, Y_test = load_data()
 92     batch_size = 100
 93     epochs = 6000
 94     for epoch in range(epochs):
 95         start = (epoch % 600) * batch_size
 96         end = start + batch_size
 97         # print(start, end)
 98         nn.forward_prop(X_train[start: end], Y_train[start: end])
 99         nn.backward_prop()
100 
101     return nn
102 
103 
104 def bp_test():
105     nn = bp_network()
106     sum = 0
107     X_train, Y_train, X_test, Y_test = load_data()
108     # test:
109     for i in range(len(X_test)):
110         res = nn.forward_prop(X_test[i], Y_test[i])
111         res = res.tolist()   # 转换为列表
112         index = res.index(max(res))   # 检测字符串中是否包含子字符串str
113         if Y_test[i, index] == 1:
114             sum += 1
115 
116     print('accuracy:', sum / len(Y_test))
117 
118 
119 if __name__ == '__main__':
120     bp_test()

 


程序员灯塔
转载请注明原文链接:手写神经网络(MNIST手写数字识别)
喜欢 (0)