使用python中pytorch库实现cnn对mnist的识别

1 环境：Anaconda3 64bit https://www.anaconda.com/download/

2 环境：pycharm 社区免费版 https://www.jetbrains.com/pycharm/download/#section=windows 下载安装完需要指定上面anaconda的python解释器为pycharm的python解释器

3 环境：pytorch，https://zhuanlan.zhihu.com/p/26871672 中离线安装方式(下载类似于pytorch-0.1.12-py36_0.1.12cu80.tar.bz2的文件)

4 环境：Conda install torchvision

5 下载数据，直接用代码下载太慢了，用迅雷下载了mnist

6 将数据转换成torch可用的格式

7 训练与测试

data.py

import os

from skimage import io

import torchvision.datasets.mnist as mnist

"""

数据集下载地址

http://yann.lecun.com/exdb/mnist/

手动下载数据集 解压 检查文件名和下面几行代码中的文件名是否一致

然后启动本代码

"""

# 下载的数据所在目录

# F:/!BiliBili/!Py/AI/cnn_bili/mnist_data/train-images-idx3-ubyte

# F:/!BiliBili/!Py/AI/cnn_bili/mnist_data/train-labels-idx1-ubyte

# F:/!BiliBili/!Py/AI/cnn_bili/mnist_data/t10k-images-idx3-ubyte

# F:/!BiliBili/!Py/AI/cnn_bili/mnist_data/t10k-labels-idx1-ubyte

root = "F:/!BiliBili/!Py/AI/cnn_bili/mnist_data/"

# 哪个文件是训练特征(图) 训练标号(是几)

train_set = (

mnist.read_image_file(os.path.join(root, 'train-images-idx3-ubyte')),

mnist.read_label_file(os.path.join(root, 'train-labels-idx1-ubyte'))

)

# 哪个文件是测试特征 测试标号

test_set = (

mnist.read_image_file(os.path.join(root, 't10k-images-idx3-ubyte')),

mnist.read_label_file(os.path.join(root, 't10k-labels-idx1-ubyte'))

)

# 打印出有多少训练数据 多少测试数据

print("training set :", train_set[0].size())

print("test set :", test_set[0].size())

# 从原始数据 到 jpg图片 顺便装到文件夹里面

def convert_to_img(train=True):

if train:  # 如果是训练数据

# 注意这里路径的写法，对就是/符号

f = open(root+'train.txt', 'w')

data_path = root+'/train/'  # 好像可以删去左边的/

# 如果不存在就新建 F:/!BiliBili/!Py/AI/cnn_bili/mnist_data//train/

if not os.path.exists(data_path):

os.makedirs(data_path)

# enumerate将可遍历对象 组合成索引 可加参数start=2 索引从2开始

for i, (img, label) in enumerate(zip(train_set[0], train_set[1])):

img_path = data_path+str(i)+'.jpg'

# 保存图片

io.imsave(img_path, img.numpy())

# 保存标号文件路径和标号

f.write(img_path+' '+str(label)+'\n')

f.close()

else:  # 如果是测试数据

f = open(root + 'test.txt', 'w')

data_path = root + '/test/'

if not os.path.exists(data_path):

os.makedirs(data_path)

for i, (img, label) in enumerate(zip(test_set[0], test_set[1])):

img_path = data_path + str(i) + '.jpg'

io.imsave(img_path, img.numpy())

f.write(img_path + ' ' + str(label) + '\n')

f.close()

print("Building training set...")

convert_to_img(True)

print("Building test set...")

convert_to_img(False)

read_cnn.py

import torch

from torch.autograd import Variable

from torchvision import transforms

from torch.utils.data import Dataset, DataLoader

from PIL import Image

root = "F:/!BiliBili/!Py/AI/cnn_bili/mnist_data/"

# -----------------准备数据--------------------------

def default_loader(path):

return Image.open(path).convert('RGB')

class MyDataset(Dataset):

# txt是路径和文件名

def __init__(self, txt, transform=transforms.ToTensor(), target_transform=None, loader=default_loader):

fh = open(txt, 'r')  # 只读打开

imgs = []

for line in fh:

line = line.strip('\n')  # 删除 回车

line = line.rstrip()  # 删除 右侧 空格

words = line.split()  # 分割：就两列，0列是路径 1列是标号

imgs.append((words[0], int(words[1])))

self.imgs = imgs

self.transform = transform

self.target_transform = target_transform

self.loader = loader  # 是个函数

# train_loader里面的

def __getitem__(self, index):

fn, label = self.imgs[index]  # fn是完整路径 label是标号

img = self.loader(fn)  # 调用上面的default_loader(path) 按照路径读取图片

if self.transform is not None:

img = self.transform(img)  # 将图片转换成FloatTensor类型

return img, label

def __len__(self):

return len(self.imgs)

# -----------------创建网络并训练------------------------

class Net(torch.nn.Module):

def __init__(self):

super(Net, self).__init__()

self.conv1 = torch.nn.Sequential(

# 3通道进 32出 卷积核大小为3*3 卷积核步长1*1  (其实输入是黑白图)

# (self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

torch.nn.Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1)),

# 激活函数

# (self, inplace=False)

torch.nn.ReLU(),

# 2*2方形最大值 降(下)采样(池化) 步长2*2 膨胀1*1(不膨胀)

# (self, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1))  # dilation膨胀

)

self.conv2 = torch.nn.Sequential(

torch.nn.Conv2d(32, 64, 3, 1, 1),

torch.nn.ReLU(),

torch.nn.MaxPool2d(2)

)

self.conv3 = torch.nn.Sequential(

torch.nn.Conv2d(64, 64, 3, 1, 1),

torch.nn.ReLU(),

torch.nn.MaxPool2d(2)

)

self.dense = torch.nn.Sequential(

# 线性分类器 64*3*3的输入 128的输出

torch.nn.Linear(64 * 3 * 3, 128),

torch.nn.ReLU(),

# 线性分类器 128的输入 10的输出

torch.nn.Linear(128, 10)

)

# 前向计算 输入x

def forward(self, x):

# 第一层的输出是x经过conv1的结果

conv1_out = self.conv1(x)

# 第二层的输出是 第一层的输出经过conv2的结果

conv2_out = self.conv2(conv1_out)

# 第三层的输出是 第二层的输出经过conv3的结果

conv3_out = self.conv3(conv2_out)

res = conv3_out.view(conv3_out.size(0), -1)

return self.dense(res)  # dense稠密

def read_cnn():

print("Reading train_data...")

train_data = MyDataset(txt=root + 'train.txt', transform=transforms.ToTensor())

# from torch.utils.data import Dataset, DataLoader 下面的函数在这里

train_loader = DataLoader(dataset=train_data, batch_size=50, shuffle=True)

print("Reading test_data...")

test_data = MyDataset(txt=root + 'test.txt', transform=transforms.ToTensor())

test_loader = DataLoader(dataset=test_data, batch_size=50)

# GPU or CPU

if torch.cuda.is_available():

is_cuda = True

print("work on GPU")

else:

is_cuda = False

print("work on CPU")

print("Setup Net...")

# =============================cuda()=======================

if is_cuda:

model = Net().cuda()

else:

model = Net()

# 打印网络结构

print(model)

# Adam 一种随机优化方法

optimizer = torch.optim.Adam(model.parameters())

# 一种优化多类分类不平衡训练集的方法

loss_func = torch.nn.CrossEntropyLoss()

for epoch in range(3):  # 训练几次停止

print('epoch {}'.format(epoch + 1))

# 训练-----------------------------

train_loss = 0.

train_acc = 0.

for batch_x, batch_y in train_loader:  # 特征 标号

# =============================cuda()=======================

if is_cuda:

batch_x, batch_y = Variable(batch_x).cuda(), Variable(batch_y).cuda()

else:

batch_x, batch_y = Variable(batch_x), Variable(batch_y)

out = model(batch_x)  # batch_x通过网络的结果是out

loss = loss_func(out, batch_y)  # 网络结果out和实际batch_y对比的得到损失

train_loss += loss.data[0]  # 累加训练损失

# =============================cuda()=======================

if is_cuda:

pred = torch.max(out, 1)[1].cuda()  # 取 out和1 的最大值?

else:

pred = torch.max(out, 1)[1]  # 取 out和1 的最大值?

train_correct = (pred == batch_y).sum()  # 多少个对的

train_acc += train_correct.data[0]  # 累加训练正确的数量？

optimizer.zero_grad()  # 清除所有优化的grad

loss.backward()  # 误差反向传递

optimizer.step()  # 单次优化

# 数据量大的时候小数点后6位可能不够

# print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(train_data)), train_acc / (len(train_data))))

print('Train Acc: {:.6f}'.format(train_acc / (len(train_data))))

# 测试评估--------------------------------

model.eval()  # 将网络设置到测试评估模式，会影响网络的Dropout和BatchNorm

eval_loss = 0.

eval_acc = 0.

for batch_x, batch_y in test_loader:  # 特征 标号

# =============================cuda()=======================

if is_cuda:

batch_x, batch_y = Variable(batch_x, volatile=True).cuda(), Variable(batch_y, volatile=True).cuda()

else:

batch_x, batch_y = Variable(batch_x, volatile=True), Variable(batch_y, volatile=True)

out = model(batch_x)

loss = loss_func(out, batch_y)

eval_loss += loss.data[0]

# =============================cuda()=======================

if is_cuda:

pred = torch.max(out, 1)[1].cuda()

else:

pred = torch.max(out, 1)[1]

num_correct = (pred == batch_y).sum()

eval_acc += num_correct.data[0]

# print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(test_data)), eval_acc / (len(test_data))))

print('Test Acc: {:.6f}'.format(eval_acc / (len(test_data))))

if __name__ == '__main__':

read_cnn()

本文说实话不适合小白来读，应该是了解了相关知识的人才能够从中获得一些帮助吧输出