[cnn]cnn训练MINST数据集demo
tips:
在文件路径进入conda
输入
jupyter nbconvert --to markdown test.ipynb
将ipynb文件转化成markdown文件
jupyter nbconvert --to html test.ipynb
jupyter nbconvert --to pdf test.ipynb
（html,pdf文件同理）
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
from torchvision import datasets,transforms
import matplotlib.pyplot as plt
import numpy as np
input_size = 28 #图像尺寸 28*28
num_class = 10 #标签总数
num_epochs = 3 #训练总周期
batch_size = 64 #一个批次多少图片
train_dataset = datasets.MNIST(
root='data',
train=True,
transform=transforms.ToTensor(),
download=True,
)
test_dataset = datasets.MNIST(
root='data',
train=False,
transform=transforms.ToTensor(),
download=True,
)
train_loader = torch.utils.data.DataLoader(
dataset = train_dataset,
batch_size = batch_size,
shuffle = True,
)
test_loader = torch.utils.data.DataLoader(
dataset = test_dataset,
batch_size = batch_size,
shuffle = True,
)
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( #输入为(1,28,28)
nn.Conv2d(
in_channels=1,
out_channels=16, #要得到几个特征图
kernel_size=5, #卷积核大小
stride=1, #步长
padding=2,
), #输出特征图为(16*28*28)
nn.ReLU(),
nn.MaxPool2d(kernel_size=2), #池化(2x2) 输出为(16,14,14)
)
self.conv2 = nn.Sequential( #输入(16,14,14)
nn.Conv2d(16, 32, 5, 1, 2), #输出(32,14,14)
nn.ReLU(),
nn.MaxPool2d(2), #输出(32,7,7)
)
self.out = nn.Linear(32 * 7 * 7, 10) #全连接
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) #flatten操作 输出为(batch_size,32*7*7)
output = self.out(x)
return output, x
def accuracy(predictions,labels):
pred = torch.max(predictions.data,1)[1]
rights = pred.eq(labels.data.view_as(pred)).sum()
return rights,len(labels)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device
'cuda'
net = CNN().to(device)
criterion = nn.CrossEntropyLoss() #损失函数
#优化器
optimizer = optim.Adam(net.parameters(),lr = 0.001)
for epoch in range(num_epochs+1):
#保留epoch的结果
train_rights = []
for batch_idx,(data,target) in enumerate(train_loader):
data = data.to(device)
target = target.to(device)
net.train()
output = net(data)[0]
loss = criterion(output,target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
right = accuracy(output,target)
train_rights.append(right)
if batch_idx %100 ==0:
net.eval()
val_rights = []
for(data,target) in test_loader:
data = data.to(device)
target = target.to(device)
output = net(data)[0]
right = accuracy(output,target)
val_rights.append(right)
#计算准确率
train_r = (sum([i[0] for i in train_rights]),sum(i[1] for i in train_rights))
val_r = (sum([i[0] for i in val_rights]),sum(i[1] for i in val_rights))
print('当前epoch:{}[{}/{}({:.0f}%)]\t损失:{:.2f}\t训练集准确率:{:.2f}%\t测试集准确率:{:.2f}%'.format(
epoch,
batch_idx * batch_size,
len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.data,
100. * train_r[0].cpu().numpy() / train_r[1],
100. * val_r[0].cpu().numpy() / val_r[1]
)
)
当前epoch:0[0/60000(0%)] 损失:2.31 训练集准确率:4.69% 测试集准确率:21.01%
当前epoch:0[6400/60000(11%)] 损失:0.51 训练集准确率:75.94% 测试集准确率:91.43%
当前epoch:0[12800/60000(21%)] 损失:0.28 训练集准确率:84.05% 测试集准确率:93.87%
当前epoch:0[19200/60000(32%)] 损失:0.15 训练集准确率:87.77% 测试集准确率:96.42%
当前epoch:0[25600/60000(43%)] 损失:0.08 训练集准确率:89.82% 测试集准确率:97.02%
当前epoch:0[32000/60000(53%)] 损失:0.14 训练集准确率:91.20% 测试集准确率:97.42%
当前epoch:0[38400/60000(64%)] 损失:0.04 训练集准确率:92.13% 测试集准确率:97.59%
当前epoch:0[44800/60000(75%)] 损失:0.08 训练集准确率:92.83% 测试集准确率:97.83%
当前epoch:0[51200/60000(85%)] 损失:0.12 训练集准确率:93.38% 测试集准确率:97.77%
当前epoch:0[57600/60000(96%)] 损失:0.19 训练集准确率:93.81% 测试集准确率:98.24%
当前epoch:1[0/60000(0%)] 损失:0.07 训练集准确率:95.31% 测试集准确率:97.90%
当前epoch:1[6400/60000(11%)] 损失:0.08 训练集准确率:97.96% 测试集准确率:98.27%
当前epoch:1[12800/60000(21%)] 损失:0.10 训练集准确率:97.99% 测试集准确率:98.30%
当前epoch:1[19200/60000(32%)] 损失:0.02 训练集准确率:98.07% 测试集准确率:98.20%
当前epoch:1[25600/60000(43%)] 损失:0.17 训练集准确率:98.09% 测试集准确率:98.40%
当前epoch:1[32000/60000(53%)] 损失:0.12 训练集准确率:98.11% 测试集准确率:98.68%
当前epoch:1[38400/60000(64%)] 损失:0.05 训练集准确率:98.11% 测试集准确率:98.63%
当前epoch:1[44800/60000(75%)] 损失:0.10 训练集准确率:98.14% 测试集准确率:98.70%
当前epoch:1[51200/60000(85%)] 损失:0.04 训练集准确率:98.19% 测试集准确率:98.56%
当前epoch:1[57600/60000(96%)] 损失:0.03 训练集准确率:98.23% 测试集准确率:98.67%
当前epoch:2[0/60000(0%)] 损失:0.06 训练集准确率:98.44% 测试集准确率:98.32%
当前epoch:2[6400/60000(11%)] 损失:0.03 训练集准确率:98.64% 测试集准确率:98.63%
当前epoch:2[12800/60000(21%)] 损失:0.05 训练集准确率:98.70% 测试集准确率:98.62%
当前epoch:2[19200/60000(32%)] 损失:0.01 训练集准确率:98.72% 测试集准确率:98.69%
当前epoch:2[25600/60000(43%)] 损失:0.01 训练集准确率:98.70% 测试集准确率:98.76%
当前epoch:2[32000/60000(53%)] 损失:0.03 训练集准确率:98.70% 测试集准确率:98.76%
当前epoch:2[38400/60000(64%)] 损失:0.07 训练集准确率:98.70% 测试集准确率:98.62%
当前epoch:2[44800/60000(75%)] 损失:0.07 训练集准确率:98.72% 测试集准确率:98.60%
当前epoch:2[51200/60000(85%)] 损失:0.03 训练集准确率:98.71% 测试集准确率:98.99%
当前epoch:2[57600/60000(96%)] 损失:0.05 训练集准确率:98.74% 测试集准确率:98.84%