通过Sequential类来实现LeNet模型
#import
import sys
sys.path.append(/home/kesci/input)
import d2lzh1981 as d2l
import torch
import torch.nn as nn
import torch.optim as optim
import time
#net
class Flatten(torch.nn.Module): #展平操作
def forward(self, x):
#import
import sys
import d2lzh_pytorch as d2l
import torch
import torch.nn as nn
import torch.optim as optim
import time
#net
class Flatten(torch.nn.Module): #展平操作
def forward(self, x):
return x.view(x.shape[0], -1)
class Reshape(torch
本文实例讲述了Pytorch实现的手写数字mnist识别功能。分享给大家供大家参考，具体如下：
import torch
import torchvision as tv
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import argparse
# 定义是否使用GPU
device = torch.device(cuda if torch.cuda.
