import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
import time ,os
#卷积网络参数,迭代次数,一批量的样本数,学习率,准确率,时间成本
#迭代次数*一批量的样本数 = 总样本数
epochs = 10
batch_size = 100
learning_rate = [0.001, 0.005, 0.01, 0.05, 0.1]
accuracy = []
time_cost = []
os.environ["CUDA_DEVIC_ORDER"] = "PCI_BUS_ID"#指定使用某一块GPU,编号从0号开始
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#Minst数据集下载以及导入,root下载目录,是否设为训练集,转变成Tensor
train_dataset = dsets.MNIST(root='./data/',
train = True,
transform=transforms.ToTensor(),
download=True )
test_dataset =dsets.MNIST(root='./data/',
train=False,
transform=transforms.ToTensor(),
download=True)
#传入torch的数据集,DataLoader是一个迭代器,使用小批量梯度下降法,shuffle打乱数据顺序
train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
卷积神经网络实例
最新推荐文章于 2025-06-04 11:49:12 发布