Python语言技术文档

微信小程序技术文档

php语言技术文档

jsp语言技术文档

asp语言技术文档

C#/.NET语言技术文档

html5/css技术文档

javascript

点击排行

用Pytorch训练CNN(数据集MNIST,使用GPU的方法)

来源:中文源码网    浏览:203 次    日期:2024-03-26 08:42:41
【下载文档:  用Pytorch训练CNN(数据集MNIST,使用GPU的方法).txt 】


用Pytorch训练CNN(数据集MNIST,使用GPU的方法)
听说pytorch使用比TensorFlow简单,加之pytorch现已支持windows,所以今天装了pytorch玩玩,第一件事还是写了个简单的CNN在MNIST上实验,初步体验的确比TensorFlow方便。
参考代码(在莫烦python的教程代码基础上修改)如下:
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import time
#import matplotlib.pyplot as plt
torch.manual_seed(1)
EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = False
if_use_gpu = 1
# 获取训练集dataset
training_data = torchvision.datasets.MNIST(
root='./mnist/', # dataset存储路径
train=True, # True表示是train训练集,False表示test测试集
transform=torchvision.transforms.ToTensor(), # 将原数据规范化到(0,1)区间
download=DOWNLOAD_MNIST,
)
# 打印MNIST数据集的训练集及测试集的尺寸
print(training_data.train_data.size())
print(training_data.train_labels.size())
# torch.Size([60000, 28, 28])
# torch.Size([60000])
#plt.imshow(training_data.train_data[0].numpy(), cmap='gray')
#plt.title('%i' % training_data.train_labels[0])
#plt.show()
# 通过torchvision.datasets获取的dataset格式可直接可置于DataLoader
train_loader = Data.DataLoader(dataset=training_data, batch_size=BATCH_SIZE,
shuffle=True)
# 获取测试集dataset
test_data = torchvision.datasets.MNIST(
root='./mnist/', # dataset存储路径
train=False, # True表示是train训练集,False表示test测试集
transform=torchvision.transforms.ToTensor(), # 将原数据规范化到(0,1)区间
download=DOWNLOAD_MNIST,
)
# 取前全部10000个测试集样本
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1).float(), requires_grad=False)
#test_x = test_x.cuda()
## (~, 28, 28) to (~, 1, 28, 28), in range(0,1)
test_y = test_data.test_labels
#test_y = test_y.cuda()
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # (1,28,28)
nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5,
stride=1, padding=2), # (16,28,28)
# 想要con2d卷积出来的图片尺寸没有变化, padding=(kernel_size-1)/2
nn.ReLU(),
nn.MaxPool2d(kernel_size=2) # (16,14,14)
)
self.conv2 = nn.Sequential( # (16,14,14)
nn.Conv2d(16, 32, 5, 1, 2), # (32,14,14)
nn.ReLU(),
nn.MaxPool2d(2) # (32,7,7)
)
self.out = nn.Linear(32*7*7, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # 将(batch,32,7,7)展平为(batch,32*7*7)
output = self.out(x)
return output
cnn = CNN()
if if_use_gpu:
cnn = cnn.cuda()
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
loss_function = nn.CrossEntropyLoss()
for epoch in range(EPOCH):
start = time.time()
for step, (x, y) in enumerate(train_loader):
b_x = Variable(x, requires_grad=False)
b_y = Variable(y, requires_grad=False)
if if_use_gpu:
b_x = b_x.cuda()
b_y = b_y.cuda()
output = cnn(b_x)
loss = loss_function(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 100 == 0:
print('Epoch:', epoch, '|Step:', step,
'|train loss:%.4f'%loss.data[0])
duration = time.time() - start
print('Training duation: %.4f'%duration)
cnn = cnn.cpu()
test_output = cnn(test_x)
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = sum(pred_y == test_y) / test_y.size(0)
print('Test Acc: %.4f'%accuracy)
以上这篇用Pytorch训练CNN(数据集MNIST,使用GPU的方法)就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持中文源码网。

相关内容