mnist的pytorch版本，简单的迭代可得到97%的概率

最新推荐文章于 2025-06-22 12:49:32 发布

少游223

最新推荐文章于 2025-06-22 12:49:32 发布

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分类专栏：深度学习文章标签： mnis pytorc

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本文详细介绍了使用PyTorch框架在MNIST数据集上训练神经网络的过程，包括数据预处理、模型定义、损失函数选择、优化器配置及训练循环等关键步骤。

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需要修改几个关键位置，一是模型，二是损失，三是优化器

import torch

import torch.nn as nn

import torchvision.datasets as dsets

import torchvision.transforms as transforms

from torch.autograd import Variable

import win_unicode_console

win_unicode_console.enable()

# hyper parameter

input_size = 28 * 28

# # image size of MNIST data

num_classes = 10

num_epochs = 10

batch_size = 100

learning_rate = 1e-3

# MNIST dataset

#train_dataset = dsets.MNIST(root = '../../data_sets/mnist', train = True, transform = transforms.ToTensor(), download = True)

train_dataset = dsets.MNIST(root = '../../data_sets/mnist', train = True, transform = transforms.ToTensor(), download = False)

#test_dataset = dsets.MNIST(root = '../../data_sets/mnist', train = False, transform = transforms.ToTensor(), download = True)

test_dataset = dsets.MNIST(root = '../../data_sets/mnist', train = False, transform = transforms.ToTensor(), download = False)

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)

#input_size = 2

hidden_size = 100

#num_classes = 3

# # 创建神经网络模型

class neural_net(nn.Module):

def __init__(self, input_num,hidden_size, out_put):

super(neural_net, self).__init__()

self.fc1 = nn.Linear(input_num, hidden_size)

self.relu = nn.ReLU()

self.fc2 = nn.Linear(hidden_size, out_put)

def forward(self, x):

out = self.fc1(x)

out = self.relu(out)

out = self.fc2(out)

return out

model = neural_net(input_size, hidden_size, num_classes)

print(model)

# optimization

learning_rate = 1e-3

num_epoches = 5

criterion = nn.CrossEntropyLoss()

optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)

for epoch in range(num_epoches):

#inputs = Variable(torch.from_numpy(train_x))

# #targets = Variable(torch.from_numpy(train_y))

# #print(inputs) #print(targets)

# #optimizer.zero_grad()

# #outputs = model(inputs)

# #loss = criterion(outputs, targets)

# #loss.backward() #optimizer.step()

# #print('current loss = %.5f' % loss.data[0])

print('current epoch = %d' % epoch)

for i, (images, labels) in enumerate(train_loader):

#利用enumerate取出一个可迭代对象的内容

images = Variable(images.view(-1, 28 * 28))

labels = Variable(labels)

optimizer.zero_grad()

outputs = model(images)

loss = criterion(outputs, labels)

loss.backward()

optimizer.step()

if i % 100 == 0:

print('current loss = %.5f' % loss.data[0])

total = 0

correct = 0

for images, labels in test_loader:

images = Variable(images.view(-1, 28 * 28))

outputs = model(images)

_, predicts = torch.max(outputs.data, 1)

total += labels.size(0)

correct += (predicts == labels).sum()

print('Accuracy = %.2f' % (100 * correct / total))