#本文从其他处copy过来，是为了留作参考，比如同样的分类问题，只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))
来源：CSDN
作者：少游223
链接：https://blog.csdn.net/shaoyou223/article/details/86636342