I am a new Pytorch user and I meet a problem.
This is my code
#-*-coding:utf-8-*-
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
# Hyper parameters
num_epochs = 20
batchsize = 100
lr = 0.001
# MNIST Dataset
train_dataset = dsets.MNIST(root='/Users/hushengyou/PycharmProjects/NJU_contest/homework3/',
train=True,
transform=transforms.ToTensor(),
download=False)
test_dataset = dsets.MNIST(root='/Users/hushengyou/PycharmProjects/NJU_contest/homework3/',
train=False,
transform=transforms.ToTensor())
# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batchsize,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batchsize,
shuffle=False)
# CNN model
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
nn.conlayer1 = nn.Sequential(
nn.Conv2d(1,6,3),
nn.Sigmoid(),
nn.MaxPool2d(2))
nn.conlayer2 = nn.Sequential(
nn.Conv2d(6,16,3),
nn.Sigmoid(),
nn.MaxPool2d(2))
nn.fc = nn.Sequential(
nn.Linear(400,120),
nn.Linear(120,84),
nn.Linear(84,10))
def forward(self, x):
out = self.conlayer1(x)
out = self.conlayer2(out)
out = out.view(out.size(0),-1)
out = self.fc(out)
return out
cnn = CNN()
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=lr)
# Train the Model
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = cnn(images)
loss = criterion(outputs,labels)
loss.backward()
optimizer.step()
if (i+1)%100 == 0:
print('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f' %
(epoch+1,num_epochs,i+1,len(train_dataset)//batchsize,loss.data[0]))
# Test the Model
cnn.eval() # Change model to 'eval' mode (BN uses moving mean/var)
correct = 0
total = 0
for images, labels in test_loader:
outputs = cnn(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Test Accuracy of the model on test images: %.6f%%' % (100.0*correct/total))
#Save the Trained Model
torch.save(cnn.state_dict(),'cnn.pkl')
Can anybody solve my problem？ Thank you very much!!!