[英]Cnn model using pytorch
I have images and labels.我有图像和标签。 I divided them into test and train sets.我将它们分为测试集和训练集。 (xtrain, ytrain, xtest, ytest). (xtrain,ytrain,xtest,ytest)。 x refers to images and y refers to label. x 指图像,y 指 label。 How to use these sets in the following train model如何在以下列车中使用这些套装 model
**# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# Test the model
model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch
mean/variance)**
from torch.utils.data import Dataset, DataLoader
training_set = Dataset(xtrain, ytrain)
test_set = Dataset(xtest, ytest)
params = {'batch_size': 64,
'shuffle': True}
train_loader = DataLoader(training_set, **params)
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