Pytorch, Deep Learning, Optimization, Tensorboard, Deep Dives.
train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True) device = torch.device("cuda:0") model = torchvision.models.resnet18(weights='IMAGENET1K_V1').cuda(device) criterion = torch.nn.CrossEntropyLoss().cuda(device) optimizer = torch.optim.SGD(model.parameters(), lr=0.001,