Pytorch, Optimization, Deep Learning, Artificial Intelligence, Tensorboard.
classification model model = VisionTransformer(patch_drop_rate=0.5).cuda(device) # define the loss function loss_fn = torch.nn.CrossEntropyLoss() # define the training optimizer optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # use random data class FakeDataset(Dataset): def