Tensors, Autograd & Training Loop. Tensors & Devices. import torch x = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) y = torch.randn(2, 2) z = x + y # broadcasting applies when shapes are compatible but not identical # Move to …
ReadData Pipeline & Advanced Training. Dataset & DataLoader. from torch.utils.data import Dataset, DataLoader from torchvision import transforms class ImageDataset(Dataset): def __init__(self, file_paths, labels): self.file_…
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