tutorials/01-basics/04-transforms/main.ipynb

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    "# TRANSFORMS"
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    "Data does not always come in its final processed form that is required for training machine learning algorithms. We use **transforms** to perform some manipulation of the data and make it suitable for training.\n",
    "\n",
    "All TorchVision datasets have two parameters-`transform` to modify the features and `target_transform` to modify the labels - that accept callables containing the transformation logic. The torchvision.transforms module offers several commonly-used transforms out of the box."
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    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor, Lambda\n",
    "\n",
    "ds = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor(),\n",
    "    target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))\n",
    ")"
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    "## `ToTensor()`\n",
    "\n",
    "**ToTensor** converts a PIL image or NumPy `ndarray` into a `FloatTensor` and scales the image’s pixel intensity values in the range `[0., 1.]`"
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    "**Lambda Transforms**\n",
    "\n",
    "Lambda transforms apply any user-defined lambda function. Here, we define a function to turn the integer into a one-hot encoded tensor. It first creates a zero tensor of size 10 (the number of labels in our dataset) and calls scatter_ which assigns a `value=1` on the index as given by the label `y`."
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    "target_transform = Lambda(lambda y: torch.zeros(\n",
    "    10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))"
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