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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
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import torch
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import torch.nn as nn
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from tqdm import tqdm
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from torch.utils.data import TensorDataset, DataLoader
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# Create a simple toy dataset example, normally this
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# would be doing custom class with __getitem__ etc,
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# which we have done in custom dataset tutorials
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x = torch.randn((1000, 3, 224, 224))
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y = torch.randint(low=0, high=10, size=(1000, 1))
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ds = TensorDataset(x, y)
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loader = DataLoader(ds, batch_size=8)
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model = nn.Sequential(
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nn.Conv2d(3, 10, kernel_size=3, padding=1, stride=1),
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nn.Flatten(),
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nn.Linear(10*224*224, 10),
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)
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NUM_EPOCHS = 100
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for epoch in range(NUM_EPOCHS):
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loop = tqdm(loader)
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for idx, (x, y) in enumerate(loop):
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scores = model(x)
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# here we would compute loss, backward, optimizer step etc.
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# you know how it goes, but now you have a nice progress bar
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# with tqdm
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# then at the bottom if you want additional info shown, you can
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# add it here, for loss and accuracy you would obviously compute
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# but now we just set them to random values
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loop.set_description(f"Epoch [{epoch}/{NUM_EPOCHS}]")
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loop.set_postfix(loss=torch.rand(1).item(), acc=torch.rand(1).item())
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# There you go. Hope it was useful :)
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