wehub-resource-sync
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
from fastai.vision.all import *
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from fastai.distributed import *
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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class Net(nn.Sequential):
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def __init__(self):
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super().__init__(
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nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
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nn.Conv2d(32, 64, 3, 1), nn.MaxPool2d(2), nn.Dropout2d(0.25),
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Flatten(), nn.Linear(9216, 128), nn.ReLU(), nn.Dropout2d(0.5),
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nn.Linear(128, 10), nn.LogSoftmax(dim=1) )
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batch_size,test_batch_size = 256,512
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epochs,lr = 5,1e-2
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kwargs = {'num_workers': 1, 'pin_memory': True}
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transform=transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])
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train_loader = DataLoader(
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datasets.MNIST('../data', train=True, download=True, transform=transform),
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batch_size=batch_size, shuffle=True, **kwargs)
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test_loader = DataLoader(
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datasets.MNIST('../data', train=False, transform=transform),
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batch_size=test_batch_size, shuffle=True, **kwargs)
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if __name__ == '__main__':
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data = DataLoaders(train_loader, test_loader)
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learn = Learner(data, Net(), loss_func=F.nll_loss, opt_func=Adam, metrics=accuracy)
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with learn.distrib_ctx(): learn.fit_one_cycle(epochs, lr)
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