39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import torch
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import torchvision
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from torchvision import transforms
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from torch.utils.data import DataLoader
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train_set = torchvision.datasets.FashionMNIST(
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root='./data'
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,train=True
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,download=True
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,transform=transforms.Compose([
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transforms.ToTensor()
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])
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)
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loader = DataLoader(train_set, batch_size=len(train_set), num_workers=1)
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# load whole dataset
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input_data, out_data = next(iter(loader))
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out_data = out_data.float()
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# compute mean and std only over batch dimension
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m_in, s_in = input_data.mean(dim=0), input_data.std(dim=0)
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m_out, s_out = out_data.mean(dim=0), out_data.std(dim=0)
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input_tf = transforms.Normalize(m_in, s_in)
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out_tf = transforms.Normalize(m_out, s_out)
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transformed_input = input_tf(input_data)
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transformed_output = torch.sigmoid(out_tf(out_data))
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# scale sigmoid output [0, 1] to acceleration interval [a_min, a_max]
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a_min, a_max = (-4, 2)
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# compute m and s such that normalization with m and s results in desired scaling
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s = 1 / (a_max - a_min)
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m = - s * a_min
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scaling = transforms.Normalize(m, s)
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# DOES NOT WORK SINCE TORCHVISION NORMALIZE WORKS ONLY ON IMAGES
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scaled_output = scaling(transformed_output)
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