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# 1. Basic autograd example 1 (Line 21 to 36)
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# 2. Basic autograd example 2 (Line 39 to 77)
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# 3. Loading data from numpy (Line 80 to 83)
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- # 4. Implementing the input pipline (Line 86 to 113)
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- # 5. Input pipline for custom dataset (Line 116 to 138)
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+ # 4. Implementing the input pipeline (Line 86 to 113)
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+ # 5. Input pipeline for custom dataset (Line 116 to 138)
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# 6. Using pretrained model (Line 141 to 155)
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# 7. Save and load model (Line 158 to 165)
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c = b .numpy () # convert torch tensor to numpy array
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- #===================== Implementing the input pipline =====================#
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+ #===================== Implementing the input pipeline =====================#
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# Download and construct dataset.
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train_dataset = dsets .CIFAR10 (root = '../data/' ,
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train = True ,
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pass
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- #===================== Input pipline for custom dataset =====================#
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+ #===================== Input pipeline for custom dataset =====================#
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# You should build custom dataset as below.
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class CustomDataset (data .Dataset ):
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def __init__ (self ):
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