50 lines
983 B
Python
50 lines
983 B
Python
import torch
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x = torch.tensor([[1, 2], [3, 4]])
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print(x)
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print("x.tile((2)) -> ", x.tile((2)).shape)
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print(x.tile((2)))
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print()
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print("x.tile((2, 1)) -> ", x.tile((2, 1)).shape)
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print(x.tile((2, 1)))
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print()
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print("x.tile((2, 1, 2)) -> ", x.tile((2, 1, 2)).shape)
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print(x.tile((2, 1, 2)))
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print()
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print("x.tile((2, 1, 1)) -> ", x.tile((2, 1, 1)).shape)
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print(x.tile((2, 1, 1)))
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print()
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y = torch.tensor([[2, 1], [3, 4]])
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print(y.ne(x))
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print()
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print(x.prod(1))
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print(x.prod(0))
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print()
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print(x.unsqueeze(1).shape)
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print(x.unsqueeze(1).squeeze(1).shape)
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x = torch.tensor([[1, 2], [3, 4]]).to(float)
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print(x.mean(1))
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print(x.mean(0))
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print(x.mean(0, keepdim=True))
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print()
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print()
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x = torch.tensor([[1, 2], [3, 4]])
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print(x.flatten(0))
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x = torch.tensor([[1, 2], [3, 4]])
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print(torch.stack((x, x), 1))
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print(torch.cat((x, x), 1))
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# So if A and B are of shape (3, 4):
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# torch.cat([A, B], dim=0) will be of shape (6, 4)
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# torch.stack([A, B], dim=0) will be of shape (2, 3, 4)
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