63 lines
1.3 KiB
Python
63 lines
1.3 KiB
Python
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import torch
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import numpy as np
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data = [[1,2],[3,4]]
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x_data = torch.tensor(data)
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print(x_data)
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np_array = np.array(data)
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x_np = torch.from_numpy(np_array)
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x_ones = torch.ones_like(x_data)
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print(f"Ones Tensor: \n {x_ones}\n")
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print(x_ones.dtype)
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x_rand = torch.rand_like(x_data, dtype=torch.float)
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print(f"Random Tensor: \n {x_rand} \n")
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print(x_rand.dtype)
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shape=(2, 3)
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rand_tensor = torch.rand(shape)
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ones_tensor = torch.ones(shape)
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zeros_tensor = torch.zeros(shape)
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print(f"Random Tensor: \n {rand_tensor} \n")
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print(f"Ones Tensor: \n {ones_tensor} \n")
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print(f"Zeros Tensor: \n {zeros_tensor} \n")
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tensor = torch.rand(3, 4)
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print(f"Shape of tensor: {tensor.shape}")
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print(f"Datatype of tensor: {tensor.dtype}")
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print(f"Device tensor is stored on: {tensor.device}")
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tensor = torch.ones(4, 4)
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if torch.cuda.is_available():
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tensor = tensor.to("cuda")
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print(f"First row: {tensor[0]}")
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print(f"First column: {tensor[:, 0]}")
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print(f"Last Column: {tensor[..., -1]}")
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tensor[:, 1] = 0
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print(tensor)
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t1 = torch.cat([tensor, tensor, tensor], dim=1)
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print(t1)
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y1 = tensor @ tensor.T
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y2 = tensor.matmul(tensor.T)
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print(y1)
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print(y2)
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y3 = torch.rand_like(y1)
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torch.matmul(tensor, tensor.T, out=y3)
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print(y3)
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print(y3.cpu().numpy())
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agg = y3.sum()
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print(agg)
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print(agg.item())
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