# 创建一个一维张量
tensor_1d = torch.tensor(list_1d, dtype=torch.float32)
print("1D Tensor:", tensor_1d)

# 创建一个二维张量
tensor_2d = torch.tensor(list_2d, dtype=torch.float32)
print("2D Tensor:\n", tensor_2d)

1D Tensor: tensor([1., 2., 3., 4., 5.])
2D Tensor:
 tensor([[1., 2., 3.],
        [4., 5., 6.]])

tensor_same_zeros = torch.zeros_like(tensor_2d)
print(tensor_same_zeros)
tensor_same_ones = torch.ones_like(tensor_2d)
print(tensor_same_ones)
tensor_same_random = torch.rand_like(tensor_2d)
print(tensor_same_random)

tensor([[0., 0., 0.],
        [0., 0., 0.]])
tensor([[1., 1., 1.],
        [1., 1., 1.]])
tensor([[0.8453, 0.3712, 0.6188],
        [0.3338, 0.0098, 0.7728]])

shape = (2, 3)
tensor_ones = torch.ones(shape)
print(tensor_ones)
tensor_zeros = torch.zeros(shape)
print(tensor_zeros)
tensor_rand = torch.rand(shape)
print(tensor_rand)

tensor([[1., 1., 1.],
        [1., 1., 1.]])
tensor([[0., 0., 0.],
        [0., 0., 0.]])
tensor([[0.1753, 0.0318, 0.0793],
        [0.7621, 0.2927, 0.9383]])

print("shape: ", tensor_2d.shape)
print("Size: ", tensor_2d.size())
print("dtype: ", tensor_2d.dtype)
print("device: ", tensor_2d.device)

shape:  torch.Size([2, 3])
Size:  torch.Size([2, 3])
dtype:  torch.float32
device:  cpu

try:
    import torch_directml
    device = torch_directml.device()
except ImportError:
    device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
print("Using {} device".format(device))

Using privateuseone:0 device

tensor_1d = tensor_1d.to(device)
print(tensor_1d.device)
tensor = torch.tensor(list_2d, device=device, dtype=torch.float32)
print(tensor.device)

privateuseone:0
privateuseone:0

tensor = torch.ones(4, 4)
print('First row: ',tensor[0])
print('First column: ', tensor[:, 0])
print('Last column:', tensor[..., -1])
tensor[:,1] = 0
print(tensor)

First row:  tensor([1., 1., 1., 1.])
First column:  tensor([1., 1., 1., 1.])
Last column: tensor([1., 1., 1., 1.])
tensor([[1., 0., 1., 1.],
        [1., 0., 1., 1.],
        [1., 0., 1., 1.],
        [1., 0., 1., 1.]])

t1 = torch.cat([tensor, tensor, tensor], dim=1)
print(t1)

tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],
        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],
        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],
        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])

# 矩阵乘法的三种方式
y1 = tensor @ tensor.T
y2 = tensor.matmul(tensor.T)

y3 = torch.rand_like(tensor)
torch.matmul(tensor, tensor.T, out=y3)


# 逐元素乘法的三种方式
z1 = tensor * tensor
z2 = tensor.mul(tensor)

z3 = torch.rand_like(tensor)
torch.mul(tensor, tensor, out=z3)

tensor([[1., 0., 1., 1.],
        [1., 0., 1., 1.],
        [1., 0., 1., 1.],
        [1., 0., 1., 1.]])

agg = tensor.sum()
agg_item = agg.item()  
print(agg_item, type(agg_item))

12.0 <class 'float'>

print(tensor, "\n")
tensor.add_(5)
print(tensor)

tensor([[1., 0., 1., 1.],
        [1., 0., 1., 1.],
        [1., 0., 1., 1.],
        [1., 0., 1., 1.]])

[6., 5., 6., 6.],
[6., 5., 6., 6.],
[6., 5., 6., 6.]])