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This article mainly introduces PyTorch basic operation example analysis, the article is very detailed, has a certain reference value, interested friends must read!

create data

torch.empty()

Create an empty tensor matrix.

Format:

torch.empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) → Tensor

Parameters:

size: The shape of the generator matrix, required

dtype: Data type, default is None

Examples:

#Create a matrix a = torch.empty(2,2)print(a)#Create a matrix b = torch.empty(3,3)print(b) of shape [3,3]

Output:

tensor([[0., 0.],

[0., 0.]])

tensor([[0., 0., 0.],

[0., 0., 0.],

[0., 0., 0.]])

torch.zeros()

Create a matrix of all zeros.

Format:

torch.zeros(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Parameters:

size: The shape of the generator matrix, required

dtype: Data type, default is None

Examples:

#Create an array of all zeros of shape [2,2] a = torch.zeros([2,2], dtype= torch. float32)print(a)#Create an array of all zeros of shape [3,3] b = torch.zeros([3,3], dtype= torch. float32)print(b)

Output:

tensor([[0., 0.],

[0., 0.]])

tensor([[0., 0., 0.],

[0., 0., 0.],

[0., 0., 0.]])

torch.ones()

Create an all-one matrix.

Format:

torch.ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Parameters:

size: The shape of the generator matrix, required

dtype: Data type, default is None

Examples:

#Create an all-one array of shape [2,2] a = torch.ones([2,2], dtype= torch. float32)print(a)#Create an all-one array of shape [3,3] b = torch.ones([3,3], dtype= torch. float32)print(b)

Output:

tensor([[1., 1.],

[1., 1.]])

tensor([[1., 1., 1.],

[1., 1., 1.],

[1., 1., 1.]])

torch.tensor()

Create tensors from data.

Format:

torch.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor

Parameters:

data: data (array, tuple, ndarray, scalar)

dtype: Data type, default is None

Examples:

#Create tensor from data array = np.arange(1, 10).reshape(3, 3)print(array)print(type(array))tensor = torch.tensor(array)print(tensor) print(type(tensor))

Output:

[[1 2 3]

[4 5 6]

[7 8 9]]

tensor([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]], dtype=torch.int32)

torch.rand()

Create a tensor matrix of random numbers 0~1.

Format:

torch.rand(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Parameters:

size: The shape of the generator matrix, required

dtype: data type, default is None

Examples:

#Create a random number matrix of shape [2, 2] rand = torch.rand(2, 2)print(rand)

Output:

tensor([[0.6209, 0.3424],

[0.3506, 0.7986]])

mathematical operations

torch.add()

Returns the added tensor.

Format:

torch.add(input, other, *, out=None) → Tensor

Examples:

#tensor addition input1 = torch.tensor([[1, 2], [3, 4]])print(input1) input2 = torch.tensor([[4, 3], [2, 1])print(input2)output = torch.add(input1, input2)print(output)

Output:

tensor([[1, 2],

[3, 4]])

tensor([[4, 3],

[2, 1]])

tensor([[5, 5],

[5, 5]])

Note: The tensor shapes must be consistent, otherwise an error will be reported.

torch.sub()

Returns the subtracted tensor.

Examples:

#tensor subtract input1 = torch.tensor([[1, 2], [3, 4]])print(input1) input2 = torch.tensor([[4, 3], [2, 1])print(input2)output = torch.sub(input1, input2)print(output)

Output:

tensor([[1, 2],

[3, 4]])

tensor([[4, 3],

[2, 1]])

tensor([[-3, -1],

[ 1, 3]])

torch.matmul()

Examples:

#tensor matrix multiplication input1 = torch.tensor([[1, 1, 1])print(input1) input2 = torch.tensor([[3], [3], [3])print(input2)output = torch.matmul(input1, input2)print(output)

Output:

tensor([[1, 1, 1]])

tensor([[3],

[3],

[3]])

tensor([[9]])

index operations

An index can help us quickly find specific information in a tensor.

Examples:

#simple index operations ones = torch.ones([3, 3])print(ones[: 2])print(ones[:, : 2])

Debug output:

tensor([[1., 1., 1.],

[1., 1., 1.]])

tensor([[1., 1.],

[1., 1.],

[1., 1.]])

That's all for "PyTorch Basic Operation Sample Analysis". Thanks for reading! Hope to share the content to help everyone, more relevant knowledge, welcome to pay attention to the industry information channel!

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