PyTorch torch.inner Function
PyTorch torch Reference Manual
torch.innerIt is a function in PyTorch used to calculate the inner product of two tensors. For vectors, it is equivalent to the dot product; for higher-dimensional tensors, it computes the inner product along specified dimensions.
Function Definition
torch.inner(input, other, out=None)
Parameters:
input(Tensor): The first input tensor.other(Tensor): The second input tensor.out(Tensor, optional): The output tensor.
Return Value:
torch.Tensor: Returns the inner product result.
Usage Examples
Example - Vector Inner Product
import torch
# Create two vectors
a = torch.tensor([1.0, 2.0, 3.0])
b = torch.tensor([4.0, 5.0, 6.0])
# Compute the inner product
result = torch.inner(a, b)
print("Vector a:", a)
print("Vector b:", b)
print("Inner product result:", result)
# Create two vectors
a = torch.tensor([1.0, 2.0, 3.0])
b = torch.tensor([4.0, 5.0, 6.0])
# Compute the inner product
result = torch.inner(a, b)
print("Vector a:", a)
print("Vector b:", b)
print("Inner product result:", result)
The output result is:
向量 a: tensor([1., 2., 3.]) 向量 b: tensor([4., 5., 6.]) 内积结果: tensor(32.)
Example - Matrix Inner Product
import torch
# Create two matrices
A = torch.randn(2, 3)
B = torch.randn(2, 3)
# Compute the inner product along the last dimension
result = torch.inner(A, B)
print("A shape:", A.shape)
print("B shape:", B.shape)
print("Result shape:", result.shape)
# Create two matrices
A = torch.randn(2, 3)
B = torch.randn(2, 3)
# Compute the inner product along the last dimension
result = torch.inner(A, B)
print("A shape:", A.shape)
print("B shape:", B.shape)
print("Result shape:", result.shape)
Other Extensions