在Python中计算具有不同维度的数组的张量点积

给定两个张量a和b，以及包含两个类似数组对象的array_like对象（a_axes、b_axes），对由a_axes和b_axes指定的轴上的a和b的元素（分量）进行求和。第三个参数可以是单个非负整数类型的标量N；如果是这样的话，a的最后N个维度和b的前N个维度将进行求和。

要计算具有不同维度的数组的张量点积，请使用Python中的numpy.tensordot()方法。a、b参数是要“点积”的张量。

轴参数，整数类型的标量N，如果是一个整数N，则按照顺序对a的最后N个轴和b的前N个轴进行求和。相应轴的大小必须匹配。

步骤

首先，导入所需的库 –

import numpy as np

使用array()方法创建具有不同维度的两个numpy数组−

arr1 = np.array(range(1, 9))
arr1.shape = (2, 2, 2)

arr2 = np.array(('p', 'q', 'r', 's'), dtype=object)
arr2.shape = (2, 2)

显示数组−

print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

检查两个数组的尺寸 –

print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

检查两个数组的形状 –

print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

要计算具有不同维度的数组的张量点积，请使用numpy.tensordot()方法−

print("\nTensor dot product...\n", np.tensordot(arr1, arr2, axes = 1))

示例

import numpy as np

# Creating two numpy arrays with different dimensions using the array() method
arr1 = np.array(range(1, 9))
arr1.shape = (2, 2, 2)
arr2 = np.array(('p', 'q', 'r', 's'), dtype=object)
arr2.shape = (2, 2)

# Display the arrays
print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

# Check the Dimensions of both the arrays
print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

# Check the Shape of both the arrays
print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

# To compute the tensor dot product for arrays with different dimensions, use the numpy.tensordot() method in Python
# The a, b parameters are Tensors to “dot”.
print("\nTensor dot product...\n", np.tensordot(arr1, arr2, axes = 1))

输出

Array1...
[[[1 2]
[3 4]]

[[5 6]
[7 8]]]

Array2...
[['p' 'q']
['r' 's']]

Dimensions of Array1...
3

Dimensions of Array2...
2

Shape of Array1...
(2, 2, 2)

Shape of Array2...
(2, 2)

Tensor dot product...
[[['prr' 'qss']
['ppprrrr' 'qqqssss']]

[['ppppprrrrrr' 'qqqqqssssss']
['ppppppprrrrrrrr' 'qqqqqqqssssssss']]]