# Get the Inner product of two multi-dimensional arrays in Python

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To get the Inner product of two multi-dimensional arrays, use the numpy.inner() method in Python. Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes. The parameters are 1 and b, two vectors. If a and b are nonscalar, their last dimensions must match.

## Steps

At first, import the required libraries -

import numpy as np

Creating two numpy Two-Dimensional array using the array() method −

arr1 = np.array([[5, 10], [15, 20]])
arr2 = np.array([[6, 12], [18, 24]])

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 get the Inner product of two multi-dimensional arrays, use the numpy.inner() method in Python. Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes −

print("\nResult (Inner Product)...\n",np.inner(arr1, arr2))

## Example

import numpy as np

# Creating two numpy Two-Dimensional array using the array() method
arr1 = np.array([[5, 10], [15, 20]])
arr2 = np.array([[6, 12], [18, 24]])

# 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 get the Inner product of two multi-dimensional arrays, use the numpy.inner() method in Python
print("\nResult (Inner Product)...\n",np.inner(arr1, arr2))

## Output

Array1...
[[ 5 10]
[15 20]]

Array2...
[[ 6 12]
[18 24]]

Dimensions of Array1...
2

Dimensions of Array2...
2

Shape of Array1...
(2, 2)

Shape of Array2...
(2, 2)

Result (Inner Product)...
[[150 330]
[330 750]]