Scalar Vector Matrix Tensor

import numpy as np

# Scalar
scalar_a = 3

# Vector
vector_a = np.arange(5)

# Matrix
matrix_a = np.random.random((6, 5))

# Tensor
tensor_a = np.random.random((7, 6, 5))

# broadcast
matrix_b = matrix_a + vector_a


tensor_b = tensor_a + vector_a
tensor_c = tensor_a + matrix_a

What is Broadcast Broad Cast Rule

1 If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.

2 The two arrays are said to be compatible in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.

3 The arrays can be broadcast together if they are compatible in all dimensions.

4 After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.

5 In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension