Popular understanding of tensor in deep learning and creation of tensor

1. The role of tensors in deep learning

Tensors in deep learning are mainly used to describe an object with numbers. For example, to describe a color picture, we can use (length, width, color) to describe, so to describe a color picture, you need to use three-dimensional Zhang Quantity, if we want to describe a collection of color pictures, we need to use (picture number, length, width, color) to describe, so to describe a collection of color pictures, we need to use four-dimensional tensor.

2. Tensor expression in deep learning

0-dimensional tensor:[1] A
0-dimensional tensor is a scalar, which is a number if it is plain.

1-dimensional tensor:[1,2,3,4,5]
1-dimensional tensor is a vector

2-dimensional tensor:
$\begin{matrix}1 & 2&3&4&5\\3&4&5&6 &4\\6&7&8&9&10\end{matrix}$
2-dimensional tensor is a matrix

3-dimensional tensor:

A 3-dimensional tensor is a stack of multiple 2-dimensional tensors

4-dimensional tensor:

4D tensor is a stack of multiple 3D tensors

The following picture is more intuitive:

3. Use numpy to represent tensors

0-dimensional tensor:

1-dimensional tensor:

You can get the dimensions of the tensor with the following command:

2-dimensional tensor:

3-dimensional tensor:

4. View the shape of the tensor

import numpy as np

unit_num = np.array([[[1,2,3],[2,3,4]],[[1,2,3],[2,3,4]]])
print(unit_num.shape)

result:

(2, 2, 3)