2022-06-27 The attributes of a tensor
Today's question:
The attributes of a tensor
A "tensor" is one of the most basic data structures used in machine learning systems.
Let's go back to basics and focus on the fundamental characteristics of a tensor.
Which of the following are valid attributes that represent a tensor?
- Its number of axes. This attribute is also called the "rank" of the tensor.
- ****Its cardinality. This attribute represents the numerical relationship between the axes of the tensor.
- Its shape. This attribute represents how many dimensions the tensor has along each axis.
- Its data type. This attribute represents the type of values contained in the tensor.
Good job!
Three primary attributes define a tensor:
- Its rank, or the number of axes.
- Its shape, or the number of dimensions per axis.
- Its data type, or the type of data contained in it.
The rank of a tensor refers to the tensor's number of axes.
Examples:
- Rank of a matrix is 2.
- Rank of a vector is 1.
- Rank of a scalar is 0.
The shape of a tensor describes the number of dimensions along each axis.
Examples:
()— scalar(2,)— vector(3, 2)— matrix(3, 2, 5)— 3D tensor
The data type of a tensor refers to the kind of data contained in it.
Examples:
float32float64uint8int64
The second choice mentions "the cardinality of a tensor" as "the numerical relationship between the axes of the tensor." This is not a correct answer.
In summary, the correct answer to the question is the first, third, and fourth choices.
Recommended reading
- Deep Learning with Python, Second Edition covers the topic of tensors really well.
- Check "A Gentle Introduction to Tensors for Machine Learning with NumPy" for a quick introduction to tensors and practical code.
See you tomorrow for another question!