keras.utils.to_categorical() in Python

Introduction:

In this tutorial, we are learning about keras.utils.to_categorical() in Python. Keras provides the numpy utility library, which provides functions to operate on numpy arrays. Using the to_categorical() method, you can convert a numpy array (or) vector with numbers representing variables into a numeric (or) matrix with multiple rows and binary values equal to the number of categories in the data. We can use these tools to create keras services that are completely deterministic and useful for the user of the application. When using the Keras utils to_categorical function, we need to use three parameters. We can create our vectors using the to_categorical function, as well as use reports and tests. Keras utils the to_categorical function to return output containing binary objects of format 0 or 1.

The easiest way to solve for a categorical variable is to subtract the variable from the data. Dataset extraction will also work when our columns do not contain important data. This theory makes sense as it has an indisputable ranking from the category.

Each categorical variable is ordered to include the effect of the ordinal variable. For model independence, we can accept different models. For independent models, we can adopt tree-based coding rules where the variables are numbers. The on-hot encoding to the to_categorical variable creates a new column indicating whether the value exists in the original data. In traditional coding, popular codings do not receive a definite ranking. So this is how we can agree to do the old job.

Syntax:

The syntax of the to_categorical function is given below -

Parameters:

To use categorical parameters in our keras utils to_categorical program, use the tensorflow module, and we need to install the tensorflow module in the system. All parameters of keras utils to_categorical are important when developing a code. The parameters of the to_categorical function are given below -

1. Y: It is an input vector that contains integers representing different classes in the data. It will define the total number of groups. If we do not consider more than one class, the to_categorical function will determine the maximum value of the input vectors and add 1 to the number of classes. The num_classes are the required parameter of the to_categorical function in keras. If we do not define the value, it will be called max(y)+1.
2. num_classes: All classes are represented here. If not specified, it determines the maximum of the input vectors and adds 1 to get the number of classes. The default value is "None".
3. dtype: It is the desired data type required to display the value. By default, its value is "float32".

Return value:

The to_categorical function returns a matrix of binary values ("1" or "0"). The number of rows is equal to the length of the input vector. Also, the number of rows and columns is equal to the number of classes.

Program Code:

Here we give a program code to consider an input vector with 8 groups (Values range from 0 to 7(n-1)) by using the keras.utils.to_categorical() in Python. The code is given below -

Output:

Now, we compile and run the above code and find the result by using the keras.utils.to_categorical() function. The output is given below -

The result is:
[[1 0 0 0 0 0 0]
 [0 0 0 0 1 0 0]
 [0 0 0 0 0 0 1]
 [0 1 0 0 0 0 0]
 [0 0 0 1 0 0 0]
 [0 0 0 0 0 1 0]
 [0 0 0 0 1 0 0]
 [0 0 1 0 0 0 0]
 [1 0 0 0 0 0 0]]

Conclusion:

In this tutorial, we are learning about keras.utils.to_categorical() in Python. We can use these tools to create keras programs that are completely deterministic and useful for the user of the application. Keras.utils.to_categorical provides a numpy utility library that provides functions to operate on numpy arrays. The to_categorical function is used to convert a vector of categories into a matrix of binary classes.