Sample Hyper Parameters Definition

The configurations included in a sample hyper parameters definition JSON file are as follows.

[
    {
        "key": "Iterations",
        "value": "100"
    },
    {
        "key": "Learning_Rate",
        "value": "0.1"
    },
    {
        "key": "SGD_Data_Fraction",
        "value": "1"
    }
]

Parameter definitions

The definitions of the parameters in the above sample hyper parameters definition JSON file are as follows.

Parameter	Description	Required	Default value	Example
Iterations	Number of iterations of gradient descent to run.	Yes	None	100
Learning_Rate	A constant (usually a small number less than 1.0 ), which is used in machine learning algorithms to control the speed of the learning.	Yes	None	0.1
SGD_Data_Fraction	The fraction of data to be used per iteration in the Stochastic Gradient Descent (SGD) optimization algorithms. If this is set to 1.0, the machine learning algorithm runs with Full-batch Gradient Descent optimizer.	Yes	None	1