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 |

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