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Boston

A JSON HTTP server for configuring and using simple, concurrently-accessible neural networks.

Each neural network runs safely in it's own goroutine.

Heavily based on http://www.datadan.io/building-a-neural-net-from-scratch-in-go/

Usage

Note: the entire Full Example JSON below can be POSTed in place of each of the payloads.

Note: I have a nanobox deployed DigitalOcean 1Gb server running at http://192.241.205.72:4343 feel free to mess around.

  1. To create a neural network POST the payload below to http://localhost:4343/learners/create:

    {
	"name": "example",
	"learning_rate": 0.1,
	"input_neurons": 10,
	"hidden_neurons": 4,
	"output_neurons": 4,
	"num_epochs": 5000
}

  2. To train the neural network POST the payload below to http://localhost:4343/learners/train:

    {
	"name": "example",
	"entries": [
		{
			"inputs": [1.0, 0.0, 1.0, 1.0, 0.2, 1.0, 0.0, 1.0, 1.0, 0.2], 
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3],
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 1.0, 1.0, 0.2, 1.0, 0.0, 1.0, 1.0, 0.2], 
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3],
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 1.0, 1.0, 0.2, 1.0, 0.0, 1.0, 1.0, 0.2], 
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3],
			"labels": [0.0, 1.0, 0.1, 1.0]
		}
	],
	"test_split": 0.3
}

  3. To predict inputs on a neural network POST the payload below to http://localhost:4343/learners/predict:

    {
	"name": "example",
	"inputs": [
		[1.0, 0.0, 0.50, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3]
	]
}

  4. To delete a neural network POST the payload below to http://localhost:4343/learners/delete:

    {
	"name": "example"
}

  5. To reset a neural network POST the payload below to http://localhost:4343/learners/delete:

    {
	"name": "example"
}

API

Routes

/learners/list
/learners/create
/learners/delete
/learners/reset
/learners/train
/learners/predict

Full Example JSON:

{
	"name": "example",
	"entries": [
		{
			"inputs": [1.0, 0.0, 1.0, 1.0, 0.2, 1.0, 0.0, 1.0, 1.0, 0.2], 
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3],
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 1.0, 1.0, 0.2, 1.0, 0.0, 1.0, 1.0, 0.2], 
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3],
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 1.0, 1.0, 0.2, 1.0, 0.0, 1.0, 1.0, 0.2], 
			"labels": [0.0, 1.0, 0.1, 1.0]
		},
		{
			"inputs": [1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3],
			"labels": [0.0, 1.0, 0.1, 1.0]
		}
	],
	"inputs": [
		[1.0, 0.0, 0.50, 1.0, 0.0, 0.98, 1.0, 0.0, 0.98, 0.3]
	],
	"learning_rate": 0.1,
	"test_split": 0.3,
	"input_neurons": 10,
	"hidden_neurons": 4,
	"output_neurons": 4,
	"num_epochs": 5000
}