NeuralNetworkVisualization/main.go

/*
 * MIT License
 *
 * Copyright (c) 2019 Alexey Edelev <semlanik@gmail.com>
 *
 * This file is part of NeuralNetwork project https://git.semlanik.org/semlanik/NeuralNetwork
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy of this
 * software and associated documentation files (the "Software"), to deal in the Software
 * without restriction, including without limitation the rights to use, copy, modify,
 * merge, publish, distribute, sublicense, and/or sell copies of the Software, and
 * to permit persons to whom the Software is furnished to do so, subject to the following
 * conditions:
 *
 * The above copyright notice and this permission notice shall be included in all copies
 * or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
 * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
 * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
 * FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
 * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 * DEALINGS IN THE SOFTWARE.
 */

package main

import (
	"git.semlanik.org/semlanik/NeuralNetwork/neuralnetwork"
	"git.semlanik.org/semlanik/NeuralNetwork/neuralnetwork/gradients"
)

func main() {
	rc := NewRemoteControl()
	sizes := []int{13, 8, 12, 3}
	nn, _ := neuralnetwork.NewNeuralNetwork(sizes, gradients.NewRPropInitializer(gradients.RPropConfig{
		NuPlus:   1.2,
		NuMinus:  0.5,
		DeltaMax: 50.0,
		DeltaMin: 0.000001,
	}))

	nn.SetStateWatcher(rc)
	rc.RunServices()

	// inFile, err := os.Open("./networkstate")
	// if err != nil {
	// 	log.Fatal(err)
	// }
	// defer inFile.Close()
	// nn.LoadState(inFile)

	// nn, _ := neuralnetwork.NewNeuralNetwork(sizes, neuralnetwork.NewBackPropInitializer(0.1))

	// for i := 0; i < nn.Count; i++ {
	// 	if i > 0 {
	// 		fmt.Printf("Weights before:\n%v\n\n", mat.Formatted(nn.Weights[i], mat.Prefix(""), mat.Excerpt(0)))
	// 		fmt.Printf("Biases before:\n%v\n\n", mat.Formatted(nn.Biases[i], mat.Prefix(""), mat.Excerpt(0)))
	// 		fmt.Printf("Z before:\n%v\n\n", mat.Formatted(nn.Z[i], mat.Prefix(""), mat.Excerpt(0)))
	// 	}
	// 	fmt.Printf("A before:\n%v\n\n", mat.Formatted(nn.A[i], mat.Prefix(""), mat.Excerpt(0)))
	// }

	// nn = &neuralnetwork.NeuralNetwork{}
	// inFile, err := os.Open("./data")
	// if err != nil {
	// 	log.Fatal(err)
	// }
	// defer inFile.Close()
	// nn.LoadState(inFile)
	// inFile.Close()

	// failCount = 0
	// training.Reset()
	// for training.NextValidator() {
	// 	dataSet, expect := training.GetValidator()
	// 	index, _ := nn.Predict(dataSet)
	// 	if expect.At(index, 0) != 1.0 {
	// 		failCount++
	// 		// fmt.Printf("Fail: %v, %v\n\n", training.ValidationIndex(), expect.At(index, 0))
	// 	}
	// }

	// fmt.Printf("Fail count: %v\n\n", failCount)

}