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main.go
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main.go
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/*
* This file is part of the N2CMU (https://github.com/nthnn/n2cmu).
* Copyright (c) 2024 Nathanne Isip.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, version 3.
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package main
import (
"machine"
"github.com/nthnn/n2cu/n2"
"github.com/nthnn/n2cu/uart"
)
func main() {
var network n2.NeuralNetwork
var epoch uint16 = 0
led := machine.PC13
led.Configure(machine.PinConfig{Mode: machine.PinOutput})
led.High()
machine.UART1.SetBaudRate(31250)
for {
command, error := machine.Serial.ReadByte()
if error != nil {
continue
}
switch command {
case N2CMU_PROC_HANDSHAKE:
uart.WriteOk()
break
case N2CMU_PROC_CPU_RESET:
machine.CPUReset()
break
case N2CMU_NET_CREATE:
led.Low()
inputCount := uart.ReadUint8()
hiddenCount := uart.ReadUint8()
outputCount := uart.ReadUint8()
network.InitNetwork(inputCount, hiddenCount, outputCount)
led.High()
break
case N2CMU_NET_RESET:
led.Low()
network.ResetNetwork()
led.High()
break
case N2CMU_NET_TRAIN:
led.Low()
dataSize := int(uart.ReadUint16())
dataSet := make([][]float32, dataSize)
for j := 0; j < dataSize; j++ {
data := make([]float32, network.InputCount)
for k := 0; k < int(network.InputCount); k++ {
data[k] = uart.ReadFloat32()
}
dataSet[j] = data
}
output := make([][]float32, dataSize)
for j := 0; j < dataSize; j++ {
data := make([]float32, network.OutputCount)
for k := 0; k < int(network.OutputCount); k++ {
data[k] = uart.ReadFloat32()
}
output[j] = data
}
learningRate := uart.ReadFloat32()
for i := 0; i < int(epoch); i++ {
dataIndex := i % dataSize
network.Train(dataSet[dataIndex], output[dataIndex], learningRate)
}
uart.WriteOk()
led.High()
break
case N2CMU_NET_INFER:
led.Low()
input := make([]float32, network.InputCount)
for j := 0; j < int(network.InputCount); j++ {
input[j] = uart.ReadFloat32()
}
output := network.Infer(input)
for i := 0; i < int(network.OutputCount); i++ {
uart.WriteFloat32(output[i])
}
uart.WriteOk()
led.High()
break
case N2CMU_SET_INPUT_COUNT:
network.InputCount = uart.ReadUint8()
network.InitMatrix()
break
case N2CMU_GET_INPUT_COUNT:
uart.WriteUint8(network.InputCount)
break
case N2CMU_SET_HIDDEN_COUNT:
network.HiddenCount = uart.ReadUint8()
network.InitMatrix()
break
case N2CMU_GET_HIDDEN_COUNT:
uart.WriteUint8(network.HiddenCount)
break
case N2CMU_SET_OUTPUT_COUNT:
network.OutputCount = uart.ReadUint8()
network.InitMatrix()
break
case N2CMU_GET_OUTPUT_COUNT:
uart.WriteUint8(network.OutputCount)
break
case N2CMU_SET_HIDDEN_NEURON:
for i := 0; i < int(network.HiddenCount); i++ {
network.HiddenNeuron[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_HIDDEN_NEURON:
for i := 0; i < int(network.HiddenCount); i++ {
uart.WriteFloat32(network.HiddenNeuron[i])
}
break
case N2CMU_SET_OUTPUT_NEURON:
for i := 0; i < int(network.OutputCount); i++ {
network.OutputNeuron[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_OUTPUT_NEURON:
for i := 0; i < int(network.OutputCount); i++ {
uart.WriteFloat32(network.OutputNeuron[i])
}
break
case N2CMU_SET_HIDDEN_WEIGHTS:
for i := 0; i < len(network.HiddenWeights); i++ {
network.HiddenWeights[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_HIDDEN_WEIGHTS:
for i := 0; i < len(network.HiddenWeights); i++ {
uart.WriteFloat32(network.HiddenWeights[i])
}
break
case N2CMU_SET_OUTPUT_WEIGHTS:
for i := 0; i < len(network.OutputWeights); i++ {
network.OutputWeights[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_OUTPUT_WEIGHTS:
for i := 0; i < len(network.OutputWeights); i++ {
uart.WriteFloat32(network.OutputWeights[i])
}
break
case N2CMU_SET_HIDDEN_BIAS:
for i := 0; i < int(network.HiddenCount); i++ {
network.HiddenBias[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_HIDDEN_BIAS:
for i := 0; i < int(network.HiddenCount); i++ {
uart.WriteFloat32(network.HiddenBias[i])
}
break
case N2CMU_SET_OUTPUT_BIAS:
for i := 0; i < int(network.OutputCount); i++ {
network.OutputBias[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_OUTPUT_BIAS:
for i := 0; i < int(network.OutputCount); i++ {
uart.WriteFloat32(network.OutputBias[i])
}
break
case N2CMU_SET_HIDDEN_GRAD:
for i := 0; i < int(network.HiddenCount); i++ {
network.HiddenGrad[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_HIDDEN_GRAD:
for i := 0; i < int(network.HiddenCount); i++ {
uart.WriteFloat32(network.HiddenGrad[i])
}
break
case N2CMU_SET_OUTPUT_GRAD:
for i := 0; i < int(network.OutputCount); i++ {
network.OutputGrad[i] = uart.ReadFloat32()
}
uart.WriteOk()
break
case N2CMU_GET_OUTPUT_GRAD:
for i := 0; i < int(network.OutputCount); i++ {
uart.WriteFloat32(network.OutputGrad[i])
}
break
case N2CMU_SET_EPOCH_COUNT:
epoch = uart.ReadUint16()
break
case N2CMU_GET_EPOCH_COUNT:
uart.WriteUint16(epoch)
break
}
}
}