This repository was archived by the owner on Mar 12, 2020. It is now read-only.
This repository was archived by the owner on Mar 12, 2020. It is now read-only.
How can i insert an array List as input of my neural network? #44
Description
I have multiples inputs , but most of them are List array and i dont know how can i configure my neural network to accept list array as an input.
Here i have a example XOR(but the inputs are just floats and i cant change to List array):
`
using SiaNet;
using SiaNet.Data;
using SiaNet.Engine;
using SiaNet.Events;
using SiaNet.Layers;
using System;
namespace Sia_XOR
{
class Program
{
static void Main(string[] args)
{
//1º- Setup Engine
Global.UseEngine(SiaNet.Backend.ArrayFire.SiaNetBackend.Instance, DeviceType.CPU);
//2º -Prep Data var (x, y) = PrepDataset(); DataFrameIter trainSet = new DataFrameIter(x, y); //Build model with simple fully connected layers var model = new Sequential(); model.EpochEnd += Model_EpochEnd; model.Add(new Dense(64, ActType.ReLU)); model.Add(new Dense(1, ActType.Sigmoid)); //Compile with Optimizer, Loss and Metric model.Compile(OptimizerType.SGD, LossType.MeanSquaredError, MetricType.BinaryAccurary); // Train for 1000 epoch with batch size of 2 model.Train(trainSet, epochs: 300, batchSize: 2); //Create prediction data to evaluate DataFrame2D predX = new DataFrame2D(2); predX.Load(0, 0, 0, 1); //Result should be 0 and 1 var rawPred = model.Predict(predX); Global.CurrentBackend.Round(rawPred).Print(); Console.ReadLine(); } #region DataSet private static (DataFrame2D, DataFrame2D) PrepDataset() { // We will prepare XOR gate dataset which will be treated as classification problem. // More about this: https://medium.com/@jayeshbahire/the-xor-problem-in-neural-networks-50006411840b DataFrame2D x = new DataFrame2D(2); x.Load(new float[] { 0, 0, 0, 1, 1, 0, 1, 1 }); DataFrame2D y = new DataFrame2D(1); y.Load(new float[] { 0, 1, 1, 0 }); return (x, y); } #endregion private static void Model_EpochEnd(object sender, EpochEndEventArgs e) { Console.WriteLine("Epoch: {0}, Loss: {1}, Metric: {2}", e.Epoch, e.Loss, e.Metric); } } }`