NMZivkovic

public class NeuralLayer
{
    public List<INeuron> Neurons;

    public NeuralLayer()
    {
        Neurons = new List<INeuron>();
    }

    /// <summary>

NMZivkovic

public interface ISynapse
{
    double Weight { get; set; }
    double PreviousWeight { get; set; }
    double GetOutput();

    bool IsFromNeuron(Guid fromNeuronId);
    void UpdateWeight(double learningRate, double delta);
}

NMZivkovic

public class Synapse : ISynapse
{
    internal INeuron _fromNeuron;
    internal INeuron _toNeuron;

    /// <summary>
    /// Weight of the connection.
    /// </summary>
    public double Weight { get; set; }

NMZivkovic

 public class InputSynapse : ISynapse
{
    internal INeuron _toNeuron;

    public double Weight { get; set; }
    public double Output { get; set; }
    public double PreviousWeight { get; set; }

    public InputSynapse(INeuron toNeuron)
    {

NMZivkovic

public class SimpleNeuralNetwork
{
    private NeuralLayerFactory _layerFactory;

    internal List<NeuralLayer> _layers;
    internal double _learningRate;
    internal double[][] _expectedResult;

    /// <summary>
    /// Constructor of the Neural Network.

NMZivkovic

var network = new SimpleNeuralNetwork(3);

var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7), new WeightedSumFunction()));

network.PushExpectedValues(
    new double[][] {
        new double[] { 0 },
        new double[] { 1 },

NMZivkovic

import tensorflow as tf

const1 = tf.constant([[1,2,3], [1,2,3]]);
const2 = tf.constant([[3,4,5], [3,4,5]]);

result = tf.add(const1, const2);

with tf.Session() as sess:
  output = sess.run(result)
  print(output)

NMZivkovic

import tensorflow as tf

var1 = tf.Variable([[1, 2], [1, 2]], name="variable1")
var2 = tf.Variable([[3, 4], [3, 4]], name="variable2")

result = tf.matmul(var1, var2)

with tf.Session() as sess:
  output = sess.run(result)
  print(output)

NMZivkovic

# Import `tensorflow` and `pandas`
import tensorflow as tf
import pandas as pd

COLUMN_NAMES = [
        'SepalLength', 
        'SepalWidth',
        'PetalLength', 
        'PetalWidth', 
        'Species'

NMZivkovic

# Setup feature columns
columns_feat = [
    tf.feature_column.numeric_column(key='SepalLength'),
    tf.feature_column.numeric_column(key='SepalWidth'),
    tf.feature_column.numeric_column(key='PetalLength'),
    tf.feature_column.numeric_column(key='PetalWidth')
]