using System;
using ConvNetSharp.Core;
using ConvNetSharp.Core.Layers.Double;
using ConvNetSharp.Core.Training.Double;
using ConvNetSharp.Volume;
using ConvNetSharp.Volume.Double;

namespace MinimalExample
{
    internal class Program
    {
        private static void Main()
        {
            // specifies a 2-layer neural network with one hidden layer of 20 neurons
            var net = new Net<double>();

            // input layer declares size of input. here: 2-D data
            // ConvNetJS works on 3-Dimensional volumes (width, height, depth), but if you're not dealing with images
            // then the first two dimensions (width, height) will always be kept at size 1
            net.AddLayer(new InputLayer(1, 1, 2));

            // declare 20 neurons
            net.AddLayer(new FullyConnLayer(20));

            // declare a ReLU (rectified linear unit non-linearity)
            net.AddLayer(new ReluLayer());

            // declare a fully connected layer that will be used by the softmax layer
            net.AddLayer(new FullyConnLayer(10));

            // declare the linear classifier on top of the previous hidden layer
            net.AddLayer(new SoftmaxLayer(10));

            // forward a random data point through the network
            var x =  BuilderInstance.Volume.From(new[] { 0.3, -0.5 }, new Shape(2));

            var prob = net.Forward(x);

            // prob is a Volume. Volumes have a property Weights that stores the raw data, and WeightGradients that stores gradients
            Console.WriteLine("probability that x is class 0: " + prob.Get(0)); // prints e.g. 0.50101

            var trainer = new SgdTrainer(net) { LearningRate = 0.01, L2Decay = 0.001 };
            trainer.Train(x, BuilderInstance.Volume.From(new[] { 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }, new Shape(1, 1, 10, 1))); // train the network, specifying that x is class zero

            var prob2 = net.Forward(x);
            Console.WriteLine("probability that x is class 0: " + prob2.Get(0));
            // now prints 0.50374, slightly higher than previous 0.50101: the networks
            // weights have been adjusted by the Trainer to give a higher probability to
            // the class we trained the network with (zero)
        }
    }
}

var net = FluentNet<double>.Create(24, 24, 1)
                   .Conv(5, 5, 8).Stride(1).Pad(2)
                   .Relu()
                   .Pool(2, 2).Stride(2)
                   .Conv(5, 5, 16).Stride(1).Pad(2)
                   .Relu()
                   .Pool(3, 3).Stride(3)
                   .FullyConn(10)
                   .Softmax(10)
                   .Build();

using ConvNetSharp.Core.Serialization;

[...]

// Serialize to json 
var json = net.ToJson();

// Deserialize from json
Net deserialized = SerializationExtensions.FromJson<double>(json);

using ConvNetSharp.Flow.Serialization;

[...]

// Serialize to two files: MyNetwork.graphml (graph structure) / MyNetwork.json (volume data)
net.Save("MyNetwork");

// Deserialize from files
var deserialized = SerializationExtensions.Load<double>("MyNetwork", false)[0];  // first element is the network (second element is the cost if it was saved along)