A data Model for the neural network.
Inheritance: Model, ISequenceModel
コード例 #1
0
        /// <summary>Generate model based on a set of examples.</summary>
        /// <param name="x">The Matrix to process.</param>
        /// <param name="y">The Vector to process.</param>
        /// <returns>Model.</returns>
        public override IModel Generate(Matrix x, Vector y)
        {
            // because I said so...
            if (this.MaxIterations == -1)
            {
                this.MaxIterations = x.Rows * 1000;
            }

            var network = Network.Default(this.Descriptor, x, y, this.Activation);
            var model   = new NeuralNetworkModel {
                Descriptor = this.Descriptor, Network = network
            };

            this.OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));

            for (int i = 0; i < this.MaxIterations; i++)
            {
                int idx = i % x.Rows;
                network.Forward(x[idx, VectorType.Row]);
                //OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
                network.Back(y[idx], this.LearningRate);
                var output = String.Format("Run ({0}/{1})", i, this.MaxIterations);
                this.OnModelChanged(this, ModelEventArgs.Make(model, output));
            }

            return(model);
        }
コード例 #2
0
        /// <summary>Generate model based on a set of examples.</summary>
        /// <param name="x">The Matrix to process.</param>
        /// <param name="y">The Vector to process.</param>
        /// <returns>Model.</returns>
        public override IModel Generate(Matrix x, Vector y)
        {
            // because I said so...
            if (this.MaxIterations == -1)
            {
                this.MaxIterations = x.Rows * 1000;
            }

            var network = Network.Default(this.Descriptor, x, y, this.Activation);
            var model = new NeuralNetworkModel { Descriptor = this.Descriptor, Network = network };
            this.OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));

            for (var i = 0; i < this.MaxIterations; i++)
            {
                var idx = i % x.Rows;
                network.Forward(x[idx, VectorType.Row]);

                // OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
                network.Back(y[idx], this.LearningRate);
                var output = string.Format("Run ({0}/{1})", i, this.MaxIterations);
                this.OnModelChanged(this, ModelEventArgs.Make(model, output));
            }

            return model;
        }
コード例 #3
0
        public virtual ISequenceModel Generate(Matrix X, Matrix Y)
        {
            this.Preprocess(X);
            // because I said so...
            if (MaxIterations == -1)
            {
                MaxIterations = 500;
            }

            var network = Network.New().Create(X.Cols, Y.Cols, Activation, OutputFunction, epsilon: Epsilon);

            INetworkTrainer trainer = new GradientDescentTrainer();

            var model = new NeuralNetworkModel
            {
                Descriptor        = Descriptor,
                NormalizeFeatures = base.NormalizeFeatures,
                FeatureNormalizer = base.FeatureNormalizer,
                FeatureProperties = base.FeatureProperties,
                Network           = network
            };

            OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));

            NetworkTrainingProperties properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, this.LearningRate, this.Lambda, this.MaxIterations);

            Vector loss = Vector.Zeros(this.MaxIterations);

            for (int i = 0; i < MaxIterations; i++)
            {
                properties.Iteration = i;

                network.ResetStates(properties);

                for (int x = 0; x < X.Rows; x++)
                {
                    network.Forward(X[x, VectorType.Row]);
                    //OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
                    network.Back(Y[x, VectorType.Row], properties, trainer);

                    loss[i] += network.Cost;
                }

                var output = String.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
                OnModelChanged(this, ModelEventArgs.Make(model, output));

                if (this.LossMinimized(loss, i))
                {
                    break;
                }
            }

            return(model);
        }
コード例 #4
0
        public virtual ISequenceModel Generate(Matrix X, Matrix Y)
        {
            Preprocess(X);
            // because I said so...
            if (MaxIterations == -1)
            {
                MaxIterations = 500;
            }

            var network = Network.New().Create(X.Cols, Y.Cols, Activation, OutputFunction, epsilon: Epsilon);

            var model = new NeuralNetworkModel
            {
                Descriptor        = Descriptor,
                NormalizeFeatures = NormalizeFeatures,
                FeatureNormalizer = FeatureNormalizer,
                FeatureProperties = FeatureProperties,
                Network           = network
            };

            OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));

            var properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, LearningRate, Lambda, MaxIterations);

            for (var i = 0; i < MaxIterations; i++)
            {
                properties.Iteration = i;

                for (var x = 0; x < X.Rows; x++)
                {
                    network.Forward(X[x, VectorType.Row]);
                    //OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
                    network.Back(Y[x, VectorType.Row], properties);
                }

                var output = string.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
                OnModelChanged(this, ModelEventArgs.Make(model, output));
            }

            return(model);
        }
コード例 #5
0
        public virtual ISequenceModel Generate(Matrix X, Matrix Y)
        {
            this.Preprocess(X);
            // because I said so...
            if (MaxIterations == -1) MaxIterations = 500;

            var network = Network.New().Create(X.Cols, Y.Cols, Activation, OutputFunction, epsilon: Epsilon);

            var model = new NeuralNetworkModel
            {
                Descriptor = Descriptor,
                NormalizeFeatures = base.NormalizeFeatures,
                FeatureNormalizer = base.FeatureNormalizer,
                FeatureProperties = base.FeatureProperties,
                Network = network
            };

            OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));

            NetworkTrainingProperties properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, this.LearningRate, this.Lambda, this.MaxIterations);

            for (int i = 0; i < MaxIterations; i++)
            {
                properties.Iteration = i;

                for (int x = 0; x < X.Rows; x++)
                {
                    network.Forward(X[x, VectorType.Row]);
                    //OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
                    network.Back(Y[x, VectorType.Row], properties);
                }

                var output = String.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
                OnModelChanged(this, ModelEventArgs.Make(model, output));
            }

            return model;
        }