public INeuralNetworkLayerUpdater CreateUpdater(INeuralNetworkLayer layer, LayerDescriptor descriptor)
{
var primary = _CreatePrimaryUpdater(layer, descriptor.Regularisation, descriptor.Lambda);
switch (descriptor.WeightUpdate)
{
case WeightUpdateType.Adagrad:
return(_weightUpdater.Adagrad(primary));
case WeightUpdateType.Momentum:
return(_weightUpdater.Momentum(primary, descriptor.Momentum));
case WeightUpdateType.NesterovMomentum:
return(_weightUpdater.NesterovMomentum(primary, descriptor.Momentum));
case WeightUpdateType.RMSprop:
return(_weightUpdater.RMSprop(primary, descriptor.DecayRate));
case WeightUpdateType.Adam:
return(_weightUpdater.Adam(primary, descriptor.DecayRate, descriptor.DecayRate2));
default:
return(primary);
}
}
/// <summary>
/// Clones the current descriptor
/// </summary>
public LayerDescriptor Clone()
{
var ret = new LayerDescriptor();
CopyTo(ret);
return(ret);
}
public INeuralNetworkTrainer CreateBatchTrainer(LayerDescriptor descriptor, params int[] layerSizes)
{
return(CreateBatchTrainer(
Enumerable.Range(0, layerSizes.Length - 1)
.Select(i => CreateTrainer(layerSizes[i], layerSizes[i + 1], descriptor))
.ToList()
));
}
/// <summary>
/// Copies attributes from this layer to the target layer
/// </summary>
/// <param name="layer">The target layer</param>
public void CopyTo(LayerDescriptor layer)
{
layer.Activation = this.Activation;
layer.WeightInitialisation = this.WeightInitialisation;
layer.DecayRate = this.DecayRate;
layer.LayerTrainer = this.LayerTrainer;
layer.Dropout = this.Dropout;
layer.Momentum = this.Momentum;
layer.Regularisation = this.Regularisation;
layer.WeightUpdate = this.WeightUpdate;
layer.Lambda = this.Lambda;
layer.DecayRate2 = this.DecayRate2;
}
StandardFeedForward _ReadFeedForward(NetworkLayer layer)
{
var descriptor = LayerDescriptor.CreateFrom(layer);
var bias = _lap.Create(layer.OutputSize, 0f);
bias.Data = layer.Bias;
var weight = _lap.Create(layer.InputSize, layer.OutputSize, 0f);
weight.Data = layer.Weight;
return(new StandardFeedForward(weight, bias, _activation[descriptor.Activation]));
}
public INeuralNetworkLayerTrainer CreateTrainer(INeuralNetworkLayer layer, LayerDescriptor descriptor)
{
var layerUpdater = CreateUpdater(layer, descriptor);
return(_CreateLayerUpdater(layerUpdater, descriptor));
}
public INeuralNetworkLayerTrainer CreateTrainer(int inputSize, int outputSize, LayerDescriptor descriptor)
{
var layerUpdater = CreateUpdater(inputSize, outputSize, descriptor);
return(_CreateLayerUpdater(layerUpdater, descriptor));
}
INeuralNetworkLayerTrainer _CreateLayerUpdater(INeuralNetworkLayerUpdater layerUpdater, LayerDescriptor init)
{
switch (init.LayerTrainer)
{
case LayerTrainerType.DropConnect:
return(_trainer.DropConnect(layerUpdater, init.Dropout));
case LayerTrainerType.Dropout:
return(_trainer.Dropout(layerUpdater, init.Dropout));
default:
return(_trainer.Standard(layerUpdater));
}
}
public INeuralNetworkRecurrentLayer CreateFeedForwardRecurrentLayer(int inputSize, int outputSize, LayerDescriptor descriptor)
{
return(new Training.Layer.Recurrent.FeedForward(CreateTrainer(inputSize, outputSize, descriptor)));
}
public INeuralNetworkRecurrentLayer CreateLstmRecurrentLayer(int inputSize, int outputSize, LayerDescriptor descriptor)
{
return(new Training.Layer.Recurrent.Lstm(inputSize, outputSize, this, descriptor));
}
public INeuralNetworkLayer CreateTiedLayer(INeuralNetworkLayer layer, LayerDescriptor descriptor)
{
return(new TiedLayer(_lap, layer, _weightInitialisation[descriptor.WeightInitialisation]));
}
public INeuralNetworkLayer CreateLayer(int inputSize, int outputSize, LayerDescriptor descriptor)
{
return(new Standard(_lap, inputSize, outputSize, descriptor, _activation[descriptor.Activation], _weightInitialisation[descriptor.WeightInitialisation]));
}