/// <summary>
/// <para>
/// Builds the layer by converting the abstract definition of the layer into
/// a concrete set of instructions for Tensorflow and a layer configuration
/// for use when training the model.
/// </para>
/// <para>This method should register any parameters and initializers with the compilation context.
/// So that they can be used during the training phase. </para>
/// <para>Additionally you are required to store the layer configuration in the
/// <see cref="context"/> property. This information is required as metadata
/// when the model is used.</para>
/// </summary>
/// <param name="context">Use this context to register trainable parameters
/// and build the computational graph for the layer</param>
public override TFOutput Compile(ModelCompilationContext context)
{
if (Configuration != null)
{
return(Configuration.Output);
}
var input = _input.Compile(context);
var inputDimension = _input.OutputShape[_input.OutputShape.Length - 1];
using (var scope = context.Graph.WithScope(Name))
{
TFShape weightsShape = new TFShape(inputDimension, _units);
TFShape biasShape = new TFShape(_units);
var weights = context.Graph.VariableV2(
weightsShape,
TFDataType.Double, operName: "Weights");
var initializers = new List <TFOperation>
{
context.Graph.Assign(weights, _weightsInitializer.Compile(context.Graph, weightsShape)).Operation
};
var parameters = new List <TFOutput>
{
weights
};
context.AddParameters(weights);
var output = context.Graph.MatMul(input, weights);
if (_useBias)
{
var bias = context.Graph.VariableV2(
biasShape,
TFDataType.Double, operName: "Bias");
initializers.Add(context.Graph.Assign(bias,
_biasInitializer.Compile(context.Graph, biasShape)).Operation);
parameters.Add(bias);
output = context.Graph.Add(output, bias);
}
output = _activation.Compile(context, output);
Configuration = new LayerConfiguration(parameters, initializers, output);
context.AddInitializers(initializers);
return(output);
}
}
/// <summary>
/// <para>
/// Builds the layer by converting the abstract definition of the layer into
/// a concrete set of instructions for Tensorflow and a layer configuration
/// for use when training the model.
/// </para>
/// <para>This method should register any parameters and initializers with the compilation context.
/// So that they can be used during the training phase. </para>
/// <para>Additionally you are required to store the layer configuration in the
/// <see cref="Layer.Configuration"/> property. This information is required as metadata
/// when the model is used.</para>
/// <param name="context">Use this context to register trainable parameters
/// and build the computational graph for the layer</param>
public override TFOutput Compile(ModelCompilationContext context)
{
var inputLayer = _input.Compile(context);
var keepProb = context.Graph.Const(_rate);
var output = context.Graph.Dropout(inputLayer, keepProb,
context.Graph.GetTensorShape(inputLayer), _seed);
Configuration = new LayerConfiguration(new TFOutput[] { }, new TFOperation[] { }, output);
return(output);
}
/// <summary>
/// <para>
/// Builds the layer by converting the abstract definition of the layer into
/// a concrete set of instructions for Tensorflow and a layer configuration
/// for use when training the model.
/// </para>
/// <para>This method should register any parameters and initializers with the compilation context.
/// So that they can be used during the training phase. </para>
/// <para>Additionally you are required to store the layer configuration in the
/// <see cref="Configuration"/> property. This information is required as metadata
/// when the model is used.</para>
/// <param name="context">Use this context to register trainable parameters
/// and build the computational graph for the layer</param>
public override TFOutput Compile(ModelCompilationContext context)
{
if (Configuration != null)
{
return(Configuration.Output);
}
if (_shape.Length == 0)
{
throw new ModelCompilationException("Shape must have at least one dimension");
}
var placeholder = context.Graph.Placeholder(TFDataType.Double,
new TFShape(OutputShape), operName: Name);
Configuration = new LayerConfiguration(new TFOutput[] { }, new TFOperation[] { }, placeholder);
return(placeholder);
}
