/// <summary>
/// Adds a dream layer to a neural network.
/// </summary>
/// <param name="input">The neural network to extend.</param>
/// <param name="image">The content image.</param>
/// <param name="width">The width of the content image.</param>
/// <param name="height">The height of the content image.</param>
/// <returns>The neural network extended with a dream layer.</returns>
public static CNTK.Function DreamLayer(
this CNTK.Function input,
float[] image,
int width,
int height)
{
// set up the dream layer
var dream_weights_init = new CNTK.NDArrayView(new int[] { width, height, 3 }, image, NetUtil.CurrentDevice);
var dream_weights = new CNTK.Parameter(dream_weights_init, "the_dream");
var dummy_features = CNTK.Variable.InputVariable(new int[] { 1 }, CNTK.DataType.Float, "dummy_features");
var dream_layer = CNTK.CNTKLib.ElementTimes(dream_weights, dummy_features, "the_dream_layer");
// combine the dream layer with the content and style layers
var replacements = new Dictionary <CNTK.Variable, CNTK.Variable>()
{
{ input.Arguments[0], dream_layer.Output }
};
var model = input.Clone(CNTK.ParameterCloningMethod.Freeze, replacements);
// return the finished model
var all_outputs = new List <CNTK.Variable>()
{
dream_layer
};
all_outputs.AddRange(model.Outputs);
return(CNTK.Function.Combine(all_outputs, name: "overall_model"));
}
/// <summary>
/// Create a Gan by combining a generator and a discriminator.
/// </summary>
/// <param name="generator">The generator to use.</param>
/// <param name="discriminator">The discriminator to use.</param>
/// <returns>A new Gan network constructed out of the generator and discriminator.</returns>
public static CNTK.Function CreateGan(
CNTK.Function generator,
CNTK.Function discriminator)
{
return(discriminator.Clone(
CNTK.ParameterCloningMethod.Share,
replacements: new Dictionary <CNTK.Variable, CNTK.Variable>()
{
{ discriminator.Arguments[0], generator }
}));
}