/// <summary>
/// Use the generator to create a list of fake images/
/// </summary>
/// <param name="generator">The generator to use.</param>
/// <param name="batchSize">The batch size.</param>
/// <param name="latentDimensions">The number of dimensions in the latent input vector.</param>
/// <returns>A list of images created by the generator.</returns>
public static IList <IList <float> > GenerateImages(
CNTK.Function generator,
int batchSize,
int latentDimensions)
{
// set up a Gaussian random number generator
var random = new Random();
var gaussianRandom = new GaussianRandom(random);
// set up randomized input for the generator
var random_latent_vectors = gaussianRandom.getFloatSamples(batchSize * latentDimensions);
var random_latent_vectors_nd = new CNTK.NDArrayView(new int[] { latentDimensions, 1, batchSize }, random_latent_vectors, NetUtil.CurrentDevice);
var generator_inputs = new Dictionary <CNTK.Variable, CNTK.Value>()
{
{ generator.Arguments[0], new CNTK.Value(random_latent_vectors_nd) }
};
var generator_outputs = new Dictionary <CNTK.Variable, CNTK.Value>()
{
{ generator.Output, null }
};
// run the generator and collect the images
generator.Evaluate(generator_inputs, generator_outputs, NetUtil.CurrentDevice);
return(generator_outputs[generator.Output].GetDenseData <float>(generator.Output));
}
/// <summary>
/// Get a new batch of images to train the discriminator.
/// The batch will contain generated images with label 1 and actual images with label 0.
/// </summary>
/// <param name="discriminatorVar">The input variable for the discriminator.</param>
/// <param name="generatedImages">The list of generated images.</param>
/// <param name="actualImages">The list of actual images.</param>
/// <param name="batchSize">The batch size.</param>
/// <param name="start">The start position in the training partition.</param>
/// <returns>A tuple with the feature batch and label batch for training.</returns>
public static (CNTK.Value featureBatch, CNTK.Value labelBatch) GetTrainingBatch(
CNTK.Variable discriminatorVar,
IList <IList <float> > generatedImages,
float[][] actualImages,
int batchSize,
int start)
{
// set up a Gaussian random number generator
var random = new Random();
var gaussianRandom = new GaussianRandom(random);
// create a training batch for the discriminator
// the first half of the mini-batch are the fake images (marked with label='1')
// the second half are real images (marked with label='0')
var combined_images = new float[2 * batchSize][];
var labels = new float[2 * batchSize];
for (int i = 0; i < batchSize; i++)
{
combined_images[i] = generatedImages[i].ToArray();
labels[i] = (float)(1 + 0.05 * gaussianRandom.NextGaussian());
combined_images[i + batchSize] = actualImages[start + i];
labels[i + batchSize] = (float)(0.05 * gaussianRandom.NextGaussian());
}
// create batches
var combined_images_minibatch = discriminatorVar.GetBatch(combined_images, 0, combined_images.Length);
var labels_minibatch = CNTK.Value.CreateBatch(new CNTK.NDShape(0), labels, NetUtil.CurrentDevice, true);
// return results
return(combined_images_minibatch, labels_minibatch);
}
public static (CNTK.Value featureBatch, CNTK.Value labelBatch) GetMisleadingBatch(
CNTK.Function gan,
int batchSize,
int latentDimensions)
{
// set up a Gaussian random number generator
var random = new Random();
var gaussianRandom = new GaussianRandom(random);
// prepare a batch to fool the discriminator: we generate fake images
// but we label them as real with label=0
var random_latent_vectors = gaussianRandom.getFloatSamples(batchSize * latentDimensions);
var misleading_targets = new float[batchSize];
var random_latent_vectors_nd = new CNTK.NDArrayView(new int[] { latentDimensions, 1, batchSize }, random_latent_vectors, NetUtil.CurrentDevice);
// return results
return(
new CNTK.Value(random_latent_vectors_nd),
CNTK.Value.CreateBatch(new CNTK.NDShape(0), misleading_targets, NetUtil.CurrentDevice, true)
);
}
