/// <summary>
/// Add a dense layer to a neural network.
/// </summary>
/// <param name="input">The neural network to expand.</param>
/// <param name="outputDim">The number of dimensions in the dense layer.</param>
/// <param name="outputName">The name of the layer.</param>
/// <returns>The neural network with the dense layer added.</returns>
public static CNTK.Variable Dense(
this CNTK.Variable input,
int outputDim,
string outputName = "")
{
var shape = CNTK.NDShape.CreateNDShape(new int[] { outputDim, CNTK.NDShape.InferredDimension });
var timesParam = new CNTK.Parameter(
shape,
CNTK.DataType.Float,
CNTK.CNTKLib.GlorotUniformInitializer(
CNTK.CNTKLib.DefaultParamInitScale,
CNTK.CNTKLib.SentinelValueForInferParamInitRank,
CNTK.CNTKLib.SentinelValueForInferParamInitRank, 1),
CurrentDevice,
"timesParam_" + outputName);
var timesFunction = CNTK.CNTKLib.Times(
timesParam,
input,
1 /* output dimension */,
0 /* CNTK should infer the input dimensions */);
var plusParam = new CNTK.Parameter(
CNTK.NDShape.CreateNDShape(new int[] { CNTK.NDShape.InferredDimension }),
0.0f,
CurrentDevice,
"plusParam_" + outputName);
var result = CNTK.CNTKLib.Plus(plusParam, timesFunction, outputName);
return(result);
}
void create_model(ref CNTK.Function model, ref float[][] labels)
{
var target_image = preprocess_image(target_image_path, img_height, img_width);
var style_reference_image = preprocess_image(style_reference_image_path, img_height, img_width);
var base_model = create_base_content_and_styles_model(img_height, img_width);
labels = compute_labels(base_model, target_image, style_reference_image);
var dream_weights_init = new CNTK.NDArrayView(new int[] { img_width, img_height, 3 }, target_image, computeDevice);
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_layler");
var replacements = new Dictionary <CNTK.Variable, CNTK.Variable>()
{
{ base_model.Arguments[0], dream_layer.Output }
};
model = base_model.Clone(CNTK.ParameterCloningMethod.Freeze, replacements);
var all_outputs = new List <CNTK.Variable>()
{
dream_layer
};
all_outputs.AddRange(model.Outputs);
model = CNTK.Function.Combine(all_outputs, name: "overall_model");
}
void print_debugging_info()
{
if (computeDevice == null)
{
computeDevice = Util.get_compute_device();
}
var features = CNTK.Variable.InputVariable(new int[] { 150, 150, 3 }, CNTK.DataType.Float, "features");
var adjusted_features = CNTK.CNTKLib.Plus(CNTK.Constant.Scalar <float>((float)(-110), computeDevice), features);
var scalar_factor = CNTK.Constant.Scalar <float>((float)(1.0 / 255.0), computeDevice);
var scaled_features = CNTK.CNTKLib.ElementTimes(scalar_factor, adjusted_features);
var convolution_map_size = new int[] { 1, 1, CNTK.NDShape.InferredDimension, 3 };
var W = new CNTK.Parameter(
CNTK.NDShape.CreateNDShape(convolution_map_size),
CNTK.DataType.Float,
CNTK.CNTKLib.GlorotUniformInitializer(CNTK.CNTKLib.DefaultParamInitScale, CNTK.CNTKLib.SentinelValueForInferParamInitRank, CNTK.CNTKLib.SentinelValueForInferParamInitRank, 1),
computeDevice);
var result = CNTK.CNTKLib.Convolution(W, scaled_features,
strides: CNTK.NDShape.CreateNDShape(new int[] { 1 }),
sharing: new CNTK.BoolVector(new bool[] { false }),
autoPadding: new CNTK.BoolVector(new bool[] { true }));
var model = VGG16.get_model(result, computeDevice);
Util.PredorderTraverse(model);
var shape = model.Output.Shape;
Console.WriteLine(shape.AsString());
}
/// <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>
/// Add an embedding layer to the neural network.
/// </summary>
/// <param name="input">The neural network to expand</param>
/// <param name="embeddingDimensions">The number of embedding dimensions to create</param>
/// <returns>The neural network with the dropout layer added</returns>
public static CNTK.Variable Embedding(
this CNTK.Variable input,
int embeddingDimensions)
{
var weight_shape = new int[] { embeddingDimensions, CNTK.NDShape.InferredDimension };
var E = new CNTK.Parameter(
weight_shape,
CNTK.DataType.Float,
CNTK.CNTKLib.GlorotUniformInitializer(),
NetUtil.CurrentDevice);
return(CNTK.CNTKLib.Times(E, input));
}
static public CNTK.Function Convolution2DWithReLU(CNTK.Variable input, int num_output_channels, int[] filter_shape, CNTK.DeviceDescriptor device, bool use_padding = false, bool use_bias = true, string outputName = "")
{
var convolution_map_size = new int[] { filter_shape[0], filter_shape[1], CNTK.NDShape.InferredDimension, num_output_channels };
var W = new CNTK.Parameter(
CNTK.NDShape.CreateNDShape(convolution_map_size),
CNTK.DataType.Float,
CNTK.CNTKLib.GlorotUniformInitializer(CNTK.CNTKLib.DefaultParamInitScale, CNTK.CNTKLib.SentinelValueForInferParamInitRank, CNTK.CNTKLib.SentinelValueForInferParamInitRank, 1),
device, outputName + "_W");
var result = CNTK.CNTKLib.Convolution(W, input, CNTK.NDShape.CreateNDShape(new int[] { 1 }) /*strides*/, new CNTK.BoolVector(new bool[] { true }) /* sharing */, new CNTK.BoolVector(new bool[] { use_padding }));
if (use_bias)
{
var b = new CNTK.Parameter(CNTK.NDShape.CreateNDShape(new int[] { 1, 1, CNTK.NDShape.InferredDimension }), 0.0f, device, outputName + "_b");
result = CNTK.CNTKLib.Plus(result, b);
}
result = CNTK.CNTKLib.ReLU(result, outputName);
return(result);
}
/// <summary>
/// Add a convolution transpose layer to the network.
/// </summary>
/// <param name="input">The neural network to extend</param>
/// <param name="filterShape">The shape of the filters to use</param>
/// <param name="numberOfFilters">The number of filters to use</param>
/// <param name="activation">The activation function to use</param>
/// <param name="padding">Set to true to use padding</param>
/// <param name="strides">The stride lengths to use</param>
/// <param name="bias">Set to true to introduce bias</param>
/// <param name="outputShape">The output shape to generate</param>
/// <param name="reductionRank"></param>
/// <param name="dilation"></param>
/// <param name="maxTempMemSizeInSamples"></param>
/// <param name="name"></param>
/// <returns></returns>
static public CNTK.Variable ConvolutionTranspose(
this CNTK.Variable input,
int[] filterShape,
int numberOfFilters,
Func <CNTK.Variable, CNTK.Function> activation = null,
bool padding = true,
int[] strides = null,
bool bias = true,
int[] outputShape = null,
uint reductionRank = 1,
int[] dilation = null,
uint maxTempMemSizeInSamples = 0,
string name = "")
{
if (strides == null)
{
strides = new int[] { 1 };
}
var sharing = PadToShape(filterShape, true);
var thePadding = PadToShape(filterShape, padding);
if (reductionRank != 1)
{
throw new NotSupportedException("reduction_rank should be 1");
}
thePadding = ConcatenateArrays(thePadding, new bool[] { false });
if (dilation == null)
{
dilation = PadToShape(filterShape, 1);
}
var output_channels_shape = new int[] { numberOfFilters };
var kernel_shape = ConcatenateArrays(filterShape, output_channels_shape, new int[] { CNTK.NDShape.InferredDimension });
var output_full_shape = outputShape;
if (output_full_shape != null)
{
output_full_shape = ConcatenateArrays(outputShape, output_channels_shape);
}
var filter_rank = filterShape.Length;
var init = CNTK.CNTKLib.GlorotUniformInitializer(CNTK.CNTKLib.DefaultParamInitScale, CNTK.CNTKLib.SentinelValueForInferParamInitRank, CNTK.CNTKLib.SentinelValueForInferParamInitRank, 1);
var W = new CNTK.Parameter(kernel_shape, CNTK.DataType.Float, init, NetUtil.CurrentDevice, name = "W");
var r = CNTK.CNTKLib.ConvolutionTranspose(
convolutionMap: W,
operand: input,
strides: strides,
sharing: new CNTK.BoolVector(sharing),
autoPadding: new CNTK.BoolVector(thePadding),
outputShape: output_full_shape,
dilation: dilation,
reductionRank: reductionRank,
maxTempMemSizeInSamples: maxTempMemSizeInSamples);
if (bias)
{
var b_shape = ConcatenateArrays(MakeOnesArray(filterShape.Length), output_channels_shape);
var b = new CNTK.Parameter(b_shape, 0.0f, NetUtil.CurrentDevice, "B");
r = CNTK.CNTKLib.Plus(r, b);
}
if (activation != null)
{
r = activation(r);
}
return(r);
}
