/// <summary>
/// Return a summary description of the neural network.
/// </summary>
/// <param name="model">The neural network to describe</param>
/// <returns>A string description of the neural network</returns>
public static string ToSummary(this CNTK.Function model)
{
var sb = new StringBuilder();
sb.AppendFormat("\tInput = " + model.Arguments[0].Shape.AsString());
sb.Append(Environment.NewLine);
for (int i = 0; i < model.Outputs.Count; i++)
{
sb.AppendFormat("\tOutput = " + model.Outputs[i].Shape.AsString());
sb.Append(Environment.NewLine);
}
sb.Append(Environment.NewLine);
var numParameters = 0;
foreach (var x in model.Parameters())
{
var shape = x.Shape;
var p = shape.TotalSize;
sb.AppendFormat(string.Format("\tFilter Shape:{0,-30} Params:{1}", shape.AsString(), p));
sb.Append(Environment.NewLine);
numParameters += p;
}
sb.AppendFormat(string.Format("\tTotal Number of Parameters: {0:N0}", numParameters));
sb.Append(Environment.NewLine);
return(sb.ToString());
}
static public void log_number_of_parameters(CNTK.Function model)
{
Console.WriteLine("\nModel Summary");
Console.WriteLine("\tInput = " + model.Arguments[0].Shape.AsString());
Console.WriteLine("\tOutput = " + model.Output.Shape.AsString());
Console.WriteLine("");
var numParameters = 0;
foreach (var x in model.Parameters())
{
var shape = x.Shape;
var p = shape.TotalSize;
Console.WriteLine(string.Format("\tFilter Shape:{0,-30} Param #:{1}", shape.AsString(), p));
numParameters += p;
}
Console.WriteLine(string.Format("\nTotal Number of Parameters: {0:N0}", numParameters));
Console.WriteLine("---\n");
}
void train(CNTK.Function model, float[][] labels)
{
var content_and_style_outputs = traverse_content_and_styles_nodes(model);
var label_variables = new List <CNTK.Variable>();
for (int i = 0; i < labels.Length; i++)
{
var shape = content_and_style_outputs[i].Shape;
var input_variable = CNTK.Variable.InputVariable(shape, CNTK.DataType.Float, "content_and_style_" + i);
label_variables.Add(input_variable);
}
var loss_function = create_loss_function(model, content_and_style_outputs, label_variables);
var pv = new CNTK.ParameterVector((System.Collections.ICollection)model.Parameters());
var learner = CNTK.CNTKLib.AdamLearner(pv, new CNTK.TrainingParameterScheduleDouble(10), new CNTK.TrainingParameterScheduleDouble(0.95));
var trainer = CNTK.CNTKLib.CreateTrainer(model, loss_function, loss_function, new CNTK.LearnerVector()
{
learner
});
var batch = create_batch(loss_function, labels);
Console.WriteLine("Training on a " + computeDevice.AsString());
var startTime = DateTime.Now;
for (int i = 0; i < 301; i++)
{
trainer.TrainMinibatch(batch, true, computeDevice);
if (i % 100 == 0)
{
Console.WriteLine($"epoch {i}, loss={trainer.PreviousMinibatchLossAverage():F3}");
}
}
var elapsedTime = DateTime.Now.Subtract(startTime);
Console.WriteLine($"Done in {elapsedTime.TotalSeconds:F1} seconds");
}
/// <summary>
/// Get an RMSProp learner to train the network.
/// </summary>
/// <param name="input">The network to train.</param>
/// <param name="learningRateSchedule">The learning rate schedule.</param>
/// <param name="gamma">The gamma value.</param>
/// <param name="inc">The inc value.</param>
/// <param name="dec">The dec value.</param>
/// <param name="max">The max value.</param>
/// <param name="min">The min value.</param>
/// <returns>An RMSProp learner to train the network.</returns>
public static CNTK.Learner GetRMSPropLearner(
this CNTK.Function input,
double learningRateSchedule,
double gamma,
double inc,
double dec,
double max,
double min)
{
var parameterVector = new CNTK.ParameterVector((System.Collections.ICollection)input.Parameters());
return(CNTK.CNTKLib.RMSPropLearner(
parameterVector,
new CNTK.TrainingParameterScheduleDouble(learningRateSchedule),
gamma,
inc,
dec,
max,
min));
}
List <List <double> > train_mse_cntk(bool sequence_mode, CNTK.Variable x, CNTK.Variable y, CNTK.Function model, GeneratorsInfo gi, int epochs, int steps_per_epoch, CNTK.DeviceDescriptor computeDevice)
{
var loss_function = CNTK.CNTKLib.SquaredError(model, y);
var accuracy_function = loss_function;
var lr = 0.001;
var parameterVector = new CNTK.ParameterVector((System.Collections.ICollection)model.Parameters());
var learner = CNTK.CNTKLib.AdamLearner(parameterVector,
new CNTK.TrainingParameterScheduleDouble(lr /*, (uint)batch_size*/),
new CNTK.TrainingParameterScheduleDouble(0.9 /*, (uint)batch_size*/),
unitGain: false);
var trainer = CNTK.CNTKLib.CreateTrainer(model, loss_function, accuracy_function, new CNTK.LearnerVector()
{
learner
});
var evaluator = CNTK.CNTKLib.CreateEvaluator(accuracy_function);
var history = fit_generator(sequence_mode, x, y, model, trainer, evaluator, gi, epochs, steps_per_epoch, computeDevice);
return(history);
}
