/// <summary>
/// Compiles the model into an executable graph
/// </summary>
/// <param name="optimizer">The optimization algorithm to use for training the model</param>
/// <param name="losses">The losses for each of the outputs of the model</param>
/// <remarks>The list of loss functions should be in order of the outputs of the model</remarks>
public void Compile(Optimizer optimizer, IEnumerable <LossFunction> losses)
{
if (optimizer == null)
{
throw new ArgumentNullException(
"The optimizer must be specified",
nameof(optimizer));
}
if (losses.Count() != _outputs.Count())
{
throw new ArgumentException(
"The number of loss functions does not match the number of outputs of the model",
nameof(losses));
}
_graph = new TFGraph();
var compilationContext = new ModelCompilationContext(_graph);
_optimizer = optimizer;
_inputMapping = new Dictionary <Input, TFOutput>();
_outputMapping = new Dictionary <Layer, TFOutput>();
_placeholderMapping = new Dictionary <Layer, TFOutput>();
var compiledLosses = new List <TFOutput>();
var layersWithLosses = Enumerable.Zip(_outputs, losses, (layer, loss) => (layer, loss));
// By compiling the outputs, the layers that are connected
// to the outputs are also compiled. This goes all the way back to the inputs.
foreach (var(layer, loss) in layersWithLosses)
{
var placeholder = _graph.Placeholder(TFDataType.Double, new TFShape(layer.OutputShape));
var output = layer.Compile(compilationContext);
_outputMapping.Add(layer, output);
_placeholderMapping.Add(layer, placeholder);
var compiledLoss = loss.Compile(compilationContext, output, placeholder);
compiledLosses.Add(compiledLoss);
}
foreach (var input in _inputs)
{
_inputMapping.Add(input, input.Configuration.Output);
}
_modelLoss = compiledLosses.Aggregate((left, right) => _graph.Add(left, right));
_optimizer.Compile(compilationContext, _modelLoss, compilationContext.Parameters);
_initializers = compilationContext.Initializers;
_parameters = compilationContext.Parameters;
}
