/// <summary>
/// Build neural network based on the lsit of layer defs.
/// </summary>
/// <param name="layerDefs">layer definitions</param>
/// <param name="input">input tensor</param>
/// <param name="scope">name scope</param>
/// <returns>value tuple of (output tensor, list of weights)</returns>
public static ValueTuple <Tensor, List <Tensor> > BuildSequentialLayers(List <SimpleDenseLayerDef> layerDefs, Tensor input, string scope = null)
{
NameScope nameScppe = null;
if (!string.IsNullOrEmpty(scope))
{
nameScppe = Current.K.name_scope(scope);
}
List <Tensor> weights = new List <Tensor>();
Tensor temp = input;
foreach (var l in layerDefs)
{
var result = l.Call(temp);
temp = result.Item1;
weights.AddRange(result.Item2);
}
if (nameScppe != null)
{
nameScppe.Dispose();
}
return(ValueTuple.Create(temp, weights));
}
/// <summary>
/// Trainers will call this method to initialize the model. This method will call the InitializeInner()
/// </summary>
/// <param name="brainParameters">brain parameter of the MLagent brain</param>
/// <param name="enableTraining">whether enable training</param>
/// <param name="trainerParams">trainer parameters passed by the trainer. Training will not be enabled </param>
public virtual void Initialize(BrainParameters brainParameters, bool enableTraining, TrainerParams trainerParams = null)
{
Debug.Assert(Initialized == false, "Model already Initalized");
NameScope ns = null;
if (!string.IsNullOrEmpty(modelName))
{
ns = Current.K.name_scope(modelName);
}
ActionSizes = brainParameters.vectorActionSize;
StateSize = brainParameters.vectorObservationSize * brainParameters.numStackedVectorObservations;
ActionSpace = brainParameters.vectorActionSpaceType;
Debug.Assert(ActionSizes[0] > 0, "Action size can not be zero");
//create basic inputs
var inputStateTensor = StateSize > 0 ? UnityTFUtils.Input(new int?[] { StateSize }, name: "InputStates")[0] : null;
HasVectorObservation = inputStateTensor != null;
var inputVisualTensors = CreateVisualInputs(brainParameters);
HasVisualObservation = inputVisualTensors != null;
//create inner intialization
InitializeInner(brainParameters, inputStateTensor, inputVisualTensors, enableTraining ? trainerParams : null);
//test
//Debug.LogWarning("Tensorflow Graph is saved for test purpose at: SavedGraph/" + name + ".pb");
//((UnityTFBackend)Current.K).ExportGraphDef("SavedGraph/" + name + ".pb");
Current.K.try_initialize_variables(true);
if (ns != null)
{
ns.Dispose();
}
if (checkpointToLoad != null)
{
RestoreCheckpoint(checkpointToLoad.bytes, true);
}
Initialized = true;
TrainingEnabled = enableTraining;
}