def DebugTrainNet(self, TaskSpec, OptionList):
LogHistory = TaskSpec["LogHistory"]
OperatorList = TaskSpec["OperatorList"]
NetworkDecor = TaskSpec["NetworkDecor"]
VertexNum = TaskSpec["OperatorNum"]
InputNum = TaskSpec["InputNum"]
OutputNum = TaskSpec["OutputNum"]
BatchSize = TaskSpec["BatchSize"]
Epochs = TaskSpec["Epochs"]
ConcatOperator = TaskSpec["ConcatOperator"]
InputOperator = TaskSpec["InputOperator"]
TrajectoryLength = TaskSpec["TrajectoryLength"]
RewardGamma = TaskSpec["RewardGamma"]
TaskInput = TaskSpec["TaskInput"]
TaskLabel = TaskSpec["TaskLabel"]
SetBatchSize(BatchSize)
self.TaskGraph = tf.Graph()
self.TaskSess = tf.Session(graph=self.TaskGraph,
config=self.SessConfig)
Gph = Graph(VertexNum=VertexNum,
OperatorList=OperatorList,
InputNum=InputNum,
OutputNum=OutputNum,
ConcatOperator=ConcatOperator,
InputOperator=InputOperator)
with self.TaskGraph.as_default():
self.InitializeTaskGraph(Data=TaskInput, Label=TaskLabel)
for Option in OptionList:
Gph.ApplyOption(Option)
self.BuildTaskGraph(Graph=Gph, OutputDecor=NetworkDecor, ID=0)
for i in Gph.InternalTensor:
if i is None:
continue
print(i, type(i).Name, i.GetTensor().shape)
#return
Loss, Acc = self.TrainTaskNet(Step=Epochs)
print(Loss, Acc)
def StartTrial(self, TaskSpec):
LogHistory = TaskSpec["LogHistory"]
OperatorList = TaskSpec["OperatorList"]
NetworkDecor = TaskSpec["NetworkDecor"]
VertexNum = TaskSpec["OperatorNum"]
InputNum = TaskSpec["InputNum"]
OutputNum = TaskSpec["OutputNum"]
BatchSize = TaskSpec["BatchSize"]
Epochs = TaskSpec["Epochs"]
ConcatOperator = TaskSpec["ConcatOperator"]
InputOperator = TaskSpec["InputOperator"]
TrajectoryLength = TaskSpec["TrajectoryLength"]
RewardGamma = TaskSpec["RewardGamma"]
TaskInput = TaskSpec["TaskInput"]
TaskLabel = TaskSpec["TaskLabel"]
self.HisNet = []
self.HisNetPerf = []
self.Log("Operator List " + str(OperatorList))
#ImageOperators
SetBatchSize(BatchSize)
self.BatchSize = BatchSize
with self.QGraph.as_default():
self.ConstructQFunc3D(ImageSize=VertexNum,
BitDepth=len(OperatorList),
BatchSize=BatchSize)
#print([i.op_def.name for i in self.QGraph.get_operations()])
QNetVar = tf.global_variables(
) #[op for op in tf.GraphKeys.GLOBAL_VARIABLES if isinstance(op,tf.Variable) and not tf.is_variable_initialized(op)]
#print([i.name for i in QNetVar])
self.QNetsess.run(tf.initialize_variables(
QNetVar)) #tf.global_variables_initializer())
Initial_Explore_Rate = 0.5
Explore_Rate_Decay = 0.001
QNet_Format = "3D_NoNull"
for i in range(Epochs):
if Epochs % 100 == 0:
self.TaskGraph = tf.Graph()
self.TaskSess = tf.Session(graph=self.TaskGraph,
config=self.SessConfig)
with self.TaskGraph.as_default():
self.InitializeTaskGraph(Data=TaskInput, Label=TaskLabel)
Gph = Graph(VertexNum=VertexNum,
OperatorList=OperatorList,
InputNum=InputNum,
OutputNum=OutputNum,
ConcatOperator=ConcatOperator,
InputOperator=InputOperator)
Gph.InitializeCheckOptionInput([self.TaskNextData])
if Initial_Explore_Rate > 0:
Initial_Explore_Rate -= Explore_Rate_Decay
TrajectoryStep = 0
HisNetTemp = []
HisPerfTemp = []
self.InitializedVar = []
for j in range(TrajectoryLength):
OptionList = Gph.ConnectOptions()
ValidOptionList = []
for Option in OptionList:
if Gph.CheckOption(Option):
ValidOptionList.append(Option)
RandDie = random.random()
if RandDie > Initial_Explore_Rate:
ChooseType = "QLearning"
else:
ChooseType = "Random"
if ChooseType == 'QLearning':
QNetInputList = []
for Option in ValidOptionList:
Gph.ApplyOption(Option)
QNetInput = Gph.UnifiedTransform(QNet_Format)
QNetInputList.append(QNetInput)
Gph.RevokeOption(Option)
QNetInput = np.array(QNetInputList)
QValuesClip = self.PredictQNet(QNetInput)
PossQValues = np.exp(QValuesClip)
_Sum = np.sum(PossQValues)
ExpDist = PossQValues / _Sum
ChosenOption = self.MakeChoice(Distribution=ExpDist,
ChoiceList=ValidOptionList)
elif ChooseType == 'Random':
ChosenOption = ValidOptionList[random.randint(
0,
len(ValidOptionList) - 1)]
self.Log("ChosenOption " + str(ChosenOption))
Gph.ApplyOption(ChosenOption)
Step = 100
#print(i,j)
with self.TaskGraph.as_default():
self.BuildTaskGraph(Graph=Gph,
OutputDecor=NetworkDecor,
ID=i)
Loss, Acc = self.TrainTaskNet(Step=Epochs)
HisItem = {
"Traj": j,
"OptionList": Gph.ConnectOptions(),
"TrainStep": Step,
"Loss": Loss,
"Acc": Acc,
"UnifiedNet": Gph.UnifiedTransform(QNet_Format)
}
if LogHistory == True:
pass
#self.Log(HisItem)
HisNetTemp.append(HisItem["UnifiedNet"])
HisPerfTemp.append(HisItem["Acc"])
TrajectoryStep += 1
self.HisNet.extend(HisNetTemp)
for i in range(TrajectoryStep - 1):
print("HisNet", HisPerfTemp[i])
HisPerfTemp[TrajectoryStep - i -
2][0] += RewardGamma * HisPerfTemp[TrajectoryStep -
i - 1][0]
self.HisNetPerf.extend(HisPerfTemp)
QStep = Epochs
self.TrainQNet(self.QNetOutput, self.HisNet, self.HisNetPerf,
QStep)
"ConcatOperator": ConcatOperatorDense,
"InputOperator": ImageInput,
"TrajectoryLength": OperatorLimit - 4,
"RewardGamma": 0.9
}
if Mode == "Train":
logging.getLogger().setLevel(logging.DEBUG)
now = datetime.datetime.now()
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='%s_%s.log' % ("MNISTTask", now.strftime("%Y-%m-%d %H-%M")),
filemode='w')
RL_Exp.StartTrial(TaskSpec)
elif Mode == "TestBuildGraph":
OptionList = [[1, 1, 1, 0], [11, 1, 1, 1], [13, 1, 1, 2], [2, 1, 1, 3],
[12, 1, 1, 4], [13, 1, 1, 5], [6, 1, 1, 6]]
RL_Exp.DebugTrainNet(TaskSpec, OptionList)
elif Mode == "TestUnifiedTrans":
g = Graph(10, Op_List, 1, 1, ConcatOperatorDense, InputOperator=ImageInput)
OptionList = [[1, 1, 1, 0], [11, 1, 1, 1], [13, 1, 1, 2], [2, 1, 1, 3],
[12, 1, 1, 4], [13, 1, 1, 5], [6, 1, 1, 6]]
np.set_printoptions(threshold=np.inf)
for Option in OptionList:
g.ApplyOption(Option)
print(np.array(g.UnifiedTransform("3D_NoNull").astype(np.int8)))
