def evaluateModelAgainstTrainingData(model):
inStates, valueLabels, policyLabels = MemoryBuffers.getAllTrainingData()
_, valueLoss, policyLoss = model.evaluate(
np.array(inStates),
[np.array(valueLabels), np.array(policyLabels)],
verbose=2,
shuffle=True)
print("ValueLoss: {} PolicyLoss: {}".format(valueLoss, policyLoss))
def sendToOverlord(overlordConnection, localPipe, amountOfWorkers, endPipe):
# Needed in the end when we wish to count the bitmaps
import time
time.sleep(3)
print("Starting init")
import StartInit
StartInit.init()
runningCycle = True
amountOfCollectedGames = 0
amountOfCollectedWorkers = 0
collectedVisitedStates = []
while (runningCycle):
tupleMsg = localPipe.get()
msgType = tupleMsg[0]
if (msgType == C.LocalWorkerProtocol.DUMP_TO_REPLAY_BUFFER):
_, amountOfGames, states, evals, polices, weights = tupleMsg
MemoryBuffers.addLabelsToReplayBuffer(states, evals, polices)
amountOfCollectedGames += amountOfGames
if (amountOfCollectedGames >=
MachineSpecificSettings.GAMES_BATCH_SIZE_TO_OVERLORD):
print("Sending to oracle from dataworker")
dStates, dEvals, dPolices, dWeights = MemoryBuffers.getAllTrainingData(
)
dumpMsg = (amountOfCollectedGames, dStates, dEvals, dPolices,
dWeights)
overlordConnection.sendMessage(
C.RemoteProtocol.DUMP_REPLAY_DATA_TO_OVERLORD, dumpMsg)
amountOfCollectedGames = 0
MemoryBuffers.clearReplayBuffer()
elif (msgType == C.LocalWorkerProtocol.DUMP_MOST_VISITED_STATES):
amountOfCollectedWorkers += 1
_, states = tupleMsg
if (amountOfCollectedWorkers >= amountOfWorkers):
print("collected states from all local workers: ",
len(collectedVisitedStates))
sendMostVisitedStatesToOverlord(overlordConnection,
collectedVisitedStates)
print("Sent message to all workers")
runningCycle = False
endPipe.put("Ending by datamanager")
print("Ending sending thread")