def Train(self, logName):
trainData, trainLabel, trainSeq = Shuffle_Triple(
self.data, self.label, self.dataSeq)
startPosition, totalLoss = 0, 0.0
with open(logName, 'w') as file:
while startPosition + self.batchSize < numpy.shape(trainData)[0]:
batchData = self.data[startPosition:startPosition +
self.batchSize]
batchDataSeq = self.dataSeq[startPosition:startPosition +
self.batchSize]
batchLabel, batchLabelSeq = self.__LabelPretreatment(
treatLabel=self.label[startPosition:startPosition +
self.batchSize])
loss, _ = self.session.run(
fetches=[self.parameters['Loss'], self.train],
feed_dict={
self.dataInput: batchData,
self.dataSeqInput: batchDataSeq,
self.labelInput: batchLabel,
self.labelSeqInput: batchLabelSeq
})
print('\rTrain %d/%d Loss = %f' %
(startPosition, numpy.shape(trainData)[0], loss),
end='')
startPosition += self.batchSize
totalLoss += loss
file.write(str(loss) + '\n')
return totalLoss
def Train(self, logName):
trainData, trainLabel, trainSeq = Shuffle_Triple(
self.data, self.label, self.dataSeq)
totalLoss = 0.0
with open(logName, 'w') as file:
for index in range(numpy.shape(trainData)[0]):
loss, _ = self.session.run(
fetches=[self.parameters['Loss'], self.train],
feed_dict={
self.dataInput: trainData[index],
self.seqInput: trainSeq[index],
self.labelInput: numpy.reshape(trainLabel[index],
[1, 1])
})
print('\rTrain %d/%d Loss = %f' %
(index, numpy.shape(trainData)[0], loss),
end='')
totalLoss += loss
file.write(str(loss) + '\n')
return totalLoss
def Train(self):
trainData, trainLabel, trainSeq = Shuffle_Triple(
self.data, self.label, self.seq)
startPosition, totalLoss = 0, 0.0
while startPosition < numpy.shape(self.data)[0]:
batchData = trainData[startPosition:startPosition + self.batchSize]
batchLabel = self.__LabelPretreatment(
trainLabel[startPosition:startPosition + self.batchSize])
batchSeq = trainSeq[startPosition:startPosition + self.batchSize]
loss, _ = self.session.run(
fetches=[self.parameters['Cost'], self.train],
feed_dict={
self.dataInput: batchData,
self.labelInput: batchLabel,
self.seqInput: batchSeq
})
print('\rTraining %d/%d Loss = %f' %
(startPosition, numpy.shape(trainData)[0], loss),
end='')
totalLoss += loss
# startPosition += self.batchSize
return totalLoss