def makeFilenames(data_name, cell_size, epoch, batch_size, timesteps, offset):
label_name = gue.tasksize_extractor(data_name)
rep_number = gue.rep_extractor(data_name)
result_path = "./result/" + str(label_name) + "_" + str(rep_number) + "/"
id = "c" + str(cell_size) + "_e" + str(epoch) + "_b" + str(
batch_size) + "_ti" + str(timesteps) + "_o" + str(offset) + "l6"
modelname = result_path + id + ".model"
statname = result_path + id + ".json"
return result_path, modelname, statname
if __name__ == "__main__":
trainSplit = 0.8
cell_size = 32
epoch = 20
batchSize = 100
timesteps = 100
offset = 10000
targetTask = 0
fileNames = glob.glob('./data/*.data')
for fileName in fileNames:
label_card = gue.tasksize_extractor(fileName) + 1
modelExists = False
result_path, modelname, statname = makeFilenames(
fileName, cell_size, epoch, batchSize, timesteps, offset)
originalTrace = cvt.readTraceFile(fileName)
originalTrace = originalTrace[100:100000]
# originalTrace = cvt.cut_random(originalTrace, 0.1, 0.01, fileName)
# trim the first couple of sequence of original trace
_, inverse = get_priors(originalTrace)
if gue.file_exists(modelname):
modelExists = True
train_X = []