def main():
if len(sys.argv) != 5:
logging.info(
'please input args: car_path, road_path, cross_path, answerPath')
exit(1)
car_path = sys.argv[1]
road_path = sys.argv[2]
cross_path = sys.argv[3]
answer_path = sys.argv[4]
logging.info("car_path is %s" % (car_path))
logging.info("road_path is %s" % (road_path))
logging.info("cross_path is %s" % (cross_path))
logging.info("answer_path is %s" % (answer_path))
# to read input file
# process
# to write output file
run(car_path, road_path, cross_path, answer_path)
from pdb import set_trace as T
from Run import run
from models.BaselineLSTM import LSTMCell
from models.RHN import RHNCell
from models.HyperRHN import HyperRHNCell
saveName = 'saves/testme/'
load = False
test = True
cellFunc = HyperRHNCell
depth = 7
h = 1000
hCell = h
hCell = (128, h)
vocab = 11007
embedDim = 27
batchSz = 200
context = 100
minContext = 50
eta = 1e-3
gateDrop = 1.0 - 0.65
embedDrop = 0.0
#15461 words in train
#2401 words in test
#3061 words in valid
cell = cellFunc(embedDim, hCell, depth, gateDrop)
run(cell, depth, h, vocab, batchSz, embedDim, embedDrop,
context, minContext, eta, saveName, load, test,mode='english_words')
def Runner(self):
copyall(tup[0], tup[1])
write2config(tup[0], fc, sep, filc, fname)
run(tup[0])
from pdb import set_trace as T
from Run import run
from models.BaselineLSTM import LSTMCell
from models.RHN import RHNCell
from models.HyperRHN import HyperRHNCell
saveName = 'saves/testme/'
load = True
test = True
cellFunc = HyperRHNCell
depth = 7
h = 1000
hCell = h
hCell = (128, h)
vocab = 50
embedDim = 27
batchSz = 200
context = 100
minContext = 50
eta = 1e-3
gateDrop = 1.0 - 0.65
embedDrop = 0.0
cell = cellFunc(embedDim, hCell, depth, gateDrop)
run(cell, depth, h, vocab, batchSz, embedDim, embedDrop,
context, minContext, eta, saveName, load, test)