# boundary = True
# boundaries_index[0] += 1
# else:
# boundary = False
print((losses[i][0], itos[numeric[0][i+1]-3]))
print(len(labels))
# return loss, len(numeric) * args.sequence_length
import time
devLosses = []
#for epoch in range(10000):
if True:
training_data = corpusIteratorWiki.training(args.language)
print("Got data")
training_chars = prepareDataset(training_data, train=True) if args.language == "italian" else prepareDatasetChunks(training_data, train=True)
rnn_drop.train(False)
startTime = time.time()
trainChars = 0
counter = 0
while True:
counter += 1
try:
numeric = [next(training_chars) for _ in range(args.batchSize)]
except StopIteration:
break
def backward(loss, printHere):
optim.zero_grad()
if printHere:
print(loss)
loss.backward()
optim.step()
import time
devLosses = []
for epoch in range(10000):
print(epoch)
training_data = corpusIteratorWiki.training("italian")
print("Got data")
training_chars = prepareDataset(training_data, train=True)
rnn_drop.train(True)
startTime = time.time()
trainChars = 0
counter = 0
while True:
counter += 1
try:
numeric = [next(training_chars) for _ in range(args.batchSize)]
except StopIteration:
break
printHere = (counter % 50 == 0)
loss, charCounts = forward(numeric, printHere=printHere, train=True)