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)
backward(loss, printHere)
trainChars += charCounts
if printHere:
print("Dev losses")
print(devLosses)
print("Chars per sec " + str(trainChars /
(time.time() - startTime)))
rnn_drop.train(False)
dev_data = corpusIteratorWiki.dev("italian")
print("Got data")
dev_chars = prepareDataset(dev_data, train=True)
dev_loss = 0
dev_char_count = 0
counter = 0
while True:
counter += 1
try:
numeric = [next(dev_chars) for _ in range(args.batchSize)]
except StopIteration:
break
printHere = (counter % 50 == 0)
loss, numberOfCharacters = forward(numeric,
if printHere:
print((epoch,counter))
print("Dev losses")
print(devLosses)
print("Chars per sec "+str(trainChars/(time.time()-startTime)))
print(learning_rate)
print(args)
if counter % 20000 == 0 and epoch == 0:
if args.save_to is not None:
torch.save(dict([(name, module.state_dict()) for name, module in named_modules.items()]), MODELS_HOME+"/"+args.save_to+".pth.tar")
rnn_drop.train(False)
dev_data = corpusIteratorWiki.dev(args.language)
print("Got data")
dev_chars = prepareDatasetChunks(dev_data, train=False)
dev_loss = 0
dev_char_count = 0
counter = 0
hidden, beginning = None, None
while True:
counter += 1
try:
numeric = next(dev_chars)
except StopIteration:
break
): # this is actually not a hard assertion, it should just be quite unlikely in languages such as English
print("WARNING",
list(zip(boundaries[j][i:], boundariesAll[j][i:])))
# if len(relevantWords[-1]) > 1:
# assert False
import time
devLosses = []
#for epoch in range(10000):
if True:
training_data = corpusIteratorWikiWords.dev(args.language,
removeMarkup=False)
training_data_c = corpusIteratorWiki.dev(args.language, doShuffling=False)
print("Got data")
training_chars = prepareDatasetChunks(training_data,
training_data_c,
train=False)
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: