optim = torch.optim.SGD(parameters(), lr=args.learning_rate, momentum=0.0) # 0.02, 0.9
named_modules = {"rnn" : rnn, "output" : output, "char_embeddings" : char_embeddings, "optim" : optim}
# Load the model from the checkpoint, if there is one
if args.load_from is not None:
checkpoint = torch.load(CHECKPOINT_HOME+args.load_from+".pth.tar")
for name, module in named_modules.items():
module.load_state_dict(checkpoint[name])
from torch.autograd import Variable
# Read the training data, so that it is reshuffled. When evaluating, we need to switch to a held-out set, but for development of this code, I'm using the training set (i.e., training set of the language model).
data = AcqdivReaderPartition(acqdivCorpusReadertrain).reshuffledIterator(blankBeforeEOS=False)
#print(list(data))
#print("stoi[char]+3")
#print(stoi[char]+3)
print("stoi")
print(stoi)
# Read the data into a list of integers
numeric_with_blanks = []
count = 0
print("Prepare chunks")
for chunk in data:
print("chunk")
print(chunk) # each "chunk" is a string representing an utterance
# print(stoi[" "])
if (labels[-1] == 1) and relevantNextWords[-1].startswith(
relevantWords[-1]
): # 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 = AcqdivReaderPartition(
acqdivCorpusReadertrain).reshuffledIterator()
print("Got data")
training_chars = prepareDatasetChunksTest(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
printHere = (counter % 50 == 0)
#relevantNextWords.append(nextRelevantWord)
positionIDs.append(int(uniquePositionID[j][i]))
charactersAll.append(codeToChar(int(numeric[j][i])))
assert boundariesAll[j][i + 1] is not None
####################################################################################3
import time
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
training_data = list(
AcqdivReaderPartition(acqdivCorpusReadertrain).reshuffledIterator(
blankBeforeEOS=True, seed=0))
#############################################
fullDataset_perRound_general = [
list(
prepareDatasetChunksTest(training_data,
train=True,
offset=ROUND *
(int(args.sequence_length / 3))))
for ROUND in range(-2, 2)
]
with open("segmentation-predictions/" + args.language + "-table.txt",
"w") as outFileTable:
print("\t".join(
