def processSingle(path):
if config.GPU == True:
ptrNet = PointerNetwork(config.HIDDEN_SIZE).cuda()
else:
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
ptrNet.load_state_dict(torch.load(path))
nlp = spacy.load("en_core_web_sm")
sentences = [
"this is definitely a difficult sentence", "your plan seems excellent",
"it is really working", "let me start with a poem",
"he is sure to pass the exam", "we meet every wednesday",
"do plan to we it tomorrow", "i like working on brain",
"development good is a this", "i am going to canada",
"The script that I am describing here mainly for training and validation "
]
if True:
for origLine in sentences:
print("")
inputLine, outputLine = processSentence(nlp, ptrNet, origLine)
print("Input Sentence: ", inputLine)
print("Outpt Sentence: ", outputLine)
print("Orig Sentence: ", origLine)
else:
lines = open("../data/englishSentences_test.dat").read().splitlines()
for i in range(7):
print("\n")
origLine = random.choice(lines)
inputLine, outputLine = processSentence(nlp, ptrNet, origLine)
print("Input Sentence: ", inputLine)
print("Outpt Sentence: ", outputLine)
print("Orig Sentence: ", origLine)
def modelEvaluate(path):
if config.GPU == True:
ptrNet = PointerNetwork(config.HIDDEN_SIZE).cuda()
else:
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
ptrNet.load_state_dict(torch.load(path))
evaluateWordSort(ptrNet, 1)
def modelEvaluate(accuracyStats, path):
if config.GPU == True:
ptrNet = PointerNetwork(config.HIDDEN_SIZE).cuda()
else:
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
ptrNet.load_state_dict(torch.load(path))
for epoch in range(EPOCHS):
evaluateWordSort(accuracyStats, ptrNet, 1)
printAccStats(accuracyStats)
def modelEvaluate(accuracyStats, path):
if config.GPU == True:
ptrNet = PointerNetwork(config.HIDDEN_SIZE).cuda()
else:
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
ptrNet.load_state_dict(torch.load(path))
for sentenceLength in range(4, 11):
print(sentenceLength)
evaluateWordSort(accuracyStats, ptrNet, sentenceLength)
printAccStats(accuracyStats)