class Generator:
def __init__(self, fileName):
t = Tokenizer()
self.wordToInd = t.getWordToInd()
self.indexToWord = t.getIndToWord()
self.model = RNN(t.getVocabSize())
load(fileName, self.model)
def postParse(self, sentence):
sentence = [v for v in sentence if not v == "[" or v == "]"]
out = str(sentence[0][0].upper() + sentence[0][1:])
for i in sentence[1:]:
if i == "," or i == "." or i == ":" or i == ";" or i == "?" or i == "!":
out += i
elif i == "i":
out += " I"
else:
out += " " + i
return out
def generateSentence(self):
newSent = [self.wordToInd["SENTENCE_START"]]
while not newSent[-1] == self.wordToInd["SENTENCE_END"]:
nextWordProbs = self.model.forwardPropagation(newSent)[0]
sampled = self.wordToInd["UNKNOWN_TOKEN"]
while sampled == self.wordToInd["UNKNOWN_TOKEN"]:
samples = np.random.multinomial(1, nextWordProbs[-1])
sampled = np.argmax(samples)
newSent.append(sampled)
sentence = [self.indexToWord[x] for x in newSent[1:-1]]
#print (sentence)
return (self.postParse(sentence))
def curateSentence(self):
sentence = self.generateSentence()
while (not (len(sentence) > 80 and len(sentence) < 270)
or ("[") in sentence or ("]") in sentence):
sentence = self.generateSentence()
if len(sentence) > 140:
ind = 0
for i in range(130, len(sentence)):
if sentence[i] == " ":
ind = i
break
split = [sentence[:ind + 1], sentence[ind + 1:]]
if (len(split[0]) > 140 or len(split[1]) > 140):
return self.curateSentence()
else:
return 2, [split[1], split[0]]
else:
return 1, [sentence]
def testSystem():
t = Tokenizer()
xTrain, yTrain = t.getData()
np.random.seed(10)
model = RNN(15000)
o, s = model.forwardPropagation(xTrain[30])
predictions = model.predict(xTrain[30])
print(o.shape)
print(o)
print(predictions.shape)
print(predictions)
print("Expected Loss: \n" + str(np.log(model.vocab)))
print("Actual Loss:")
print(model.calculateLoss(xTrain[:100], yTrain[:100]))
