import pickle
import matplotlib.pyplot as plt
from VanillaRNN import RNN
from TrainingSummary import TrainingSummary
from WordDistance import WordDistance
RNN = RNN('../input/case_sample.xml')
RNN.train(100000)
summary = pickle.load(open("training_summary.pkl", "rb"))
distance_calc = WordDistance()
score = distance_calc.sentenceScore(summary[0].sample)
#now we want to compute a bunch more statistics for each item in the list
print("Processing %s samples"%len(summary))
new_s = []
for s in summary:
s = s.addStats()
print(s.score)
new_s.append(s)
#sum_stats = [summ.addStats() for summ in summary]
pickle.dump(new_s, open("training_summary_add.pkl", "wb"))
scores = [s.score for s in new_s]
loss = [s.loss for s in new_s]
import pickle
import matplotlib.pyplot as plt
from VanillaRNN import RNN
from TrainingSummary import TrainingSummary
from WordDistance import WordDistance
RNN = RNN('../input/case_sample.xml')
RNN.train(100000)
summary = pickle.load(open("training_summary.pkl", "rb"))
distance_calc = WordDistance()
score = distance_calc.sentenceScore(summary[0].sample)
#now we want to compute a bunch more statistics for each item in the list
print("Processing %s samples" % len(summary))
new_s = []
for s in summary:
s = s.addStats()
print(s.score)
new_s.append(s)
#sum_stats = [summ.addStats() for summ in summary]
pickle.dump(new_s, open("training_summary_add.pkl", "wb"))
scores = [s.score for s in new_s]
loss = [s.loss for s in new_s]
time = [s.t for s in new_s]
def addStats(self):
word_fixer = WordDistance()
self.score = word_fixer.sentenceScore(self.sample)
self.sample_fixed = word_fixer.closestSentence(self.sample)
self.score_fixed = word_fixer.sentenceScore(self.sample_fixed)
return self
def addStats(self): word_fixer = WordDistance() self.score = word_fixer.sentenceScore(self.sample) self.sample_fixed = word_fixer.closestSentence(self.sample) self.score_fixed = word_fixer.sentenceScore(self.sample_fixed) return self
