wassname/keras-language-modeling
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bug fix

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codekansas
2016-05-08 23:46:44 -04:00
parent 0ee23e88cc
commit c176ac6426
2 changed files with 19 additions and 13 deletions
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keras_models.py
+1 -1
View File
@@ -104,7 +104,7 @@ class LanguageModel:
question_output, answer_output = self._models
similarity = self.get_similarity()
qa_model = merge([question_output, answer_output], mode=similarity, output_shape=lambda x: x[:-1])
qa_model = merge([question_output, answer_output], mode=similarity, output_shape=lambda x: x[0][:-1])
self._qa_model = Model(input=[self.question, self.get_answer()], output=[qa_model])
seq2seq/answer_to_question.py
+18 -12
View File
@@ -24,6 +24,8 @@ try:
except:
import pickle
model_save = 'models/answer_to_question.h5'
class InsuranceQA:
def __init__(self):
@@ -53,9 +55,10 @@ class InsuranceQA:
indices[i, self.words_indices[w]] = 1
return indices
def decode(self, indices, calc_argmax=True, noise=0.2):
def decode(self, indices, calc_argmax=True):
if calc_argmax:
indices = [self.sample(i, noise=noise) for i in indices]
indices = np.argmax(indices, axis=-1)
# indices = [self.sample(i) for i in indices]
return ' '.join(self.indices_words[x] for x in indices)
def sample(self, index, noise=0.2):
@@ -64,7 +67,7 @@ class InsuranceQA:
index = np.argmax(np.random.multinomial(1, index, 1))
return index
def get_model(question_maxlen, answer_maxlen, vocab_len, n_hidden):
def get_model(question_maxlen, answer_maxlen, vocab_len, n_hidden, load_save=False):
answer = Input(shape=(answer_maxlen, vocab_len))
masked = Masking(mask_value=0.)(answer)
@@ -97,6 +100,9 @@ def get_model(question_maxlen, answer_maxlen, vocab_len, n_hidden):
model = Model([answer], [softmax])
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
if os.path.exists(model_save) and load_save:
model.load_weights(model_save)
return model
if __name__ == '__main__':
@@ -126,7 +132,7 @@ if __name__ == '__main__':
for a in ans:
answer = qa.table.encode([qa.vocab[x] for x in answers[a]], answer_maxlen)
question = qa.table.encode([qa.vocab[x] for x in s['question']], question_maxlen)
question = np.amax(question, axis=0, keepdims=False)
# question = np.amax(question, axis=0, keepdims=False)
answer_idx[i] = answer
question_idx[i] = question
i += 1
@@ -138,7 +144,7 @@ if __name__ == '__main__':
test_gen = gen_questions(n_test, test=True)
print('Generating model...')
model = get_model(question_maxlen=question_maxlen, answer_maxlen=answer_maxlen, vocab_len=len(qa.vocab), n_hidden=128)
model = get_model(question_maxlen=question_maxlen, answer_maxlen=answer_maxlen, vocab_len=len(qa.vocab), n_hidden=128, load_save=True)
print('Training model...')
for iteration in range(1, 200):
@@ -146,13 +152,13 @@ if __name__ == '__main__':
print('-' * 50)
print('Iteration', iteration)
model.fit_generator(gen, samples_per_epoch=100*batch_size, nb_epoch=10)
model.save_weights(model_save, overwrite=True)
x, y = next(test_gen)
y = y[0]
pred = model.predict(x, verbose=0)
for noise in [0.2, 0.5, 1.0, 1.2]: # not sure what noise values would be good
print(' Noise: {}'.format(noise))
for i in range(n_test):
print(' Answer: {}'.format(qa.table.decode(x[0][i])))
print(' Expected: {}'.format(qa.table.decode(y[i])))
print(' Predicted: {}'.format(qa.table.decode(pred[i], noise=noise)))
y = y[0]
x = x[0]
for i in range(n_test):
print('Answer: {}'.format(qa.table.decode(x[i])))
print(' Expected: {}'.format(qa.table.decode(y[i])))
print(' Predicted: {}'.format(qa.table.decode(pred[i])))