wassname/keras-language-modeling
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i think this works now 💩

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This commit is contained in:
Ben Bolte
2016-08-02 05:23:58 -07:00
parent 66b5e1b58c
commit 0517eeff11
2 changed files with 10 additions and 11 deletions
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insurance_qa_eval.py
+6 -6
View File
@@ -26,7 +26,7 @@ class Evaluator:
self.path = data_path
self.conf = dict() if conf is None else conf
self.params = conf.get('training_params', dict())
self.answers = self.load('answers') # self.load('generated')
self.answers = self.load('answers') # self.load('generated')
self._vocab = None
self._reverse_vocab = None
self._eval_sets = None
@@ -171,7 +171,7 @@ class Evaluator:
max_r = np.argmax(r)
max_n = np.argmax(r[:n_good])
# print(' '.join(self.revegrt(d['question'])))
# print(' '.join(self.revert(d['question'])))
# print(' '.join(self.revert(self.answers[indices[max_r]])))
# print(' '.join(self.revert(self.answers[indices[max_n]])))
@@ -224,13 +224,13 @@ if __name__ == '__main__':
'training_params': {
'save_every': 1,
'batch_size': 20,
'nb_epoch': 100,
'nb_epoch': 10,
'validation_split': 0.2,
'optimizer': Adam(clipnorm=1e-2),
},
'model_params': {
'n_embed_dims': 100,
'n_embed_dims': 1000,
'n_hidden': 200,
# convolution
@@ -240,12 +240,12 @@ if __name__ == '__main__':
# recurrent
'n_lstm_dims': 141, # * 2
'initial_embed_weights': np.load('models/word2vec_100_dim.h5'),
'initial_embed_weights': np.load('models/word2vec_1000_dim.h5'),
'similarity_dropout': 0.2,
},
'similarity_params': {
'mode': 'cosine',
'mode': 'gesd',
'gamma': 1,
'c': 1,
'd': 2,
keras_models.py
+4 -5
View File
@@ -103,12 +103,11 @@ class LanguageModel:
if self._qa_model is None:
question_output, answer_output = self._models
dropout = Dropout(self.similarity_params.get('similarity_dropout', 0.2))
similarity = self.get_similarity()
qa_model = merge([question_output, answer_output], mode=similarity, output_shape=lambda _: (None, 1))
dropout = Dropout(self.similarity_params.get('similarity_dropout', 0.2))(qa_model)
self._qa_model = Model(input=[self.question, self.get_answer()], output=[dropout])
qa_model = merge([dropout(question_output), dropout(answer_output)],
mode=similarity, output_shape=lambda _: (None, 1))
self._qa_model = Model(input=[self.question, self.get_answer()], output=[qa_model])
return self._qa_model