def get_theano_model(self, x_train, y_train):
from neuralnet.nn.rnn_theano import RNNTheano
trained_model_data = './data/trained-model-theano.npz'
model = RNNTheano(self.vocabulary_size, hidden_dim=50)
if os.path.isfile(trained_model_data):
logger.info(
"found trained model, loading instead of training new: %s" %
trained_model_data)
Utils.load_model_parameters_theano(trained_model_data, model)
else:
logger.info("training new model...")
numpy.random.seed(10)
self.train_with_sgd(model,
x_train,
y_train,
nepoch=10,
evaluate_loss_after=1)
logger.info("saving trained model to: %s" % trained_model_data)
Utils.save_model_parameters_theano(trained_model_data, model)
return model
def execute(self, stage):
ok = True
(word_to_index, index_to_word, tokenized_sentences) = Utils.load_csv_data(
_TRAINING_FILE, self.sentence_start_token, self.sentence_end_token,
self.unknown_token, self.vocabulary_size)
self.rnn_theano(word_to_index, index_to_word, tokenized_sentences)
return ok
def get_theano_model(self, x_train, y_train):
from neuralnet.nn.rnn_theano import RNNTheano
trained_model_data = './data/trained-model-theano.npz'
model = RNNTheano(self.vocabulary_size, hidden_dim=50)
if os.path.isfile(trained_model_data):
logger.info("found trained model, loading instead of training new: %s" % trained_model_data)
Utils.load_model_parameters_theano(trained_model_data, model)
else:
logger.info("training new model...")
numpy.random.seed(10)
self.train_with_sgd(model, x_train, y_train, nepoch=10, evaluate_loss_after=1)
logger.info("saving trained model to: %s" % trained_model_data)
Utils.save_model_parameters_theano(trained_model_data, model)
return model
def execute(self, stage):
ok = True
(word_to_index, index_to_word,
tokenized_sentences) = Utils.load_csv_data(_TRAINING_FILE,
self.sentence_start_token,
self.sentence_end_token,
self.unknown_token,
self.vocabulary_size)
self.rnn_theano(word_to_index, index_to_word, tokenized_sentences)
return ok
