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