def run_chord2vec(): """ To run the model. To finishing the training early, set configs['max_iter'] to less. """ configs = get_configs() configs['retrieve_model'] = False configs['max_iter'] = 1 data = get_data(configs) nn, bigram = get_models(data, configs) print nn.plot_w1()
def retrieve_skipgram_and_ngram(): """ By default, loads cached model. To train new model, set configs['retrieve_model'] as in the run_chord2vec function :return: neural-net skipgram, bigram model """ configs = get_configs() data = get_data(configs) nn, bigram = get_models(data, configs) # saves a plot of the 2D PCA of the chord vectors nn.plot_w1()