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()