def train_chord_to_melody_model(self,
tt_split=0.9,
epochs=100,
model_name='basic_rnn'):
'''
Train model step - model takes in chord piano roll and outputs melody piano roll.
:param tt_split: train test split
:param epochs: number of epochs to train
:param model_name: specify which model we are training
:return: None. Model is assigned as self.model for this generator
'''
# Train test split
self.__prepare_data_tt_splited(tt_split=tt_split,
model_name=model_name,
src="nottingham-embed")
# print('Chords shape: {} Melodies shape: {}'.format(chords.shape, melodies.shape))
# Load / train model
if model_name == 'basic_rnn':
if os.path.exists("basic_rnn.h5"):
mb = ModelBuilder(self.X_train, self.Y_train, self.X_test,
self.Y_test)
model = mb.build_basic_rnn_model(
input_dim=self.X_train.shape[1:])
model.load_weights("basic_rnn.h5")
else:
mb = ModelBuilder(self.X_train, self.Y_train, self.X_test,
self.Y_test)
model = mb.build_attention_bidirectional_rnn_model(
input_dim=self.X_train.shape[1:])
model = mb.train_model(model,
epochs,
loss="categorical_crossentropy")
model.save_weights("basic_rnn.h5")
self.model = model
def load_model(self, model_name, tt_split=0.9, is_fast_load=True):
# clear session to avoid any errors
K.clear_session()
print("Chosen model: {}".format(model_name))
if not is_fast_load:
# Train test split
if model_name == 'bidem' or model_name == 'attention' or model_name == "bidem_preload":
self.__prepare_data_tt_splited(tt_split=tt_split,
model_name=model_name,
src='nottingham-embed')
print('Chords shape: {} Melodies shape: {}'.format(
self.X_train.shape, self.Y_train.shape))
else:
self.__prepare_data_tt_splited(tt_split=tt_split,
model_name=model_name,
src='nottingham')
print('Chords shape: {} Melodies shape: {}'.format(
self.X_train.shape, self.Y_train.shape))
if is_fast_load:
mb = ModelBuilder(None, None, None, None)
else:
mb = ModelBuilder(self.X_train, self.Y_train, self.X_test,
self.Y_test)
if model_name == 'basic_rnn_normalized':
self.model = mb.build_basic_rnn_model(input_dim=(1200, 128))
weights_path = '../note/active_models/basic_rnn_weights_500.h5'
print('Loading ' + weights_path + '...')
self.model.load_weights(weights_path)
elif model_name == 'basic_rnn_unnormalized':
self.model = mb.build_basic_rnn_model(input_dim=(1200, 128))
weights_path = '../note/active_models/basic_rnn_weights_500_unnormalized.h5'
print('Loading ' + weights_path + '...')
self.model.load_weights(weights_path)
elif model_name == 'bidem':
self.model = mb.build_bidirectional_rnn_model(input_dim=(1200, ))
weights_path = '../note/active_models/bidem_weights_500.h5'
print('Loading ' + weights_path + '...')
self.model.load_weights(weights_path)
elif model_name == 'bidem_regularized':
self.model = mb.build_bidirectional_rnn_model_no_embeddings(
input_dim=(1200, 1))
weights_path = '../note/active_models/bidirectional_regularized_500.h5'
print('Loading ' + weights_path + '...')
self.model.load_weights(weights_path)
elif model_name == 'attention':
self.model = mb.build_attention_bidirectional_rnn_model(
input_dim=(1200, ))
weights_path = '../note/active_models/attention_weights_1000.h5'
print('Loading ' + weights_path + '...')
self.model.load_weights(weights_path)
elif model_name == 'bidem_preload':
self.model = mb.build_bidirectional_rnn_model_no_embeddings(
input_dim=(None, 32))
weights_path = '../note/active_models/bidirectional_embedding_preload_100.h5'
print('Loading ' + weights_path + '...')
self.model.load_weights(weights_path)
else:
print('No model name: {}'.format(model_name))
return
self.model_name = model_name