def main(model_name, exp_name='fma'):
"""
DO the work!
"""
assert model_name in ['crnn', 'lstm', 'lstm_bi']
print("-" * 60)
print("Keunwoo: Welcome! Lets do something deep with Jamendo dataset.")
print(" I'm assuming you finished pre-processing.")
print(" We're gonna use {} model".format(model_name))
print("It's a good practice to use callbacks in Keras.")
callbacks = my_callbacks.get_callbacks(name=exp_name)
early_stopper, model_saver, weight_saver, csv_logger = callbacks # just to show you
print("Preparing data generators for training and validation...")
train_batch_size = 40
valid_batch_size = 5
steps_per_epoch = 30
gen_train = data_gen('train', SR * 10, is_shuffle=True, is_infinite=True, batch_size=train_batch_size)
gen_valid = data_gen('valid', SR * 10, is_shuffle=False, is_infinite=True, batch_size=valid_batch_size)
print("Keunwoo: Getting model...")
if model_name == 'crnn':
model = models_time_varying.model_convrnn(n_out=2)
elif model_name == 'lstm':
model = models_time_varying.model_lstm_leglaive_icassp2014(n_out=2, bidirectional=False)
elif model_name == 'lstm_bi'
model = models_time_varying.model_lstm_leglaive_icassp2014(n_out=2, bidirectional=True)
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
print("Keunwoo: Starting to train...")
model.fit_generator(gen_train, steps_per_epoch, epochs=30,
callbacks=callbacks,
validation_data=gen_valid,
validation_steps=N_DATA['valid'] // valid_batch_size)
print("Keunwoo: Training is done. Loading the best weights...")
model.load_weights("{}_best_weights.h5".format(exp_name))
gen_test = data_gen('test', None, is_shuffle=False, is_infinite=True, batch_size=1)
print(" Evaluating...")
scores = model.evaluate_generator(gen_test, N_DATA['test'])
print('Keunwoo: Done for {}!'.format(model_name))
print(" test set loss:{}".format(scores[0]))
print(" test set accuracy: {}%".format([scores[1]]))
def main(model_name, exp_name='fma'):
"""
DO it!
"""
assert model_name in ['multi_kernel', 'crnn', 'cnn3x3', 'cnn1d']
print("-" * 60)
print("Keunwoo: Welcome! Lets do something deep with FMA dataset.")
print(" I'm assuming you finished pre-processing.")
print(" We're gonna use {} model".format(model_name))
csv_path = os.path.join(DIR_FMA_CSV, 'tracks.csv')
tracks = pd.read_csv(csv_path, index_col=0, header=[0, 1])
small = tracks['set', 'subset'] == 'small'
training = (tracks['set', 'split'] == 'training') & small
validation = (tracks['set', 'split'] == 'validation') & small
test = (tracks['set', 'split'] == 'test') & small
print("Keunwoo: We're loading and modifying label values.")
enc = LabelEncoder()
y_train = enc.fit_transform(tracks[training]['track', 'genre_top'])
y_valid = enc.transform(tracks[validation]['track', 'genre_top'])
y_test = enc.transform(tracks[test]['track', 'genre_top'])
y_train = np.eye(8)[y_train]
y_valid = np.eye(8)[y_valid]
y_test = np.eye(8)[y_test]
print("It's a good practice to use callbacks in Keras.")
callbacks = my_callbacks.get_callbacks(name=exp_name)
early_stopper, model_saver, weight_saver, csv_logger = callbacks
print("Preparing data generators for training and validation...")
batch_size = 40
steps_per_epoch = len(y_train) // batch_size
gen_train = data_gen(tracks[training], y_train, True, batch_size=batch_size)
gen_valid = data_gen(tracks[validation], y_valid, False, batch_size=batch_size)
print("Keunwoo: Getting model...")
if model_name == 'multi_kernel':
model = models_time_invariant.model_multi_kernel_shape(n_out=8)
elif model_name == 'crnn':
model = models_time_invariant.model_crnn_icassp2017_choi(n_out=8)
elif model_name == 'cnn3x3':
model = models_time_invariant.model_conv3x3_ismir2016_choi(n_out=8)
elif model_name == 'cnn1d':
model = models_time_invariant.model_conv1d_icassp2014_sander(n_out=8)
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])
print("Keunwoo: Starting to train...")
model.fit_generator(gen_train, steps_per_epoch, epochs=5,
callbacks=callbacks,
validation_data=gen_valid,
validation_steps=len(y_valid) // batch_size)
print("Keunwoo: Training is done. Loading the best weights...")
model.load_weights("{}_best_weights.h5".format(exp_name))
gen_test = data_gen(tracks[test], y_test, False, batch_size=batch_size)
print(" Evaluating...")
scores = model.evaluate_generator(gen_test, len(y_test) // batch_size)
print('Keunwoo: Done for {}!'.format(model_name))
print(" test set loss:{}".format(scores[0]))
print(" test set accuracy: {}%".format([scores[1]]))