def main(argv=None):
model = RNN(train_images_dir='data/train/',
val_images_dir='data/val/',
test_images_dir='data/test/',
num_epochs=40,
train_batch_size=1000,
val_batch_size=1000,
test_batch_size=1000,
height_of_image=28,
width_of_image=28,
num_channels=1,
num_classes=10,
learning_rate=0.001,
base_dir='results',
max_to_keep=2,
model_name="RNN",
model='RNN')
model.create_network()
model.initialize_network()
if True:
model.train_model(1, 1, 1, 2)
else:
model.test_model()
def main(argv=None):
model = RNN(train_images_dir=FLAGS.train_images_dir,
val_images_dir=FLAGS.val_images_dir,
test_images_dir=FLAGS.test_images_dir,
num_epochs=FLAGS.num_epochs,
train_batch_size=FLAGS.train_batch_size,
val_batch_size=FLAGS.val_batch_size,
test_batch_size=FLAGS.test_batch_size,
height_of_image=FLAGS.height_of_image,
width_of_image=FLAGS.width_of_image,
num_channels=FLAGS.num_channels,
num_classes=FLAGS.num_classes,
learning_rate=FLAGS.learning_rate,
base_dir=FLAGS.base_dir,
max_to_keep=FLAGS.max_to_keep,
model_name=FLAGS.model_name,
keep_prob=FLAGS.keep_prob)
model.create_network()
model.initialize_network()
if FLAGS.train:
model.train_model(FLAGS.display_step, FLAGS.validation_step,
FLAGS.checkpoint_step, FLAGS.summary_step)
else:
model.test_model()
def main(argv=None):
if FLAGS.mode == 'preprocess':
prepare_dataset(FLAGS.data_path, FLAGS.subset, FLAGS.split_valid, FLAGS.slice_duration, FLAGS.save_as)
exit()
if FLAGS.mode == 'generate_noisy':
generate_noisy_signal(FLAGS.song_path)
exit()
model = RNN(
train_dir=FLAGS.data_path + '/train',
val_dir=FLAGS.data_path + '/valid',
test_dir=FLAGS.data_path + '/test',
train_batch_size=FLAGS.train_batch_size,
valid_batch_size=FLAGS.valid_batch_size,
test_batch_size=FLAGS.test_batch_size,
n_inputs=FLAGS.n_inputs,
seq_length=FLAGS.seq_length,
num_epochs=FLAGS.num_epochs,
learning_rate=FLAGS.learning_rate,
base_dir=FLAGS.base_dir,
max_to_keep=FLAGS.max_to_keep,
model_name=FLAGS.model_name
)
model.create_network()
model.initialize_network()
if FLAGS.mode == 'train':
model.train_model(FLAGS.display_step, FLAGS.validation_step, FLAGS.checkpoint_step, FLAGS.summary_step)
elif FLAGS.mode == 'test':
model.test_model()
elif FLAGS.mode == 'test_song':
model.estimate_test_song(FLAGS.noisy_song_path, FLAGS.output_estimated_path)
