def train(args):
# First we extract the Data and vectorize it
data_loc = get_path_to_loc(args.data_path)
checkpoint_path = get_path_to_loc(args.checkpoint_prefix)
list_test = run_data_extraction(data_loc)
notes2idx, idx2note = get_notes_mapping_dict(list_test)
notes_vec = vectorize_notes_by_mapping(list_test, notes2idx)
# Now we set the model and the optimizer
model = build_model(len(notes_vec), args.embedding_dim, args.rnn_units,
args.batch_size)
optimizer = set_optimizer(args.optimizer, args.learning_rate)
# train the model
history = []
# plotter = PeriodicPlotter(sec=2, xlabel='Iterations', ylabel='Loss') todo
if hasattr(tqdm, '_instances'):
tqdm._instances.clear() # clear if it exists
for iteration in tqdm(range(args.training_iterations)):
# Grab a batch and propagate it through the network
x_batch, y_batch = get_batch(notes_vec, args.seq_length,
args.batch_size)
loss = train_step(x_batch, y_batch, model, optimizer)
# Update the progress bar
history.append(loss.numpy().mean())
# plotter.plot(history) Todo: put back
# Update the model with the changed weights!
if iteration % 100 == 0:
model.save_weights(checkpoint_path)
# Save the trained model and the weights
model.save_weights(args.checkpoint_prefix)
class_weight = utils.class_weight(training_set[-1])
train_batch_fetcher = iterator_dr_640_segmentation_as_input.TrainBatchFetcher(
training_set, batch_size, segmentation_home, class_weight)
val_batch_fetcher = iterator_dr_640_segmentation_as_input.ValidationBatchFetcher(
validation_set, batch_size, segmentation_home)
# create networks
if FLAGS.load_model_dir:
network_file = utils.all_files_under(FLAGS.load_model_dir,
extension=".json")
weight_file = utils.all_files_under(FLAGS.load_model_dir, extension=".h5")
assert len(network_file) == 1 and len(weight_file) == 1
with open(network_file[0], 'r') as f:
network = model_from_json(f.read())
network.load_weights(weight_file[0])
network = model.set_optimizer(network)
else:
network = model.dr_network_segmentation_as_input()
network.summary()
with open(os.path.join(model_out_dir, "network.json"), 'w') as f:
f.write(network.to_json())
# start training
scheduler = utils.Scheduler(schedules)
for epoch in range(n_epochs):
# update step sizes, learning rates
scheduler.update_steps(epoch)
K.set_value(network.optimizer.lr, scheduler.get_lr())
# train on the training set
start_time = time.time()
