def multi_driver_training(options):
""" Train model using all drivers' data in the given datapath."""
print("Loading data...")
dataset = load_all(options)
print("Building model and compiling functions...")
net = models.cnn_1(options, output_size=len(dataset['label_map']))
# net = models.softmax_only(output_size=len(dataset['label_map']))
print("Starting training...")
start_time = time.time()
try:
train_loop(dataset['train_data'], dataset['val_data'], net, options)
print('Training Complete...')
except KeyboardInterrupt:
print('Keyboard Interrupt...')
end_time = time.time()
print('--------------------')
print(' Saving model, check logs for results.\n'
' Time taken: {0}\n'
.format(end_time - start_time))
utils.save_model(model=get_all_param_values(net), options=options)
return net
def _train_single(driver_index, dataset, options):
print('Building model and compiling functions...')
net = models.cnn_1(options, output_size=2)
# net = models.softmax_only(output_size=len(dataset['label_map']))
train_labels = (dataset['train_data']['y'] == driver_index)
val_labels = (dataset['val_data']['y'] == driver_index)
assert(train_labels.dtype == bool)
assert(val_labels.dtype == bool)
driver_dataset = dict(train_data=dict(x=dataset['train_data']['x'], y=train_labels),
val_data=dict(x=dataset['val_data']['x'], y=val_labels))
train_loop(driver_dataset['train_data'], driver_dataset['val_data'], net, options)
return net
