def train_and_test(network, trainer, train_source, test_source, max_epochs,
minibatch_size, epoch_size, restore, profiling):
# define mapping from intput streams to network inputs
input_map = {
network['feature']: train_source.streams.features,
network['label']: train_source.streams.labels
}
if profiling:
start_profiler(sync_gpu=True)
training_session(
trainer=trainer,
mb_source=train_source,
model_inputs_to_streams=input_map,
mb_size=minibatch_size,
progress_frequency=epoch_size,
checkpoint_config=CheckpointConfig(frequency=epoch_size,
filename=os.path.join(
model_path,
"BN-Inception_CIFAR10"),
restore=restore),
test_config=TestConfig(test_source,
minibatch_size=minibatch_size)).train()
if profiling:
stop_profiler()
def main(params):
# Create output and log directories if they don't exist
if not os.path.isdir(params['output_folder']):
os.makedirs(params['output_folder'])
if not os.path.isdir(params['log_folder']):
os.makedirs(params['log_folder'])
# Create the network
network = create_network()
# Create readers
train_reader = cbf_reader(os.path.join(params['input_folder'], 'train{}.cbf'.format(params['prefix'])), is_training=True,
max_samples=cntk.io.INFINITELY_REPEAT)
cv_reader = cbf_reader(os.path.join(params['input_folder'], 'test{}.cbf'.format(params['prefix'])), is_training=False,
max_samples=cntk.io.FULL_DATA_SWEEP)
test_reader = cbf_reader(os.path.join(params['input_folder'], 'test{}.cbf'.format(params['prefix'])), is_training=False,
max_samples=cntk.io.FULL_DATA_SWEEP)
input_map = {
network['input']: train_reader.streams.front,
network['target']: train_reader.streams.label
}
# Create learner
mm_schedule = momentum_schedule(0.90)
lr_schedule = learning_parameter_schedule([(40, 0.1), (40, 0.01)], minibatch_size=params['minibatch_size'])
learner = cntk.adam(network['model'].parameters, lr_schedule, mm_schedule, l2_regularization_weight=0.0005,
epoch_size=params['epoch_size'], minibatch_size=params['minibatch_size'])
# Use TensorBoard for visual logging
log_file = os.path.join(params['log_folder'], 'log.txt')
pp_writer = cntk.logging.ProgressPrinter(freq=10, tag='Training', num_epochs=params['max_epochs'], log_to_file=log_file)
tb_writer = cntk.logging.TensorBoardProgressWriter(freq=10, log_dir=params['log_folder'], model=network['model'])
# Create trainer and training session
trainer = Trainer(network['model'], (network['loss'], network['metric']), [learner], [pp_writer, tb_writer])
test_config = TestConfig(minibatch_source=test_reader, minibatch_size=params['minibatch_size'], model_inputs_to_streams=input_map)
cv_config = CrossValidationConfig(minibatch_source=cv_reader, frequency=(1, DataUnit.sweep),
minibatch_size=params['minibatch_size'], model_inputs_to_streams=input_map)
checkpoint_config = CheckpointConfig(os.path.join(params['output_folder'], model_name), frequency=(10, DataUnit.sweep), restore=params['restore'])
session = training_session(trainer=trainer,
mb_source=train_reader,
mb_size=params['minibatch_size'],
model_inputs_to_streams=input_map,
max_samples=params['epoch_size'] * params['max_epochs'],
progress_frequency=(1, DataUnit.sweep),
checkpoint_config=checkpoint_config,
cv_config=cv_config,
test_config=test_config)
cntk.logging.log_number_of_parameters(network['model'])
session.train()
# Save the trained model
path = os.path.join(params['output_folder'], 'final_model.dnn')
network['model'].save(path)
print('Saved final model to', path)