def train_and_test(network,
trainer,
train_source,
test_source,
minibatch_size,
epoch_size,
restore,
profiling=False):
# define mapping from intput streams to network inputs
input_map = {
network['feature']: train_source.streams.features,
network['label']: train_source.streams.labels
}
# Train all minibatches
if profiling:
cntk.start_profiler(sync_gpu=True)
training_session(
trainer=trainer,
mb_source=train_source,
var_to_stream=input_map,
mb_size=minibatch_size,
progress_frequency=epoch_size,
checkpoint_config=CheckpointConfig(frequency=epoch_size,
filename=os.path.join(
model_path,
"ConvNet_CIFAR10_DataAug"),
restore=restore),
cv_config=CrossValidationConfig(source=test_source,
mb_size=minibatch_size)).train()
if profiling:
cntk.stop_profiler()
def train_and_test(network, trainer, train_source, test_source, progress_writers, minibatch_size, epoch_size, restore, profiling=False):
# define mapping from intput streams to network inputs
input_map = {
network['feature']: train_source.streams.features,
network['label']: train_source.streams.labels
}
training_session = cntk.training_session(
training_minibatch_source = train_source,
trainer = trainer,
model_inputs_to_mb_source_mapping = input_map,
mb_size_schedule = cntk.minibatch_size_schedule(minibatch_size),
progress_printer = progress_writers,
checkpoint_frequency = epoch_size,
checkpoint_filename = os.path.join(model_path, "ConvNet_CIFAR10_DataAug"),
# save_all_checkpoints = False,
progress_frequency=epoch_size,
cv_source = test_source,
cv_mb_size_schedule=cntk.minibatch_size_schedule(minibatch_size),
# cv_frequency = epoch_size,
restore=restore)
# Train all minibatches
if profiling:
cntk.start_profiler(sync_gpu=True)
training_session.train()
if profiling:
cntk.stop_profiler()
def train_and_test(network, trainer, train_source, test_source, minibatch_size, epoch_size, restore, profiling=False):
# define mapping from intput streams to network inputs
input_map = {
network['feature']: train_source.streams.features,
network['label']: train_source.streams.labels
}
# Train all minibatches
if profiling:
cntk.start_profiler(sync_gpu=True)
training_session(
trainer=trainer, mb_source = train_source,
var_to_stream = input_map,
mb_size = minibatch_size,
progress_frequency=epoch_size,
checkpoint_config = CheckpointConfig(frequency = epoch_size,
filename = os.path.join(model_path, "ConvNet_CIFAR10_DataAug"),
restore = restore),
cv_config = CrossValidationConfig(source = test_source, mb_size=minibatch_size)
).train()
if profiling:
cntk.stop_profiler()