def train(data_type,
seq_length,
model,
saved_model=None,
class_limit=None,
image_shape=None,
config=None):
if config is not None:
load_to_memory = config.videoLoadToMemory
batch_size = config.videoBatchSize
nb_epoch = config.videoEpochs
repo_dir = config.repoDir
feature_file_path = config.featureFileName
work_dir = config.workDir
lr = config.videoLearningRate
decay = config.videoDecay
classlist = config.classes
else:
load_to_memory = False
batch_size = 32
nb_epoch = 100
repo_dir = ''
feature_file_path = 'data/data_file.csv'
work_dir = 'data'
lr = 1e-5
decay = 1e-6
classlist = []
# Helper: Save the model.
checkpointpath = os.path.join(work_dir, 'checkpoints')
if not os.path.exists(checkpointpath):
print("Creating checkpoint folder [%s]", checkpointpath)
os.makedirs(checkpointpath)
checkpointer = ModelCheckpoint(
filepath=os.path.join(work_dir, 'checkpoints', model + '-' + data_type + \
'.{epoch:03d}-{val_loss:.3f}.hdf5'),
verbose=1,
save_best_only=True)
# Helper: TensorBoard
logpath = os.path.join(work_dir, 'logs')
if not os.path.exists(logpath):
print("Creating log folder [%s]", logpath)
os.makedirs(logpath)
tb = TensorBoard(log_dir=os.path.join(work_dir, 'logs', model))
# Helper: Stop when we stop learning.
early_stopper = EarlyStopping(patience=5)
# Helper: Save results.
timestamp = time.time()
csv_logger = CSVLogger(os.path.join(logpath, model + '-' + 'training-' + \
str(timestamp) + '.log'))
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length,
class_limit=class_limit,
repo_dir=repo_dir,
feature_file_path=feature_file_path,
work_dir=work_dir,
classlist=classlist)
else:
data = DataSet(seq_length=seq_length,
class_limit=class_limit,
image_shape=image_shape,
repo_dir=repo_dir,
feature_file_path=feature_file_path,
work_dir=work_dir,
classlist=classlist)
# Check if data is sufficient
if False == data.check_data(batch_size):
print("Insufficient data")
sys.exit(0)
# Get samples per epoch.
# Multiply by 0.7 to attempt to guess how much of data.data is the train set.
steps_per_epoch = (len(data.data) * 0.7) // batch_size
if load_to_memory:
# Get data.
X, y = data.get_all_sequences_in_memory('train', data_type)
X_test, y_test = data.get_all_sequences_in_memory('test', data_type)
else:
# Get generators.
generator = data.frame_generator(batch_size, 'train', data_type)
val_generator = data.frame_generator(batch_size, 'test', data_type)
# Get the model.
rm = ResearchModels(len(data.classes), model, seq_length, saved_model, lr,
decay)
# Fit!
if load_to_memory:
# Use standard fit.
rm.model.fit(X,
y,
batch_size=batch_size,
validation_data=(X_test, y_test),
verbose=1,
callbacks=[tb, early_stopper, csv_logger],
epochs=nb_epoch)
else:
# Use fit generator.
rm.model.fit_generator(
generator=generator,
steps_per_epoch=steps_per_epoch,
epochs=nb_epoch,
verbose=1,
callbacks=[tb, early_stopper, csv_logger, checkpointer],
validation_data=val_generator,
validation_steps=40,
workers=4)
