def train(data_type,
seq_length,
model,
saved_model=None,
concat=False,
class_limit=None,
image_shape=None,
load_to_memory=False):
# Set variables.
nb_epoch = 1000
batch_size = 16
# Helper: Save the model.
checkpointer = ModelCheckpoint(
filepath='./data/checkpoints/' + model + '-' + data_type + \
'.{epoch:03d}-{val_loss:.3f}.hdf5',
verbose=1,
save_best_only=True)
incepcheck = ModelCheckpoint(
filepath='./data/checkpoints/' + model + '-' + data_type + \
'.{epoch:03d}-{val_loss:.3f}.hdf5',
verbose=1,
save_best_only=True,
save_weights_only=True)
# Helper: TensorBoard
tb = TensorBoard(log_dir='./data/logs')
# Helper: Stop when we stop learning.
early_stopper = EarlyStopping(patience=10)
# Helper: Save results.
timestamp = time.time()
csv_logger = CSVLogger('./data/logs/' + 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)
else:
data = DataSet(seq_length=seq_length,
class_limit=class_limit,
image_shape=image_shape)
# 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(batch_size, 'train', data_type,
concat)
X_test, y_test = data.get_all_sequences_in_memory(
batch_size, 'test', data_type, concat)
elif model == 'div_crnn':
generator = data.frame_generator2(batch_size, 'train', data_type,
concat)
val_generator = data.frame_generator2(batch_size, 'test', data_type,
concat)
else:
# Get generators.
generator = data.frame_generator(batch_size, 'train', data_type,
concat)
val_generator = data.frame_generator(batch_size, 'test', data_type,
concat)
# Get the model.
rm = ResearchModels(len(data.classes), model, seq_length, saved_model)
# model_json_str = rm.model.to_json()
# open('/home/takubuntu/PycharmProjects/DL/Wake_detect/IR_classification/data/checkpoints/json_model.json','w').write(model_json_str)
# 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=[checkpointer, tb, csv_logger],
epochs=nb_epoch)
# elif model == 'inception*':
# rm.model.fit_generator(
# generator=generator,
# steps_per_epoch=steps_per_epoch,
# epochs=nb_epoch,
# verbose=1,
# callbacks=[incepcheck, tb, csv_logger],
# validation_data=val_generator,
# validation_steps=10)
else:
# Use fit generator.
rm.model.fit_generator(generator=generator,
steps_per_epoch=steps_per_epoch,
epochs=nb_epoch,
verbose=1,
callbacks=[checkpointer, tb, csv_logger],
validation_data=val_generator,
validation_steps=10)
