64,
aug=aug,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
64,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
# if there is no specific model checkpoint supplied, then initialize
# the network and compile the model
if args["model"] is None:
print("[INFO] compiling model...")
model = DeeperGoogLeNet.build(width=64,
height=64,
depth=3,
classes=config.NUM_CLASSES,
reg=0.0002)
opt = Adam(1e-3)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# otherwise, load the checkpoint from disk
else:
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(K.get_value(
model.optimizer.lr)))
def main():
"""Train DeeperGoogLeNet
"""
# construct the argument parse and parse the arguments
args = argparse.ArgumentParser()
args.add_argument("-c",
"--checkpoints",
required=True,
help="path to output checkpoint directory")
args.add_argument("-m",
"--model",
type=str,
help="path to *specific* model checkpoint to load")
args.add_argument("-s",
"--start-epoch",
type=int,
default=0,
help="epoch to restart training at")
args = vars(args.parse_args())
# construct the training image generator for data augmentation
augmentation = ImageDataGenerator(rotation_range=18,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest")
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(64, 64)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
image_to_array_preprocessor = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
train_gen = HDF5DatasetGenerator(config.TRAIN_HDF5,
64,
augmentation=augmentation,
preprocessors=[
simple_preprocessor,
mean_preprocessor,
image_to_array_preprocessor
],
classes=config.NUM_CLASSES)
val_gen = HDF5DatasetGenerator(config.VAL_HDF5,
64,
preprocessors=[
simple_preprocessor, mean_preprocessor,
image_to_array_preprocessor
],
classes=config.NUM_CLASSES)
# if there is no specific model checkpoint supplied,
# then initialize the network and compile the model
if args["model"] is None:
print("[INFO] compiling model...")
model = DeeperGoogLeNet.build(width=64,
height=64,
depth=3,
classes=config.NUM_CLASSES,
reg=0.0002)
opt = Adam(1e-3)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# otherwise, load the checkpoint from disk
else:
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(
K.get_value(model.optimizer.lr)))
K.set_value(model.optimizer.lr, 1e-5)
print("[INFO] new learning rate: {}".format(
K.get_value(model.optimizer.lr)))
# construct the set of callbacks
callbacks = [
EpochCheckpoint(args["checkpoints"],
every=5,
start_at=args["start_epoch"]),
TrainingMonitor(config.FIG_PATH,
json_path=config.JSON_PATH,
start_at=args["start_epoch"])
]
# train the network
model.fit_generator(train_gen.generator(),
steps_per_epoch=train_gen.num_images // 64,
validation_data=val_gen.generator(),
validation_steps=val_gen.num_images // 64,
epochs=10,
max_queue_size=10,
callbacks=callbacks,
verbose=1)
# close the databases
train_gen.close()
val_gen.close()