vertical_flip=args.v_flip)
val_datagen = ImageDataGenerator(
preprocessing_function=preprocessing_function)
train_generator = train_datagen.flow_from_directory(TRAIN_DIR,
target_size=(HEIGHT,
WIDTH),
batch_size=BATCH_SIZE)
validation_generator = val_datagen.flow_from_directory(
VAL_DIR, target_size=(HEIGHT, WIDTH), batch_size=BATCH_SIZE)
# Save the list of classes for prediction mode later
class_list = utils.get_subfolders(TRAIN_DIR)
utils.save_class_list(class_list, model_name=args.model, dataset_name="")
finetune_model = utils.build_finetune_model(base_model,
dropout=args.dropout,
fc_layers=FC_LAYERS,
num_classes=len(class_list))
if args.continue_training:
finetune_model.load_weights("./checkpoints/" + args.model +
"_model_weights.h5")
print("load success!")
adam = Adam(lr=0.00001)
finetune_model.compile(adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
)
val_datagen = ImageDataGenerator(preprocessing_function=preprocessing_function)
train_generator = train_datagen.flow_from_directory(TRAIN_DIR, target_size=(HEIGHT, WIDTH), batch_size=BATCH_SIZE)
validation_generator = val_datagen.flow_from_directory(VAL_DIR, target_size=(HEIGHT, WIDTH), batch_size=BATCH_SIZE)
"""
### STOP EDITING HERE .
# Save the list of classes for prediction mode later
class_list = utils.get_subfolders(TRAIN_DIR)
utils.save_class_list(
OUT_DIR,
class_list,
model_name=args.model,
dataset_name=os.path.basename(args.dataset),
)
optim = eval(args.optimizer)(lr=args.lr)
if args.continue_training is not None:
finetune_model = load_model(args.continue_training)
if args.transfer_strategy == "finetune":
utils.set_trainable(finetune_model, True)
else:
finetune_model = utils.build_finetune_model(
base_model,
dropout=args.dropout,
fc_layers=FC_LAYERS,
num_classes=len(class_list),
as_fixed_feature_extractor=True
USE_BOTTLENECKS = False
# Create directories if needed
if not os.path.isdir("checkpoints"):
os.makedirs("checkpoints")
if not os.path.isdir("bottlenecks"):
os.makedirs("bottlenecks")
if not os.path.isdir("models"):
os.makedirs("models")
if USE_BOTTLENECKS and not args.skip_bottleneck_check:
save_bottleneck_features()
class_list = utils.get_subfolders(TRAIN_DIR)
utils.save_class_list(class_list,
model_name=args.model,
dataset_name=DATASET_NAME)
num_classes = len(class_list)
if (num_classes > 2):
class_mode = 'categorical'
else:
class_mode = 'binary'
print("Class Mode -->", class_mode,
"(" + str(num_classes) + " categories)")
if USE_BOTTLENECKS:
train_generator, validation_generator = prepare_bottleneck_data_generators(
val_split=args.val_split, batch_size=BATCH_SIZE)
else:
val_datagen = ImageDataGenerator(
preprocessing_function=preprocessing_function)
train_generator = train_datagen.flow_from_directory(
BASE_IMG_DIR + TRAIN_DIR,
target_size=(HEIGHT, WIDTH),
batch_size=BATCH_SIZE)
validation_generator = val_datagen.flow_from_directory(
BASE_IMG_DIR + VAL_DIR,
target_size=(HEIGHT, WIDTH),
batch_size=BATCH_SIZE)
# Save the list of classes for prediction mode later
class_list = utils.get_subfolders(BASE_IMG_DIR + TRAIN_DIR)
utils.save_class_list(class_list, model_name=args.model)
finetune_model = utils.build_finetune_model(base_model,
dropout=args.dropout,
fc_layers=FC_LAYERS,
num_classes=len(class_list))
if args.continue_training:
finetune_model.load_weights("./checkpoints/" + args.model +
"_model_weights.h5")
adam = Adam(lr=0.00001)
finetune_model.compile(adam,
loss='categorical_crossentropy',
metrics=['accuracy'])