# target_size=(224,224)
# elif args.model_name=='xception':
# target_size=(299,299)
# else:
# target_size=(224,224)
histories = []
current_time = datetime.datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S")
experiment_dir = os.path.join(
r'/media/data/jacob/Fossil_Project', 'experiments',
args.model_name, args.dataset_name, args.color_type,
f'lr-{args.base_learning_rate}-bsz_{args.batch_size}',
current_time)
reset_eager_session()
dataset_config = DatasetConfig(
dataset_name=args.dataset_name,
label_col='family',
# target_size=target_size,
# num_channels=num_channels,
grayscale=(args.color_type == 'grayscale'),
low_class_count_thresh=args.low_class_count_thresh,
data_splits={
'val_size': 0.2,
'test_size': 0.2
},
tfrecord_root_dir=args.tfrecord_dir,
data_db_path=args.data_db_path,
num_shards=10)
def main(dataset_name='PNAS',
model_name='vgg16',
experiment_dir=r'/media/data/jacob/Fossil_Project/vgg16/PNAS',
gpu_ids=[0],
tfrecord_root_dir=r'/media/data/jacob/Fossil_Project/tfrecord_data',
batch_size=64,
target_size=(224, 224),
base_learning_rate=0.001,
num_epochs=100,
preprocessing='imagenet',
augment_images=False):
reset_eager_session()
# tf.reset_default_graph()
dataset_config = DatasetConfig(dataset_name=dataset_name,
label_col='family',
target_size=target_size,
channels=3,
low_class_count_thresh=3,
data_splits={
'val_size': 0.2,
'test_size': 0.2
},
tfrecord_root_dir=tfrecord_root_dir,
num_shards=10)
train_config = TrainConfig(model_name=model_name,
batch_size=batch_size,
frozen_layers=(0, -4),
base_learning_rate=base_learning_rate,
buffer_size=1000,
num_epochs=num_epochs,
preprocessing=preprocessing,
augment_images=augment_images,
seed=3)
experiment_config = ExperimentConfig(dataset_config=dataset_config,
train_config=train_config)
############################################
#TODO: Move config definitions outside main() for:
# 1. simplifying overall logic in main & segregating configuration to section marked by if __name__=='__main__'
# 2. Moving towards defining most or all run parameters in separate config files
############################################
def build_tunable_model(hp):
return build_model(name=hp.Choice('name',
values=[
'shallow', 'vgg16', 'xception',
'resnet_50_v2', 'resnet_101_v2'
]),
num_classes=10000,
frozen_layers=(0, -4),
input_shape=(224, 224, 3),
base_learning_rate=hp.Float('base_learning_rate',
min_value=1e-6,
max__value=0.01,
sampling='log'))
RandomSearch(build_tunable_model,
objective='val_accuracy',
max_trials=num_epochs,
seed=seed,
hyperparameters=None,
tune_new_entries=True,
allow_new_entries=True,
**kwargs)
trainer = BaseTrainer(experiment_config=experiment_config)
train_data = trainer.get_data_loader(subset='train')
val_data = trainer.get_data_loader(subset='val')
test_data = trainer.get_data_loader(subset='test')
# AUTOTUNE = tf.data.experimental.AUTOTUNE
# train_data = tfds.load("mnist", split='train').shuffle(1000).batch(batch_size).repeat().prefetch(AUTOTUNE)
model_params = trainer.get_model_params('train')
fit_params = trainer.get_fit_params()
callbacks = get_callbacks(weights_best=os.path.join(
experiment_dir, 'weights_best.h5'),
logs_dir=os.path.join(experiment_dir, 'logdir'),
restore_best_weights=False)
model = build_model(
**model_params
) #name='shallow', num_classes=10000, frozen_layers=(0,-4), input_shape=(224,224,3), base_learning_rate=0.0001)
history = model.fit(train_data,
steps_per_epoch=fit_params['steps_per_epoch'],
epochs=fit_params['epochs'],
validation_data=val_data,
validation_steps=fit_params['validation_steps'],
callbacks=callbacks)
return history