def main():
"""
Load data and train a model on it.
"""
args = argument_parser().parse_args()
random.seed(args.seed)
train_set, val_set, test_set = read_dataset(DATA_DIR)
model = MiniImageNetModel(args.classes, **model_kwargs(args))
with tf.Session() as sess:
if not args.pretrained:
print('Training...')
train(sess, model, train_set, test_set, args.checkpoint,
**train_kwargs(args))
else:
print('Restoring from checkpoint...')
tf.train.Saver().restore(
sess, tf.train.latest_checkpoint(args.checkpoint))
print('Evaluating...')
eval_kwargs = evaluate_kwargs(args)
#print('Train accuracy: ' + str(evaluate(sess, model, train_set, **eval_kwargs)))
#print('Validation accuracy: ' + str(evaluate(sess, model, val_set, **eval_kwargs)))
print('Test accuracy: ' +
str(evaluate(sess, model, test_set, **eval_kwargs)))
def main():
"""
Load data and train a model on it.
"""
args = argument_parser().parse_args()
random.seed(args.seed)
train_set, val_set, test_set = read_dataset(DATA_DIR)
if args.org:
model = MiniImageNetModel(args.classes, **model_kwargs(args))
run_miniimagenet(model, args, train_set, val_set, test_set,
args.checkpoint_org, "MODEL WITHOUT REGULARIZATION")
tf.reset_default_graph()
if args.reg:
reg_model = RegularizedMiniImageNetModel(args.classes,
**model_kwargs(args))
run_miniimagenet(reg_model, args, train_set, val_set, test_set,
args.checkpoint_reg, "MODEL WITH REGULARIZATION")
def main():
"""
Load data and train a model on it.
"""
context = neptune.Context()
context.integrate_with_tensorflow()
final_train_channel = context.create_channel('final_train_accuracy', neptune.ChannelType.NUMERIC)
final_val_channel = context.create_channel('final_val_accuracy', neptune.ChannelType.NUMERIC)
final_test_channel = context.create_channel('final_test_accuracy', neptune.ChannelType.NUMERIC)
args = neptune_args(context)
print('args:', args)
random.seed(args.seed)
train_set, val_set, test_set = read_dataset(args.miniimagenet_src)
model = ProgressiveMiniImageNetModel(args.classes, **model_kwargs(args))
config = tf.ConfigProto()
config.gpu_options.allow_growth = args.allow_growth
with tf.Session(config=config) as sess:
if not args.pretrained:
print('Training...')
train(sess, model, train_set, test_set, args.checkpoint, **train_kwargs(args))
else:
print('Restoring from checkpoint...')
tf.train.Saver().restore(sess, tf.train.latest_checkpoint(args.checkpoint))
print('Evaluating...')
eval_kwargs = evaluate_kwargs(args)
final_train_accuracy = evaluate(sess, model, train_set, **eval_kwargs)
print('final_train_accuracy:', final_train_accuracy)
final_train_channel.send(final_train_accuracy)
final_val_accuracy = evaluate(sess, model, val_set, **eval_kwargs)
print('final_val_accuracy:', final_val_accuracy)
final_val_channel.send(final_val_accuracy)
final_test_accuracy = evaluate(sess, model, test_set, **eval_kwargs)
print('final_test_accuracy:', final_test_accuracy)
final_test_channel.send(final_test_accuracy)
def main():
"""
Load data and train a model on it.
"""
args = argument_parser().parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
random.seed(args.seed)
train_set, val_set, test_set = read_dataset(DATA_DIR)
if args.metatransfer:
model = MiniImageNetMetaTransferModel(args.classes,
**model_kwargs(args))
else:
model = MiniImageNetModel(args.classes, **model_kwargs(args))
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
if not args.pretrained:
print('Training...')
train(sess, model, train_set, test_set, args.checkpoint,
**train_kwargs(args))
else:
print('Restoring from checkpoint...')
tf.train.Saver().restore(
sess, tf.train.latest_checkpoint(args.checkpoint))
print('Evaluating...')
eval_kwargs = evaluate_kwargs(args)
# print('Train accuracy: ' + str(evaluate(sess, model, train_set, **eval_kwargs)))
# print('Validation accuracy: ' + str(evaluate(sess, model, val_set, **eval_kwargs)))
print('Test accuracy: ' +
str(evaluate(sess, model, test_set, **eval_kwargs)))
