parser.add_argument('--deconv',
action='store_true',
help='save visualization data from deconvolution')
parser.add_argument('--subset_pct',
type=float,
default=100,
help='subset of training dataset to use (percentage)')
args = parser.parse_args()
model, cost = create_network()
rseed = 0 if args.rng_seed is None else args.rng_seed
# setup data provider
assert 'train' in args.manifest, "Missing train manifest"
assert 'val' in args.manifest, "Missing validation manifest"
train = make_alexnet_train_loader(args.manifest['train'], args.manifest_root,
model.be, args.subset_pct, rseed)
valid = make_validation_loader(args.manifest['val'], args.manifest_root,
model.be, args.subset_pct)
sched_weight = Schedule([10], change=0.1)
opt = GradientDescentMomentum(0.01, 0.9, wdecay=0.0005, schedule=sched_weight)
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model,
eval_set=valid,
metric=valmetric,
**args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train, valid)
parser = NeonArgparser(__doc__, default_config_files=config_files,
default_overrides=dict(batch_size=64))
parser.add_argument('--deconv', action='store_true',
help='save visualization data from deconvolution')
parser.add_argument('--subset_pct', type=float, default=100,
help='subset of training dataset to use (percentage)')
args = parser.parse_args()
model, cost = create_network()
rseed = 0 if args.rng_seed is None else args.rng_seed
# setup data provider
assert 'train' in args.manifest, "Missing train manifest"
assert 'val' in args.manifest, "Missing validation manifest"
train = make_alexnet_train_loader(args.manifest['train'], args.manifest_root,
model.be, args.subset_pct, rseed)
valid = make_validation_loader(args.manifest['val'], args.manifest_root,
model.be, args.subset_pct)
sched_weight = Schedule([10], change=0.1)
opt = GradientDescentMomentum(0.01, 0.9, wdecay=0.0005, schedule=sched_weight)
# configure callbacks
valmetric = TopKMisclassification(k=5)
callbacks = Callbacks(model, eval_set=valid, metric=valmetric, **args.callback_args)
if args.deconv:
callbacks.add_deconv_callback(train, valid)
model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
