def shallow_finetune_labelflip(feat_net='resnet50', double_seeds=True, outlier_model=False):
if cfg.use_toy_dataset:
trainset_name = cfg.dataset + '_train'
validset_name = None
valid_split = 0.3
else:
trainset_name = cfg.dataset + '_train' + ('_ds' if double_seeds else '')
validset_name = cfg.dataset + '_valid'
valid_split = 0
print("Shallow train on features from net: " + feat_net)
print("Trainset: " + trainset_name)
in_shape = cfg.feat_shape_dict[feat_net]
out_shape = feat_dataset_n_classes(trainset_name, feat_net)
SNB = ShallowNetBuilder(in_shape, out_shape)
SL = ShallowLoader(trainset_name, feat_net)
load_iter = ['10']
decay = [0.1, 0.01, 0.001, 0.0001]
lrs = [0.01, 0.001, 0.0001, 0.00001]
for li in load_iter:
for dc in decay:
for lr in lrs:
extr_n = '_ft@{}_dc-{}_lr-{}'.format(li, dc, lr)
opt = SGD(lr=0.001, momentum=0.9, decay=1e-6, nesterov=True)
snet = [SNB.H8K(extr_n, lf_decay=0.01).init(lf=True).load(SL, li, SNB.H8K())]
ST = ShallowTrainer(feat_net, trainset_name, validset_name, valid_split, batch_size=BATCH, loss=LOSS, metric=METRIC)
ST.train(snet, opt, epochs=20, chk_period=1)
def extract_shallow_features():
feat_net = 'resnet50'
cfg.init(include_nets=[feat_net])
old_trainset_name = cfg.dataset + '_train_ds'
#old_testset_name = cfg.dataset + '_test'
dataset_name = cfg.dataset + '_train_ds'
dataset_name = cfg.dataset + '_test'
#crop, size = cfg.crop_size(net=feat_net)
print("\nloading dataset: " + dataset_name)
try:
dataset = common.feat_dataset(dataset_name, feat_net)
except IOError:
print("Can't open dataset.")
return
print("dataset loaded.")
in_shape = cfg.feat_shape_dict[feat_net]
out_shape = feat_dataset_n_classes(dataset_name, feat_net)
B = ShallowNetBuilder(in_shape, out_shape)
SL = ShallowLoader(old_trainset_name, feat_net)
pretrain_weight_epoch = '10'
labelflip_finetune_epoch = '00'
out_layer = 'additional_hidden_0'
extr_n = '_ft@' + pretrain_weight_epoch
model = B.H8K(extr_n, lf_decay=0.01).init(lf=False).load(SL, labelflip_finetune_epoch).model()
#model.summary()
feature_vectors = net_utils.extract_features(model, dataset, out_layer, batch_size, True)
feature_vectors.save_hdf5("shallow_extracted_features/shallow_feat_" + dataset_name + ".h5")