if item not in selected:
feat = resnet_features_[item]
dist = np.linalg.norm(mu - feat / (k + 1) - sum_others)
if dist < dist_min:
dist_min = dist
newone = item
newonefeat = feat
selected_feat.append(newonefeat)
selected.append(newone)
x_train_protoset[iter_dico].append(x_train_from_cl[newone])
error_message = str(
y_train_from_cl[newone]) + " " + str(iter_dico)
# label should be the same with iter_dico
assert (y_train_from_cl[newone] == iter_dico), error_message
y_train_protoset[iter_dico].append(y_train_from_cl[newone])
if __name__ == '__main__':
# tf.logging.set_verbosity(tf.logging.INFO)
parser = resnet.ResnetArgParser(
resnet_size_choices=[18, 34, 50, 101, 152, 200])
parser.set_defaults(resnet_size=18,
model_dir=models_save_path,
train_epochs=100,
epochs_per_eval=100,
batch_size=256)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(argv=[sys.argv[0]] + unparsed)
labels,
mode,
Cifar10Model,
resnet_size=params['resnet_size'],
weight_decay=weight_decay,
learning_rate_fn=learning_rate_fn,
momentum=0.9,
data_format=params['data_format'],
loss_filter_fn=loss_filter_fn,
multi_gpu=params['multi_gpu'])
def main(unused_argv):
resnet.resnet_main(FLAGS, cifar10_model_fn, input_fn)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
parser = resnet.ResnetArgParser()
# Set defaults that are reasonable for this model.
parser.set_defaults(data_dir='/tmp/cifar10_data',
model_dir='/tmp/cifar10_model',
resnet_size=32,
train_epochs=250,
epochs_per_eval=10,
batch_size=128)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(argv=[sys.argv[0]] + unparsed)