def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(
tr_x, tr_y, te_x, te_y, net_func=cifar10_sequential_cbn6d)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(
tr_x, tr_y, te_x, te_y, net_func=cifar10_bn_inception_v1)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(
tr_x, tr_y, te_x, te_y, net_func=cifar10_resnet_bottleneck_20)
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
def main():
tr_x, tr_y, te_x, te_y = load_cifar10_data()
tr_x, te_x = global_mean_std(tr_x, te_x)
mean_acc, max_acc, min_acc = eval_net_basic(tr_x,
tr_y,
te_x,
te_y,
net_func=partial(
cifar10_se_resnext_29,
cardinality=8,
group_width=16))
print("Mean accuracy: ", mean_acc)
print("Max accuracy: ", max_acc)
print("Min accuracy: ", min_acc)
