def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = partial(cifar10_resnext_29_wd, cardinality = 8, group_width = 16), optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 300, batch_size = 128, lr_decay_func = DivideAtRates( start = 0.1, divide_by = 10, at = [0.5, 0.75], max_steps = 300), weight_decay = 0.0005 ) 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 = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = cifar10_resnet_bottleneck_20_wd, optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 200, batch_size = 128, lr_decay_func = DivideAtRates( start = 0.1, divide_by = 10, at = [0.5, 0.75], max_steps = 200), weight_decay = 0.0001 ) 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 = channel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = partial(cifar10_densenet_40_wd, drop_rate = 0.0), optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 300, batch_size = 64, lr_decay_func = DivideAtRates( start = 0.1, divide_by = 10, at = [0.5, 0.75], max_steps = 300), weight_decay = 0.0001 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)