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)