H = 1.
kernel_lr_multiplier = 'Glorot'
data_format = 'channels_last'
# nn
batch_size = 32
epochs = 20 
channels = 1
img_rows = 28 
img_cols = 28 
filters = 32 
kernel_size = (3, 3)
pool_size = (2, 2)
hidden_units = 128
classes = 10
use_bias = False

# learning rate schedule
lr_start = 1e-3
lr_end = 1e-4
lr_decay = (lr_end / lr_start)**(1. / epochs)

# BN
epsilon = 1e-6
momentum = 0.9

# dropout
p1 = 0.25
p2 = 0.5
