def __init__(self,
kernel_size=3,
stride=2,
n_splits=2,
w_initializer=None,
w_regularizer=None,
acti_func='relu',
name='residual_additive_upsample'):
TrainableLayer.__init__(self, name=name)
self.n_splits = n_splits
self.deconv_param = {'w_initializer': w_initializer,
'w_regularizer': w_regularizer,
'kernel_size': kernel_size,
'acti_func': acti_func,
'stride': stride}
def __init__(self,
kernel_size=3,
stride=2,
n_splits=2,
w_initializer=None,
w_regularizer=None,
acti_func='relu',
name='residual_additive_upsample'):
TrainableLayer.__init__(self, name=name)
self.n_splits = n_splits
self.deconv_param = {'w_initializer': w_initializer,
'w_regularizer': w_regularizer,
'kernel_size': kernel_size,
'acti_func': acti_func,
'stride': stride}
def __init__(self, n_chns, conv_params):
TrainableLayer.__init__(self, name='TwoConv')
self.n_chns = n_chns
self.conv_params = conv_params
def __init__(self, eps=1e-6, gamma_initializer=None, name='instance_norm'):
TrainableLayer.__init__(self, name=name)
self.eps = eps
self.gamma_initializer = gamma_initializer
def __init__(self, n_chns, conv_params):
TrainableLayer.__init__(self, name='TwoConv')
self.n_chns = n_chns
self.conv_params = conv_params