def __init__(self,
dim_in,
dim_out,
weight_decay,
dropout=0.,
act=lambda x: x,
bias=True,
model_pretrain=None,
**kwargs):
"""
model_pretrain is not None if you want to load the trained model
model_pretrain[0] is weights
model_pretrain[1] is bias
"""
super(Dense, self).__init__(**kwargs)
self.dropout = dropout
self.act = F_ACT[act]
self.bias = bias
self.dim_in = dim_in
self.dim_out = dim_out
self.weight_decay = weight_decay
with tf.variable_scope(self.name + '_vars'):
if model_pretrain is None:
self.vars['weights'] = tf.get_variable(
'weights',
shape=(dim_in, dim_out),
dtype=DTYPE,
initializer=tf.contrib.layers.xavier_initializer(),
regularizer=tf.contrib.layers.l2_regularizer(
self.weight_decay))
if self.bias:
self.vars['bias'] = zeros([dim_out], name='bias')
else:
self.vars['weights'] = trained(model_pretrain[0],
name='weight')
if self.bias:
self.vars['bias'] = trained(model_pretrain[1], name='bias')
if self.logging:
self._log_vars()
def __init__(self,
dim_in,
dim_out,
dropout=0.,
act='relu',
order=1,
aggr='mean',
model_pretrain=None,
is_train=True,
bias='norm',
**kwargs):
super(HighOrderAggregator, self).__init__(**kwargs)
self.dropout = dropout
self.bias = bias
self.act = F_ACT[act]
self.order = order
self.aggr = aggr
self.is_train = is_train
if dim_out > 0:
with tf.variable_scope(self.name + '_vars'):
if model_pretrain is None:
for o in range(self.order + 1):
_k = 'order{}_weights'.format(o)
self.vars[_k] = glorot([dim_in, dim_out], name=_k)
else:
for o in range(self.order + 1):
_k = 'order{}_weights'.format(o)
self.vars[_k] = trained(model_pretrain[0], name=_k)
for o in range(self.order + 1):
_k = 'order{}_bias'.format(o)
self.vars[_k] = zeros([dim_out], name=_k)
if self.bias == 'norm':
for o in range(self.order + 1):
_k1 = 'order{}_offset'.format(o)
_k2 = 'order{}_scale'.format(o)
self.vars[_k1] = zeros([1, dim_out], name=_k1)
self.vars[_k2] = ones([1, dim_out], name=_k2)
print('>> layer {}, dim: [{},{}]'.format(self.name, dim_in, dim_out))
if self.logging:
self._log_vars()
self.dim_in = dim_in
self.dim_out = dim_out