def __init__(self, input_dim, output_dim, dropout=0.,
act=tf.nn.relu, placeholders=None, bias=True, featureless=False,
sparse_inputs=False, **kwargs):
super(DenseLayerWithWeights, self).__init__(**kwargs)
self.dropout = dropout
self.act = act
self.featureless = featureless
self.bias = bias
self.input_dim = input_dim
self.output_dim = output_dim
# helper variable for sparse dropout
self.sparse_inputs = sparse_inputs
if sparse_inputs:
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
self.vars['weights'] = tf.get_variable('weights', shape=(input_dim, output_dim),
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),
regularizer=tf.contrib.layers.l2_regularizer(FLAGS.weight_decay))
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def __init__(self,
input_dim1,
input_dim2,
placeholders,
dropout=False,
act=tf.nn.sigmoid,
loss_fn='xent',
bias=False,
bilinear_weights=False,
**kwargs):
"""
Basic class that applies skip-gram-like loss
(i.e., dot product of node+target and node and negative samples)
Args:
bilinear_weights: use a bilinear weight for affinity calculation: u^T A v. If set to
false, it is assumed that input dimensions are the same and the affinity will be
based on dot product.
"""
super(BipartiteEdgePredLayer, self).__init__(**kwargs)
self.input_dim1 = input_dim1
self.input_dim2 = input_dim2
self.act = act
self.bias = bias
self.eps = 1e-7
# Margin for hinge loss
self.margin = 0.1
self.bilinear_weights = bilinear_weights
if dropout:
self.dropout = placeholders['dropout']
else:
self.dropout = 0.
# output a likelihood term
self.output_dim = 1
with tf.variable_scope(self.name + '_vars'):
# bilinear form
if bilinear_weights:
#self.vars['weights'] = glorot([input_dim1, input_dim2],
# name='pred_weights')
self.vars['weights'] = tf.get_variable(
'pred_weights',
shape=(input_dim1, input_dim2),
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
if self.bias:
self.vars['bias'] = zeros([self.output_dim], name='bias')
if loss_fn == 'xent':
self.loss_fn = self._xent_loss
elif loss_fn == 'skipgram':
self.loss_fn = self._skipgram_loss
elif loss_fn == 'hinge':
self.loss_fn = self._hinge_loss
if self.logging:
self._log_vars()
def __init__(self, input_dim, output_dim, dropout=0.,
act=tf.nn.relu, placeholders=None, bias=True, featureless=False,
sparse_inputs=False, **kwargs):
super(Dense, self).__init__(**kwargs)
self.dropout = dropout
self.act = act
self.featureless = featureless
self.bias = bias
self.input_dim = input_dim
self.output_dim = output_dim
# helper variable for sparse dropout
self.sparse_inputs = sparse_inputs
if sparse_inputs:
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.compat.v1.variable_scope(self.name + '_vars'):
self.vars['weights'] = tf.compat.v1.get_variable('weights', shape=(input_dim, output_dim),
dtype=tf.float32,
initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"),
regularizer=tf.keras.regularizers.l2(0.5 * (FLAGS.weight_decay)))
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
