def __init__(self, n_layers, x_names, input_dim, output_dim, dropout, bias, act=lambda x: x,**kwargs):
super(Fusion, self).__init__(**kwargs)
self.n_layers = n_layers + 1
self.input_dim = input_dim
self.output_dim = output_dim
self.act = act
self.dropout = dropout
self.node_features = x_names[0]
self.neighbor_features = x_names[1]
self.bias = bias
# self.fusion_dim = 128
self.fusion_dim = self.output_dim
self.start_h = 0
# if len(self.node_features) == 0 and self.m_name no:
# self.start_h += 1
self.fusion_dim = self.input_dim
for i in range(self.start_h, self.n_layers):
self.vars['weights_'+str(i)] = glorot((self.input_dim, self.fusion_dim), name='weights_'+str(i))
# self.vars['weights_final'] = identity((self.fusion_dim, self.output_dim), name='weights_final')
self.vars['weights_final'] = identity((self.fusion_dim, self.fusion_dim), name='weights_final')
# Matrix gating
self.vars['weights_A'] = identity((self.fusion_dim, self.fusion_dim), name='weights_A')
# Triple vector Gating
gate_dim = self.fusion_dim
self.vars['weights_C'] = identity((self.fusion_dim, gate_dim), name='weights_C')
self.vars['weights_D'] = identity((self.fusion_dim, gate_dim), name='weights_D')
self.vars['weights_V'] = tanh_init((1, gate_dim), name='weights_V')
self.vars['weights'] = glorot((self.fusion_dim, self.output_dim), name='weights_final2')
def __init__(self,
n_layers,
x_names,
input_dim,
output_dim,
dropout,
bias,
act=lambda x: x,
**kwargs):
super(Fusion, self).__init__(**kwargs)
self.n_layers = n_layers + 1
self.input_dim = input_dim
self.output_dim = output_dim
self.act = act
self.dropout = dropout
self.node_features = x_names[0]
self.neighbor_features = x_names[1]
self.bias = bias
# Default
self.fusion_dim = self.output_dim
self.start_h = 0
if len(self.node_features) == 0 and self.m_name not in [
'krylov', 'cheby'
]:
self.start_h += 1
self.fusion_dim = self.output_dim
for i in range(self.start_h, self.n_layers):
self.vars['weights_' + str(i)] = glorot(
(self.input_dim, self.fusion_dim), name='weights_' + str(i))
def __init__(self, n_layers, x_names, input_dim, output_dim, dropout, bias, act=lambda x: x,**kwargs):
super(Fusion, self).__init__(**kwargs)
self.n_layers = n_layers + 1
self.input_dim = input_dim
self.output_dim = output_dim
self.act = act
self.dropout = dropout
self.node_features = x_names[0]
self.neighbor_features = x_names[1]
self.bias = bias
self.start_h = 0
self.fusion_dim = self.input_dim
for i in range(self.start_h, self.n_layers):
self.vars['weights_' + str(i)] = glorot((self.input_dim, self.fusion_dim), name='weights_' + str(i))
self.vars['weights'] = glorot((self.fusion_dim, self.output_dim), name='weights_final')
def __init__(self, n_layers, x_names, input_dim, output_dim, dropout, bias, act=lambda x: x,**kwargs):
super(Fusion, self).__init__(**kwargs)
self.n_layers = n_layers + 1
self.input_dim = input_dim
self.output_dim = output_dim
self.act = act
self.dropout = dropout
self.node_features = x_names[0]
self.neighbor_features = x_names[1]
self.bias = bias
# self.fusion_dim = self.output_dim
self.fusion_dim = self.input_dim
self.start_h = 0
self.lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(self.fusion_dim, forget_bias=1.0)
self.lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(self.fusion_dim, forget_bias=1.0)
self.vars['weights_att'] = glorot((self.fusion_dim*2, 1), name='weights_A')
self.vars['weights'] = glorot((self.fusion_dim*2, self.output_dim), name='weights_final')
def __init__(self,
layer_id,
x_names,
dims,
dropout,
act=tf.nn.relu,
nnz_features=None,
sparse_inputs=False,
bias=False,
shared_weights=True,
skip_connection=False,
add_labels=False,
weights=True,
**kwargs):
super(Kernels_new, self).__init__(**kwargs)
self.layer_id = layer_id
self.act = act
self.dropout = dropout
self.skip_connetion = skip_connection
self.add_labels = add_labels
self.nnz_features = nnz_features
self.sparse_inputs = sparse_inputs
self.weights = weights
self.bias = bias
self.shared_weights = shared_weights
self.node = None
self.neighbor = None
self.g0 = None
self.g1 = None
self.node_feautures = x_names[0]
self.neighbor_features = x_names[1]
if self.add_labels:
if len(self.neighbor_features) == 0:
self.neighbor_features = ['l']
else:
self.neighbor_features.append('l')
# Weights initialization
self.input_dims = {}
# Initially 'x' and 'h' save same input
if self.layer_id == 0 and self.m_name != '':
self.input_dims['x'] = dims[0]
else:
self.input_dims['x'] = dims[1]
self.input_dims['h'] = dims[layer_id]
self.input_dims['l'] = dims[-1]
self.output_dim = dims[layer_id + 1]
self.node_dims = 0
self.neigh_dims = 0
if self.bias:
self.vars['bias'] = const([self.output_dim])
# Compute total dimensions
for key in self.node_feautures:
self.node_dims += self.input_dims[key]
for key in self.neighbor_features:
self.neigh_dims += self.input_dims[key]
self.weights_node = {}
self.bias_node = None
self.weights_neigh = {}
self.bias_neigh = None
if weights:
if not shared_weights:
# Neigh weights
with tf.variable_scope(self.name + "_neighbor_vars"):
keys = self.neighbor_features
for key in keys:
self.weights_neigh[key] = glorot(
(self.input_dims[key], self.output_dim),
name=key + 'weights')
# Node weights
with tf.variable_scope(self.name + "_node_vars"):
if shared_weights:
keys = self.node_feautures + list(
set(self.neighbor_features) - set(self.node_feautures))
else:
keys = self.node_feautures
for key in keys:
self.weights_node[key] = glorot(
(self.input_dims[key], self.output_dim),
name=key + 'weights')
