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def train(args, adj_list, features, train_data, train_label, test_data, test_label, paras):
with tf.Session() as sess:
adj_data = adj_list
net = GAS(session=sess, nodes=paras[0], class_size=paras[4], embedding_r=paras[1], embedding_u=paras[2], embedding_i=paras[3], h_u_size=paras[6], h_i_size=para... |
class GEM(Algorithm):
def __init__(self, session, nodes, class_size, meta, embedding, encoding, hop):
self.nodes = nodes
self.meta = meta
self.class_size = class_size
self.embedding = embedding
self.encoding = encoding
self.hop = hop
self.placeholders = {'a... |
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=123, help='Random seed.')
parser.add_argument('--dataset_str', type=str, default='dblp', help="['dblp','example']")
parser.add_argument('--epoch_num', type=int, default=30, help='Number of epochs to tr... |
def set_env(args):
tf.reset_default_graph()
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
|
def get_data(ix, int_batch, train_size):
if ((ix + int_batch) >= train_size):
ix = (train_size - int_batch)
end = train_size
else:
end = (ix + int_batch)
return (train_data[ix:end], train_label[ix:end])
|
def load_data(args):
if (args.dataset_str == 'dblp'):
(adj_list, features, train_data, train_label, test_data, test_label) = load_data_dblp()
if (args.dataset_str == 'example'):
(adj_list, features, train_data, train_label, test_data, test_label) = load_example_gem()
node_size = features.s... |
def train(args, adj_list, features, train_data, train_label, test_data, test_label, paras):
with tf.Session() as sess:
adj_data = adj_list
meta_size = len(adj_list)
net = GEM(session=sess, class_size=paras[2], encoding=args.k, meta=meta_size, nodes=paras[0], embedding=paras[1], hop=args.ho... |
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=123, help='Random seed.')
parser.add_argument('--dataset_str', type=str, default='dblp', help="['dblp','example']")
parser.add_argument('--epoch_num', type=int, default=30, help='Number of epochs to tr... |
def set_env(args):
tf.reset_default_graph()
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
|
def get_data(ix, int_batch, train_size):
if ((ix + int_batch) >= train_size):
ix = (train_size - int_batch)
end = train_size
else:
end = (ix + int_batch)
return (train_data[ix:end], train_label[ix:end])
|
def load_data(args):
if (args.dataset_str == 'dblp'):
(adj_list, features, train_data, train_label, test_data, test_label) = load_data_dblp()
node_size = features.shape[0]
node_embedding = features.shape[1]
class_size = train_label.shape[1]
train_size = len(train_data)
... |
def train(args, adj_list, features, train_data, train_label, test_data, test_label, paras):
with tf.Session() as sess:
adj_data = adj_list
net = GeniePath(session=sess, out_dim=paras[2], dim=args.dim, lstm_hidden=args.lstm_hidden, nodes=paras[0], in_dim=paras[1], heads=args.heads, layer_num=args.l... |
class MeanAggregator(Layer):
'\n Aggregates via mean followed by matmul and non-linearity.\n '
def __init__(self, input_dim, output_dim, neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MeanAggregator, self).__init__(**kwargs)
self... |
class HeteMeanAggregator(Layer):
'\n Aggregates via mean followed by matmul and non-linearity.\n '
def __init__(self, input_dim, output_dim, neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MeanAggregator, self).__init__(**kwargs)
... |
class GCNAggregator(Layer):
'\n Aggregates via mean followed by matmul and non-linearity.\n Same matmul parameters are used self vector and neighbor vectors.\n '
def __init__(self, input_dim, output_dim, neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs... |
class MaxPoolingAggregator(Layer):
' Aggregates via max-pooling over MLP functions.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MaxPoolingAggregator, self).__init__(**kwar... |
class MeanPoolingAggregator(Layer):
' Aggregates via mean-pooling over MLP functions.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MeanPoolingAggregator, self).__init__(**k... |
class TwoMaxLayerPoolingAggregator(Layer):
' Aggregates via pooling over two MLP functions.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(TwoMaxLayerPoolingAggregator, self)... |
class SeqAggregator(Layer):
' Aggregates via a standard LSTM.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(SeqAggregator, self).__init__(**kwargs)
self.dropout = dr... |
def uniform(shape, scale=0.05, name=None):
'Uniform init.'
initial = tf.random_uniform(shape, minval=(- scale), maxval=scale, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def glorot(shape, name=None):
'Glorot & Bengio (AISTATS 2010) init.'
init_range = np.sqrt((6.0 / (shape[0] + shape[1])))
initial = tf.random_uniform(shape, minval=(- init_range), maxval=init_range, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def zeros(shape, name=None):
'All zeros.'
initial = tf.zeros(shape, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def ones(shape, name=None):
'All ones.'
initial = tf.ones(shape, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def get_layer_uid(layer_name=''):
'Helper function, assigns unique layer IDs.'
if (layer_name not in _LAYER_UIDS):
_LAYER_UIDS[layer_name] = 1
return 1
else:
_LAYER_UIDS[layer_name] += 1
return _LAYER_UIDS[layer_name]
|
class Layer(object):
'Base layer class. Defines basic API for all layer objects.\n Implementation inspired by keras (http://keras.io).\n # Properties\n name: String, defines the variable scope of the layer.\n logging: Boolean, switches Tensorflow histogram logging on/off\n\n # Methods\n ... |
class Dense(Layer):
'Dense layer.'
def __init__(self, input_dim, output_dim, dropout=0.0, 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.featurele... |
def masked_logit_cross_entropy(preds, labels, mask):
'Logit cross-entropy loss with masking.'
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=preds, labels=labels)
loss = tf.reduce_sum(loss, axis=1)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.maximum(tf.reduce_sum(mask), tf.constant([1... |
def masked_softmax_cross_entropy(preds, labels, mask):
'Softmax cross-entropy loss with masking.'
loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.maximum(tf.reduce_sum(mask), tf.constant([1.0]))
loss *= mask
return t... |
def masked_l2(preds, actuals, mask):
'L2 loss with masking.'
loss = tf.nn.l2(preds, actuals)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.reduce_mean(mask)
loss *= mask
return tf.reduce_mean(loss)
|
def masked_accuracy(preds, labels, mask):
'Accuracy with masking.'
correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(labels, 1))
accuracy_all = tf.cast(correct_prediction, tf.float32)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.reduce_mean(mask)
accuracy_all *= mask
return... |
class UniformNeighborSampler(Layer):
'\n Uniformly samples neighbors.\n Assumes that adj lists are padded with random re-sampling\n '
def __init__(self, adj_info, **kwargs):
super(UniformNeighborSampler, self).__init__(**kwargs)
self.adj_info = adj_info
def _call(self, inputs):
... |
class DistanceNeighborSampler(Layer):
'\n Sampling neighbors based on the feature consistency.\n '
def __init__(self, adj_info, **kwargs):
super(DistanceNeighborSampler, self).__init__(**kwargs)
self.adj_info = adj_info
self.num_neighs = adj_info.shape[(- 1)]
def _call(self... |
class BipartiteEdgePredLayer(Layer):
def __init__(self, input_dim1, input_dim2, placeholders, dropout=False, act=tf.nn.sigmoid, loss_fn='xent', neg_sample_weights=1.0, bias=False, bilinear_weights=False, **kwargs):
'\n Basic class that applies skip-gram-like loss\n (i.e., dot product of nod... |
class SupervisedGraphconsis(models.SampleAndAggregate):
'Implementation of supervised GraphConsis.'
def __init__(self, num_classes, placeholders, features, adj, degrees, layer_infos, concat=True, aggregator_type='mean', model_size='small', sigmoid_loss=False, identity_dim=0, num_re=3, **kwargs):
'\n ... |
def calc_f1(y_true, y_pred):
if (not FLAGS.sigmoid):
y_true = np.argmax(y_true, axis=1)
y_pred = np.argmax(y_pred, axis=1)
else:
y_pred[(y_pred > 0.5)] = 1
y_pred[(y_pred <= 0.5)] = 0
return (metrics.f1_score(y_true, y_pred, average='micro'), metrics.f1_score(y_true, y_pred... |
def calc_auc(y_true, y_pred):
return metrics.roc_auc_score(y_true, y_pred)
|
def evaluate(sess, model, minibatch_iter, size=None):
t_test = time.time()
(feed_dict_val, labels) = minibatch_iter.node_val_feed_dict(size)
node_outs_val = sess.run([model.preds, model.loss], feed_dict=feed_dict_val)
(mic, mac) = calc_f1(labels, node_outs_val[0])
auc = calc_auc(labels, node_outs_... |
def incremental_evaluate(sess, model, minibatch_iter, size, test=False):
t_test = time.time()
finished = False
val_losses = []
val_preds = []
labels = []
iter_num = 0
finished = False
while (not finished):
(feed_dict_val, batch_labels, finished, _) = minibatch_iter.incremental_... |
def construct_placeholders(num_classes):
placeholders = {'labels': tf.placeholder(tf.float32, shape=(None, num_classes), name='labels'), 'batch': tf.placeholder(tf.int32, shape=None, name='batch1'), 'dropout': tf.placeholder_with_default(0.0, shape=(), name='dropout'), 'batch_size': tf.placeholder(tf.int32, name=... |
def train(train_data, test_data=None):
G = train_data[0]
features = train_data[1]
id_map = train_data[2]
class_map = train_data[4]
gs = train_data[5]
num_relations = len(gs)
if isinstance(list(class_map.values())[0], list):
num_classes = len(list(class_map.values())[0])
else:
... |
def main(argv=None):
print('Loading training data..')
file_name = FLAGS.file_name
train_perc = FLAGS.train_perc
relations = ['net_rur', 'net_rtr', 'net_rsr']
train_data = load_data(FLAGS.train_prefix, file_name, relations, train_perc)
print('Done loading training data..')
train(train_data)... |
class MeanAggregator(Layer):
'\n Aggregates via mean followed by matmul and non-linearity.\n '
def __init__(self, input_dim, output_dim, neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MeanAggregator, self).__init__(**kwargs)
self... |
class GCNAggregator(Layer):
'\n Aggregates via mean followed by matmul and non-linearity.\n Same matmul parameters are used self vector and neighbor vectors.\n '
def __init__(self, input_dim, output_dim, neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs... |
class MaxPoolingAggregator(Layer):
' Aggregates via max-pooling over MLP functions.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MaxPoolingAggregator, self).__init__(**kwar... |
class MeanPoolingAggregator(Layer):
' Aggregates via mean-pooling over MLP functions.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(MeanPoolingAggregator, self).__init__(**k... |
class TwoMaxLayerPoolingAggregator(Layer):
' Aggregates via pooling over two MLP functions.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(TwoMaxLayerPoolingAggregator, self)... |
class SeqAggregator(Layer):
' Aggregates via a standard LSTM.\n '
def __init__(self, input_dim, output_dim, model_size='small', neigh_input_dim=None, dropout=0.0, bias=False, act=tf.nn.relu, name=None, concat=False, **kwargs):
super(SeqAggregator, self).__init__(**kwargs)
self.dropout = dr... |
def uniform(shape, scale=0.05, name=None):
'Uniform init.'
initial = tf.random_uniform(shape, minval=(- scale), maxval=scale, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def glorot(shape, name=None):
'Glorot & Bengio (AISTATS 2010) init.'
init_range = np.sqrt((6.0 / (shape[0] + shape[1])))
initial = tf.random_uniform(shape, minval=(- init_range), maxval=init_range, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def zeros(shape, name=None):
'All zeros.'
initial = tf.zeros(shape, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def ones(shape, name=None):
'All ones.'
initial = tf.ones(shape, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def get_layer_uid(layer_name=''):
'Helper function, assigns unique layer IDs.'
if (layer_name not in _LAYER_UIDS):
_LAYER_UIDS[layer_name] = 1
return 1
else:
_LAYER_UIDS[layer_name] += 1
return _LAYER_UIDS[layer_name]
|
class Layer(object):
'Base layer class. Defines basic API for all layer objects.\n Implementation inspired by keras (http://keras.io).\n # Properties\n name: String, defines the variable scope of the layer.\n logging: Boolean, switches Tensorflow histogram logging on/off\n\n # Methods\n ... |
class Dense(Layer):
'Dense layer.'
def __init__(self, input_dim, output_dim, dropout=0.0, 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.featurele... |
def masked_logit_cross_entropy(preds, labels, mask):
'Logit cross-entropy loss with masking.'
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=preds, labels=labels)
loss = tf.reduce_sum(loss, axis=1)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.maximum(tf.reduce_sum(mask), tf.constant([1... |
def masked_softmax_cross_entropy(preds, labels, mask):
'Softmax cross-entropy loss with masking.'
loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.maximum(tf.reduce_sum(mask), tf.constant([1.0]))
loss *= mask
return t... |
def masked_l2(preds, actuals, mask):
'L2 loss with masking.'
loss = tf.nn.l2(preds, actuals)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.reduce_mean(mask)
loss *= mask
return tf.reduce_mean(loss)
|
def masked_accuracy(preds, labels, mask):
'Accuracy with masking.'
correct_prediction = tf.equal(tf.argmax(preds, 1), tf.argmax(labels, 1))
accuracy_all = tf.cast(correct_prediction, tf.float32)
mask = tf.cast(mask, dtype=tf.float32)
mask /= tf.reduce_mean(mask)
accuracy_all *= mask
return... |
class UniformNeighborSampler(Layer):
'\n Uniformly samples neighbors.\n Assumes that adj lists are padded with random re-sampling\n '
def __init__(self, adj_info, **kwargs):
super(UniformNeighborSampler, self).__init__(**kwargs)
self.adj_info = adj_info
def _call(self, inputs):
... |
class BipartiteEdgePredLayer(Layer):
def __init__(self, input_dim1, input_dim2, placeholders, dropout=False, act=tf.nn.sigmoid, loss_fn='xent', neg_sample_weights=1.0, bias=False, bilinear_weights=False, **kwargs):
'\n Basic class that applies skip-gram-like loss\n (i.e., dot product of nod... |
class SupervisedGraphsage(models.SampleAndAggregate):
'Implementation of supervised GraphSAGE.'
def __init__(self, num_classes, placeholders, features, adj, degrees, layer_infos, concat=True, aggregator_type='mean', model_size='small', sigmoid_loss=False, identity_dim=0, **kwargs):
'\n Args:\n... |
def calc_f1(y_true, y_pred):
if (not FLAGS.sigmoid):
y_true = np.argmax(y_true, axis=1)
y_pred = np.argmax(y_pred, axis=1)
else:
y_pred[(y_pred > 0.5)] = 1
y_pred[(y_pred <= 0.5)] = 0
return (metrics.f1_score(y_true, y_pred, average='micro'), metrics.f1_score(y_true, y_pred... |
def evaluate(sess, model, minibatch_iter, size=None):
t_test = time.time()
(feed_dict_val, labels) = minibatch_iter.node_val_feed_dict(size)
node_outs_val = sess.run([model.preds, model.loss], feed_dict=feed_dict_val)
(mic, mac) = calc_f1(labels, node_outs_val[0])
return (node_outs_val[1], mic, ma... |
def log_dir():
log_dir = ((FLAGS.base_log_dir + '/sup-') + FLAGS.train_prefix.split('/')[(- 2)])
log_dir += '/{model:s}_{model_size:s}_{lr:0.4f}/'.format(model=FLAGS.model, model_size=FLAGS.model_size, lr=FLAGS.learning_rate)
if (not os.path.exists(log_dir)):
os.makedirs(log_dir)
return log_di... |
def incremental_evaluate(sess, model, minibatch_iter, size, test=False):
t_test = time.time()
finished = False
val_losses = []
val_preds = []
labels = []
iter_num = 0
finished = False
while (not finished):
(feed_dict_val, batch_labels, finished, _) = minibatch_iter.incremental_... |
def construct_placeholders(num_classes):
placeholders = {'labels': tf.placeholder(tf.float32, shape=(None, num_classes), name='labels'), 'batch': tf.placeholder(tf.int32, shape=None, name='batch1'), 'dropout': tf.placeholder_with_default(0.0, shape=(), name='dropout'), 'batch_size': tf.placeholder(tf.int32, name=... |
def train(train_data, test_data=None):
G = train_data[0]
features = train_data[1]
id_map = train_data[2]
class_map = train_data[4]
if isinstance(list(class_map.values())[0], list):
num_classes = len(list(class_map.values())[0])
else:
num_classes = len(set(class_map.values()))
... |
def main(argv=None):
print('Loading training data..')
train_data = load_data(FLAGS.train_prefix)
print('Done loading training data..')
train(train_data)
|
def calc_f1(y_true, y_pred):
y_true = np.argmax(y_true, axis=1)
y_pred = np.argmax(y_pred, axis=1)
return (metrics.f1_score(y_true, y_pred, average='micro'), metrics.f1_score(y_true, y_pred, average='macro'))
|
def cal_acc(y_true, y_pred):
y_true = np.argmax(y_true, axis=1)
y_pred = np.argmax(y_pred, axis=1)
return metrics.accuracy_score(y_true, y_pred)
|
class Model(object):
def __init__(self, data_config, pretrain_data, args):
self.model_type = 'hacud'
self.adj_type = args.adj_type
self.early_stop = args.early_stop
self.pretrain_data = pretrain_data
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
self.n_nodes = data... |
def parse_args():
parser = argparse.ArgumentParser(description='Run HACUD.')
parser.add_argument('--weights_path', nargs='?', default='', help='Store model path.')
parser.add_argument('--data_path', nargs='?', default='../Data/', help='Input data path.')
parser.add_argument('--proj_path', nargs='?', d... |
class Player2Vec(Algorithm):
def __init__(self, session, meta, nodes, class_size, gcn_output1, embedding, encoding):
self.meta = meta
self.nodes = nodes
self.class_size = class_size
self.gcn_output1 = gcn_output1
self.embedding = embedding
self.encoding = encoding
... |
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=123, help='Random seed.')
parser.add_argument('--dataset_str', type=str, default='dblp', help="['dblp','example']")
parser.add_argument('--epoch_num', type=int, default=30, help='Number of epochs to tr... |
def set_env(args):
tf.reset_default_graph()
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
|
def get_data(ix, int_batch, train_size):
if ((ix + int_batch) >= train_size):
ix = (train_size - int_batch)
end = train_size
else:
end = (ix + int_batch)
return (train_data[ix:end], train_label[ix:end])
|
def load_data(args):
if (args.dataset_str == 'dblp'):
(adj_list, features, train_data, train_label, test_data, test_label) = load_data_dblp()
node_size = features.shape[0]
node_embedding = features.shape[1]
class_size = train_label.shape[1]
train_size = len(train_data)
... |
def train(args, adj_list, features, train_data, train_label, test_data, test_label, paras):
with tf.Session() as sess:
adj_data = [normalize_adj(adj) for adj in adj_list]
meta_size = len(adj_list)
net = Player2Vec(session=sess, class_size=paras[2], gcn_output1=args.hidden1, meta=meta_size,... |
class SemiGNN(Algorithm):
def __init__(self, session, nodes, class_size, semi_encoding1, semi_encoding2, semi_encoding3, init_emb_size, meta, ul, alpha, lamtha):
self.nodes = nodes
self.meta = meta
self.class_size = class_size
self.semi_encoding1 = semi_encoding1
self.semi... |
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=123, help='Random seed.')
parser.add_argument('--dataset_str', type=str, default='example', help="['dblp','example']")
parser.add_argument('--epoch_num', type=int, default=30, help='Number of epochs to... |
def set_env(args):
tf.reset_default_graph()
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
|
def get_data(ix, int_batch, train_size):
if ((ix + int_batch) >= train_size):
ix = (train_size - int_batch)
end = train_size
else:
end = (ix + int_batch)
return (train_data[ix:end], train_label[ix:end])
|
def load_data(args):
if (args.dataset_str == 'example'):
(adj_list, features, train_data, train_label, test_data, test_label) = load_example_semi()
node_size = features.shape[0]
node_embedding = features.shape[1]
class_size = train_label.shape[1]
train_size = len(train_data... |
def train(args, adj_list, features, train_data, train_label, test_data, test_label, paras):
with tf.Session() as sess:
adj_nodelists = [matrix_to_adjlist(adj, pad=False) for adj in adj_list]
meta_size = len(adj_list)
pairs = [random_walks(adj_nodelists[i], 2, 3) for i in range(meta_size)]
... |
class Algorithm(object):
def __init__(self, **kwargs):
self.nodes = None
def forward_propagation(self):
pass
def save(self, sess=None):
if (not sess):
raise AttributeError('TensorFlow session not provided.')
saver = tf.train.Saver()
save_path = saver.... |
def uniform(shape, scale=0.05, name=None):
'Uniform init.'
initial = tf.random_uniform(shape, minval=(- scale), maxval=scale, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def glorot(shape, name=None):
'Glorot & Bengio (AISTATS 2010) init.'
init_range = np.sqrt((6.0 / (shape[0] + shape[1])))
initial = tf.random_uniform(shape, minval=(- init_range), maxval=init_range, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def zeros(shape, name=None):
'All zeros.'
initial = tf.zeros(shape, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def ones(shape, name=None):
'All ones.'
initial = tf.ones(shape, dtype=tf.float32)
return tf.Variable(initial, name=name)
|
def get_layer_uid(layer_name=''):
'Helper function, assigns unique layer IDs.'
if (layer_name not in _LAYER_UIDS):
_LAYER_UIDS[layer_name] = 1
return 1
else:
_LAYER_UIDS[layer_name] += 1
return _LAYER_UIDS[layer_name]
|
def sparse_dropout(x, keep_prob, noise_shape):
'Dropout for sparse tensors.'
random_tensor = keep_prob
random_tensor += tf.random_uniform(noise_shape)
dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
pre_out = tf.sparse_retain(x, dropout_mask)
return (pre_out * (1.0 / keep_prob))... |
def dot(x, y, sparse=False):
'Wrapper for tf.matmul (sparse vs dense).'
if sparse:
res = tf.sparse_tensor_dense_matmul(x, y)
else:
res = tf.matmul(x, y)
return res
|
class Layer(object):
'Base layer class. Defines basic API for all layer objects.\n Implementation inspired by keras (http://keras.io).\n\n # Properties\n name: String, defines the variable scope of the layer.\n logging: Boolean, switches Tensorflow histogram logging on/off\n\n # Methods\n ... |
class GraphConvolution(Layer):
'Graph convolution layer.'
def __init__(self, input_dim, output_dim, placeholders, index=0, dropout=0.0, sparse_inputs=False, act=tf.nn.relu, bias=False, featureless=False, norm=False, **kwargs):
super(GraphConvolution, self).__init__(**kwargs)
self.dropout = dr... |
class AttentionLayer(Layer):
' AttentionLayer is a function f : hkey × Hval → hval which maps\n a feature vector hkey and the set of candidates’ feature vectors\n Hval to an weighted sum of elements in Hval.\n '
def attention(inputs, attention_size, v_type=None, return_weights=False, bias=True, join... |
class ConcatenationAggregator(Layer):
"This layer equals to the equation (3) in\n paper 'Spam Review Detection with Graph Convolutional Networks.'\n "
def __init__(self, input_dim, output_dim, review_item_adj, review_user_adj, review_vecs, user_vecs, item_vecs, dropout=0.0, act=tf.nn.relu, name=None, c... |
class AttentionAggregator(Layer):
"This layer equals to equation (5) and equation (8) in\n paper 'Spam Review Detection with Graph Convolutional Networks.'\n "
def __init__(self, input_dim1, input_dim2, output_dim, hid_dim, user_review_adj, user_item_adj, item_review_adj, item_user_adj, review_vecs, us... |
class GASConcatenation(Layer):
'GCN-based Anti-Spam(GAS) layer for concatenation of comment embedding learned by GCN from the Comment Graph\n and other embeddings learned in previous operations.\n '
def __init__(self, review_item_adj, review_user_adj, review_vecs, item_vecs, user_vecs, homo_vecs, nam... |
class GEMLayer(Layer):
"This layer equals to the equation (8) in\n paper 'Heterogeneous Graph Neural Networks for Malicious Account Detection.'\n "
def __init__(self, placeholders, nodes, device_num, embedding, encoding, name=None, **kwargs):
super(GEMLayer, self).__init__(**kwargs)
sel... |
class GAT(Layer):
"This layer is adapted from PetarV-/GAT.'\n "
def __init__(self, dim, attn_drop, ffd_drop, bias_mat, n_heads, name=None, **kwargs):
super(GAT, self).__init__(**kwargs)
self.dim = dim
self.attn_drop = attn_drop
self.ffd_drop = ffd_drop
self.bias_mat... |
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