def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.gcn0 = GCN(16,
activation=tf.nn.relu,
kernel_regularizer=L1L2(l2=l2_coef))
self.gcn1 = GCN(num_classes, kernel_regularizer=L1L2(l2=l2_coef))
self.dropout = tf.keras.layers.Dropout(drop_rate)
def __init__(self, num_clusters_list, num_features_list, num_classes,
*args, **kwargs):
super().__init__(*args, **kwargs)
self.diff_pools = []
for num_features, num_clusters in zip(num_features_list,
num_clusters_list):
diff_pool = DiffPool(feature_gnn=GCN(num_features),
assign_gnn=GCN(num_clusters),
units=num_features,
num_clusters=num_clusters,
activation=tf.nn.relu)
self.diff_pools.append(diff_pool)
self.mlp = tf.keras.Sequential(
[tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(num_classes)])
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.gcns = []
self.sag_pools = []
for _ in range(3):
self.gcns.append(GCN(128, activation=tf.nn.relu))
self.sag_pools.append(
SAGPool(score_gnn=GCN(1),
ratio=0.5,
score_activation=tf.nn.tanh))
self.mlp = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dense(num_classes)
])
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
activations = [
tf.nn.relu if i < len(units_list) - 1 else None
for i in range(len(units_list))
]
self.gcns = [
GCN(units=units, activation=activation)
for units, activation in zip(units_list, activations)
]
self.dropout = Dropout(drop_rate)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.gcns = []
self.asaps = []
for _ in range(3):
self.gcns.append(GCN(64, activation=tf.nn.relu))
self.asaps.append(ASAP(ratio=0.5, drop_rate=0.1))
self.mlp = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(num_classes)
])
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.gcns = [GCN(128, activation=tf.nn.relu) for _ in range(3)]
self.k = 30
self.sort_pool = SortPool(k=self.k)
self.conv1d = tf.keras.layers.Conv1D(128, 5, activation=tf.nn.relu)
self.flatten = tf.keras.layers.Flatten()
self.mlp = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.5),
# tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dense(num_classes)
])
# coding=utf-8
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
from tensorflow import keras
from tf_geometric.datasets.cora import CoraDataset
from tf_geometric.layers import GCN
graph, (train_index, valid_index, test_index) = CoraDataset().load_data()
num_classes = graph.y.shape[-1]
gcn0 = GCN(32, activation=tf.nn.relu)
gcn1 = GCN(num_classes)
def forward(graph):
h = gcn0([graph.x, graph.edge_index, graph.edge_weight], cache=graph.cache)
h = gcn1([h, graph.edge_index, graph.edge_weight], cache=graph.cache)
return h
def compute_losses(logits, mask_index):
masked_logits = tf.gather(logits, mask_index)
masked_labels = tf.gather(graph.y, mask_index)
losses = tf.nn.softmax_cross_entropy_with_logits(logits=masked_logits,
labels=masked_labels)
return losses
def evaluate():