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import tensorflow as tf
class SAGE(tf.keras.Model):
"""
SAGE layer class.
"""
def __init__(self, args, number_of_features):
"""
Creating a SAGE layer.
:param args: Arguments object.
:param number_of_features: Number of node features.
"""
super(SAGE, self).__init__()
self.args = args
self.number_of_features = number_of_features
self._setup()
def _setup(self):
"""
Setting up upstream and pooling layers.
"""
self.graph_convolution_1 = tf.keras.layers.Dense(units=self.args.first_gcn_dimensions,
activation='relu',
input_shape=(self.number_of_features,))
self.graph_convolution_2 = tf.keras.layers.Dense(units=self.args.second_gcn_dimensions,
activation='relu')
self.fully_connected_1 = tf.keras.layers.Dense(units=self.args.first_dense_neurons,
activation='tanh')
self.fully_connected_2 = tf.keras.layers.Dense(units=self.args.second_dense_neurons,
activation='softmax')
def call(self, data):
"""
Making a forward pass with the graph level data.
:param data: Data feed dictionary.
:return graph_embedding: Graph level embedding.
:return penalty: Regularization loss.
"""
edges = tf.convert_to_tensor(data["edge"])
features = tf.convert_to_tensor(data["features"])
node_features_1 = tf.nn.relu(self.graph_convolution_1(features))
node_features_2 = tf.nn.relu(self.graph_convolution_2(node_features_1))
abstract_features_1 = tf.math.tanh(self.fully_connected_1(node_features_2))
attention = tf.nn.softmax(self.fully_connected_2(abstract_features_1), axis=0)
attention = tf.transpose(attention)
graph_embedding = tf.matmul(tf.transpose(attention), node_features_2)
graph_embedding = tf.reshape(graph_embedding, [1, -1])
penalty = tf.matmul(tf.transpose(attention), attention) - tf.eye(self.args.second_dense_neurons)
penalty = tf.norm(penalty, ord=2)
return graph_embedding, penalty
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