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import tensorflow as tf
from tensorflow.compat.v1 import placeholder
from tensorflow.compat.v1.layers import dense
# Disable eager execution (evaluate tf operations instantly instead of having
# to build a graph) so placeholder() can work.
tf.compat.v1.disable_eager_execution()
n_nodes = 50
n_features = 50
n_labels = 10
X = placeholder(tf.float64, shape=(None, n_nodes, n_features))
A = placeholder(tf.float64, shape=(None, n_nodes, n_nodes))
Y_truth = placeholder(tf.float64, shape=(None, n_labels))
# Function for implementation of H⁽l+1)=sigma(A(AH^lW^l)+ b^l).
# With the bias term given by the tf dense layer.
def graph_conv(_X, _A, O):
"""
Equation of graph convolution.
_X: vector X. Nodes.
_A: adjacency matrix. Edges or path.
"""
out = dense(_X, units=O, use_bias=True)
out = tf.matmul(_A, out)
out = tf.nn.relu(out)
return out
X_new = graph_conv(X, A, 32)
print(X_new)
gconv1 = graph_conv(X, A, 32)
gconv2 = graph_conv(gconv1, A, 32)
gconv3 = graph_conv(gconv2, A, 32)
Y_pred = tf.nn.softmax(dense(gconv3, units=n_labels, use_bias=True), axis=2)
print(Y_pred)
Y_pred = tf.reshape(Y_pred, [-1])
loss = tf.reduce_mean(Y_truth*tf.math.log(Y_pred + 1.0 ** -5))
print(loss)
print(tf.__version__)
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