First exploration, following tutorial

1 files changed, 49 insertions, 0 deletions

diff --git a/other_exp/gcn_classification.py b/other_exp/gcn_classification.py
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+++ b/other_exp/gcn_classification.py
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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__)