Add next tutorial .pyHEADmaster

1 files changed, 69 insertions, 0 deletions

diff --git a/other_exp/gcn_prediction.py b/other_exp/gcn_prediction.py
new file mode 100644
index 000000000..86086f54a
--- /dev/null
+++ b/other_exp/gcn_prediction.py
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+import tensorflow as tf
+from tensorflow.compat.v1 import placeholder
+from tensorflow.compat.v1.layers import dense
+
+tf.compat.v1.disable_eager_execution()
+
+n_nodes = 50
+n_features = 50
+X = placeholder(tf.float64, [None, n_nodes, n_features])
+A = placeholder(tf.float64, [None, n_nodes, n_nodes])
+Y_truth = placeholder(tf.float64, [None, ])
+
+
+def graph_conv(_X, _A, O):
+    out = dense(_X, units=O, use_bias=True)
+    out = tf.matmul(_A, out)
+    out = tf.nn.relu(out)
+
+    return out
+
+
+def readout_nw(_X, O):
+    """
+    Node-wise summation implementation.
+    _X: final node embeddings.
+    O: desired output dimension.
+    """
+    out = dense(_X, O, use_bias=True)
+    out = tf.reduce_sum(out, axis=1)
+    out = tf.nn.relu(out)
+
+    return out
+
+
+def readout_gg(_X, X, O):
+    """
+    Graph Gathering implementation. (The none shown in the equation)
+    _X: final node embeddings.
+    X: initial node features.
+    O: desired output dimension.
+    """
+    val1 = dense(tf.concat([_X, X], axis=2), O, use_bias=True)
+    val1 = tf.nn.sigmoid(val1)
+    val2 = dense(_X, O, use_bias=True)
+
+    out = tf.multiply(val1, val2)
+    out = tf.reduce_sum(out, axis=1)
+    out = tf.nn.relu(out)
+
+    return out
+
+
+gconv1 = graph_conv(X, A, 32)
+gconv2 = graph_conv(gconv1, A, 32)
+gconv3 = graph_conv(gconv2, A, 32)
+
+graph_feature = readout_gg(gconv3, gconv1, 128)
+print("\t\tNode-wise summation implementation result.")
+print(graph_feature)
+
+Y_pred = dense(graph_feature, 128, use_bias=True, activation=tf.nn.relu)
+Y_pred = dense(Y_pred, 128, use_bias=True, activation=tf.nn.tanh)
+Y_pred = dense(Y_pred, 1, use_bias=True, activation=None)
+print(Y_pred)
+
+Y_pred = tf.reshape(Y_pred, shape=[-1])
+Y_truth = tf.reshape(Y_truth, shape=[-1])
+loss = tf.reduce_mean(tf.pow(Y_truth - Y_pred, 2))
+print(loss)