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author | David Luevano Alvarado <55825613+luevano@users.noreply.github.com> | 2020-11-20 22:52:57 -0700 |
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committer | David Luevano Alvarado <55825613+luevano@users.noreply.github.com> | 2020-11-20 22:52:57 -0700 |
commit | 26936e03746f13d3282cb747705f3dd23da1c6fd (patch) | |
tree | eb47185312cd6a536072bc1d20e45157f414f369 | |
parent | 65ccf902c890f09073147aa718897447f5e5ab5b (diff) |
-rw-r--r-- | other_exp/gcn_prediction.py | 69 |
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 @@ -0,0 +1,69 @@ +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) |