Created
March 8, 2018 14:52
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Message passing tensorflow
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import tensorflow as tf | |
def message_passing(nodes, edges, edge_features, message_fn, edge_keep_prob=1.0): | |
""" | |
Pass messages between nodes and sum the incoming messages at each node. | |
Implements equation 1 and 2 in the paper, i.e. m_{.j}^t &= \sum_{i \in N(j)} f(h_i^{t-1}, h_j^{t-1}) | |
:param nodes: (n_nodes, n_features) tensor of node hidden states. | |
:param edges: (n_edges, 2) tensor of indices (i, j) indicating an edge from nodes[i] to nodes[j]. | |
:param edge_features: features for each edge. Set to zero if the edges don't have features. | |
:param message_fn: message function, will be called with input of shape (n_edges, 2*n_features + edge_features). The output shape is (n_edges, n_outputs), where you decide the size of n_outputs | |
:param edge_keep_prob: The probability by which edges are kept. Basically dropout for edges. Not used in the paper. | |
:return: (n_nodes, n_output) Sum of messages arriving at each node. | |
""" | |
n_nodes = tf.shape(nodes)[0] | |
n_features = nodes.get_shape()[1].value | |
n_edges = tf.shape(edges)[0] | |
message_inputs = tf.gather(nodes, edges) # n_edges, 2, n_features | |
reshaped = tf.concat([tf.reshape(message_inputs, (-1, 2 * n_features)), edge_features], 1) | |
messages = message_fn(reshaped) # n_edges, n_output | |
messages = tf.nn.dropout(messages, edge_keep_prob, noise_shape=(n_edges, 1)) | |
updates = tf.unsorted_segment_sum(messages, edges[:, 1], n_nodes) | |
return updates |
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