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bxshi/Hits_at_K_TensorFlow.py

Created January 12, 2017 21:34
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  1. bxshi created this gist Jan 12, 2017.
    52 changes: 52 additions & 0 deletions Hits_at_K_TensorFlow.py
    View file
    Original file line number Diff line number Diff line change
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    import tensorflow as tf

    with tf.Session() as sess:
    """
    idx (h,r) top_3
    [ [
    [0,1], [0,8,3],
    [1,3], [7,2,1],
    [2,4], [4,3,9],
    ] ]
    triples (h,r,t)
    [
    [0,1,2],
    [0,1,5],
    [1,3,2],
    [1,2,7],
    [2,4,3],
    [2,4,9],
    [2,4,4]
    ]
    """
    idx = tf.Variable([[0, 1], [1, 3], [2, 4]], trainable=False, dtype=tf.int32)
    top_3 = tf.Variable([[0, 8, 3], [7, 2, 1], [4, 3, 9]], trainable=False, dtype=tf.int32)
    triples = tf.Variable([[0, 1, 2], [0, 1, 5], [1, 3, 2], [1, 2, 7], [2, 4, 3], [2, 4, 9], [2, 4, 4]],
    trainable=False,
    dtype=tf.int32)


    def hits_func(acc, item):
    hr = item[0]
    top = item[1]

    mask = tf.logical_and(tf.equal(hr[0], triples[:, 0]), tf.equal(hr[1], triples[:, 1]))

    t = tf.boolean_mask(triples[:, 2], mask)

    def in_op(acc, it):
    return tf.reduce_any(tf.equal(t, it))

    hits = tf.scan(in_op, top, initializer=tf.Variable(initial_value=False, dtype=tf.bool, trainable=False))

    return tf.reduce_mean(tf.cast(hits, dtype=tf.float32))


    hits_list = tf.scan(hits_func, (idx, top_3),
    initializer=tf.Variable(initial_value=0., dtype=tf.float32, trainable=False))

    tf.global_variables_initializer().run()

    print("Hits@3 is", sess.run(tf.reduce_mean(hits_list)))