Question

我的张量如下：

arr = [[1.5,0.2],[2.3,0.1],[1.3,0.21],[2.2,0.09],[4.4,0.8]]

我想收集小的数组，它们的第一元素之差在0.3以内，第二元素之差在0.03以内。例如，[1.5,0.2]和[1.3,0.21]应该属于同一类别。它们的第一元素之差为0.2 <0.3，第二元素之差为0.01 <0.03。

我希望张量看起来像这样

arr = {[[1.5,0.2],[1.3,0.21]],[[2.3,0.1],[2.2,0.09]]}

如何在张量流中做到这一点？急切模式还可以。

我发现了一种难看且缓慢的方法：

samples = np.array([[1.5,0.2],[2.3,0.1],[1.3,0.2],[2.2,0.09],[4.4,0.8],[2.3,0.11]],dtype=np.float32)
ini_samples = samples
samples = tf.split(samples,2,1)

a = samples[0]
b = samples[1]

find_match1 = tf.reduce_sum(tf.abs(tf.expand_dims(a,0) - tf.expand_dims(a,1)),2)
a = tf.logical_and(tf.greater(find_match1, tf.zeros_like(find_match1)),tf.less(find_match1, 0.3*tf.ones_like(find_match1)))

find_match2 = tf.reduce_sum(tf.abs(tf.expand_dims(b,0) - tf.expand_dims(b,1)),2)
b = tf.logical_and(tf.greater(find_match2, tf.zeros_like(find_match2)),tf.less(find_match2, 0.03*tf.ones_like(find_match2)))
x,y = tf.unique(tf.reshape(tf.where(tf.logical_or(a,b)),[1,-1])[0])

r = tf.gather(ini_samples, x)

tensorflow是否具有更优雅的功能？

Answer 1

您无法获得由大小不同的向量“组”组成的结果。相反，您可以制作一个“ group id”张量，根据您的标准将每个向量分为一组。使这一点更加复杂的部分是，您必须将具有共同元素的组“融合”，我认为这只能通过循环来完成。这段代码是这样的：

import tensorflow as tf

def make_groups(correspondences):
    # Multiply each row by its index
    m = tf.to_int32(correspondences) * tf.range(tf.shape(correspondences)[0])
    # Pick the largest index for each row
    r = tf.reduce_max(m, axis=1)
    # While loop accounts for transitive correspondences
    # (e.g. if A and B go toghether and B and C go together, then A, B and C go together)
    # The loop makes sure every element gets the largest common group id
    r_prev = -tf.ones_like(r)
    r, _ = tf.while_loop(lambda r, r_prev: tf.reduce_any(tf.not_equal(r, r_prev)),
                         lambda r, r_prev: (tf.gather(r, r), tf.identity(r)),
                         [r, r_prev])
    # Use unique indices to make sequential group ids starting from 0
    return tf.unique(r)[1]

# Test
with tf.Graph().as_default(), tf.Session() as sess:
    arr = tf.constant([[1.5 , 0.2 ],
                       [2.3 , 0.1 ],
                       [1.3 , 0.21],
                       [2.2 , 0.09],
                       [4.4 , 0.8 ],
                       [1.1 , 0.23]])
    a = arr[:, 0]
    b = arr[:, 0]
    cond = (tf.abs(a - a[:, tf.newaxis]) < 0.3) | (tf.abs(b - b[:, tf.newaxis]) < 0.03)
    groups = make_groups(cond)
    print(sess.run(groups))
    # [0 1 0 1 2 0]

因此，在这种情况下，组将是：

[1.5, 0.2]，[1.3, 0.21]和[1.1, 0.23]
[2.3, 0.1]和[2.2, 0.09]
[4.4, 0.8]

tensorflow从列表中收集相似的值

1 个答案: