通过在Tensorflow中将固定数量的行相加来减少矩阵

你可以重塑你的张量,以便在一个新的维度上隔离三胞胎,然后 tf.reduce_sum 在该维度上:

import tensorflow as tf

x = tf.constant([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
shape_x = tf.shape(x)

# Reshaping the tensor to have triplets on dimension #1:
new_shape_for_reduce = tf.stack([shape_x[0] // 3, 3, shape_x[1]])
reshaped_x = tf.reshape(x, new_shape_for_reduce)
# Sum-reducing over dimension #1:
sum_3_rows = tf.reduce_sum(reshaped_x, axis=1)

with tf.Session() as sess:
    res = sess.run(sum_3_rows)
    print(res)
    # [[9  12]
    #  [27 30]]