def test_SparseSegmentMean(self):
t = tf.sparse_segment_mean(self.random(4, 3, 2), [0, 2, 3], [0, 1, 1])
self.check(t)
"""详细计算方法参见tf_sparse_segment_sum""" import tensorflow as tf """tf.sparse_segment_mean(data, indices, segment_ids, name=None) 功能：tensor进行拆分后求平均值。和segment_mean类似，只是segment_ids的rank数可以小于‘data’第0维度数。 输入：indices:选择第0维度参与运算的编号。""" a = tf.constant([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], tf.float32) z = tf.sparse_segment_mean(a, tf.constant([0, 1]), tf.constant([0, 0])) z2 = tf.sparse_segment_mean(a, tf.constant([0, 1]), tf.constant([0, 1])) z3 = tf.sparse_segment_mean(a, tf.constant([0, 2]), tf.constant([0, 1])) z4 = tf.sparse_segment_mean(a, tf.constant([0, 1, 2]), tf.constant([0, 0, 1])) sess = tf.Session() print(sess.run(z)) print(sess.run(z2)) print(sess.run(z3)) print(sess.run(z4)) sess.close() # z==>[[3. 4. 5. 6.]] # z2==>[[1. 2. 3. 4.] # [5. 6. 7. 8.]] # z3==>[[1. 2. 3. 4.] # [9. 10. 11. 12.]] # z4==>[[3. 4. 5. 6.] # [9. 10. 11. 12.]]
def test_SparseSegmentMean(self):
t = tf.sparse_segment_mean(self.random(4, 3, 2), [0, 2, 3], [0, 1, 1])
self.check(t)
x = np.random.rand(10, 4, 3)
segment_ids = [5, 0, 0, 2, 1, 2, 3, 4, 0, 5]
num_segments = np.max(segment_ids) + 1
z_unsorted_segment_sum = tf.unsorted_segment_sum(x, segment_ids, num_segments)
# tf.sparse_segment_sum
x = np.random.rand(10, 4, 3)
indices = [0, 2, 3, 4]
segment_ids = [0, 0, 1, 1]
z_sparse_segment_sum = tf.sparse_segment_sum(x, indices, segment_ids)
# tf.sparse_segment_mean
x = np.random.rand(10, 4, 3)
indices = [0, 2, 3, 4]
segment_ids = [0, 1, 1, 1]
z_sparse_segment_mean = tf.sparse_segment_mean(x, indices, segment_ids)
with tf.Session() as sess:
print "tf.segment_sum"
print sess.run(z_segment_sum)
print "tf.segment_prod"
print sess.run(z_segment_prod)
print "tf.segment_min"
print sess.run(z_segment_min)
print "tf.segment_max"
print sess.run(z_segment_max)
print "tf.segment_mean"