计算 TensorFlow 中每个张量行的平均值

Question

我是 tensorflow 的新手，我想从张量中计算每一行的平均值。为了做到这一点，Tensorflow 有 tf.reduce_mean 操作。问题是，当一行具有 nan 值时，该行的平均值也是 nan。除此之外，我想自己实现它，以便更好地理解 tensorflow 的哲学。那么我该如何手动实现呢？我写的代码：

import tensorflow as tf
import numpy as np

ratings = np.array([[7, 6, 7, 4, 5, 4], [6, 7, np.NaN, 4, 3, 4], [np.NaN, 3, 3, 1, 1, np.NaN],
                   [1, 2, 2, 3, 3, 4], [1, np.NaN, 1, 2, 3, 3]], dtype = np.float16)

tRatings = tf.convert_to_tensor(ratings, dtype = np.float16)

means = tf.get_variable("means", shape=(5), dtype=tf.float16)


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    mean = tf.reduce_mean(tRatings, axis=1)
    print(sess.run(mean))

Answer 1

import tensorflow as tf
import numpy as np
ratings = np.array([[7, 6, 7, 4, 5, 4], [6, 7, np.NaN, 4, 3, 4], [np.NaN, 3, 3, 1, 1, np.NaN],
                       [1, 2, 2, 3, 3, 4], [1, np.NaN, 1, 2, 3, 3]], dtype = np.float16)

tRatings = tf.convert_to_tensor(ratings, dtype = np.float16)
means = tf.get_variable("means", shape=(5), dtype=tf.float16)
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  #mean = tf.reduce_mean(tRatings, axis=1)
  tRatings_wonan=tf.where(tf.is_nan(tRatings), tf.zeros_like(tRatings), tRatings)
  sum = tf.reduce_sum(tRatings_wonan,axis=1)
  count_nans = tf.reduce_sum(tf.cast(tf.is_nan(tRatings), tf.float16),axis=1)
  mean = tf.div(sum,tf.subtract(tf.cast(tf.shape(tRatings)[1], tf.float16),count_nans))
  print(sess.run(mean))