Question

当我为某些数据进行批量标记时，我有一个用于记录所有计算结果的变量：

p_all = tf.Variable(tf.zeros([batch_num, batch_size]), name = "probability");

在计算中，我有一个循环来处理每个批次：

for i in range(batch_num):
    feed = {x: testDS.test.next_batch(batch_size)}
    sess.run(p_each_batch, feed_dict=feed)

如何将p_each_bach的值复制到p_all？

为了更清楚，我想要像：

... ...
p_all[batch_index,:] = p_each_batch
for i in range(batch_num):
    feed = {x: testDS.test.next_batch(batch_size), batch_index: i}
    sess.run(p_all, feed_dict=feed)

如何让这些代码真正起作用？

Answer 1

由于p_all是tf.Variable，您可以使用tf.scatter_update()操作来更新每个批次中的各个行：

# Equivalent to `p_all[batch_index, :] = p_each_batch`
update_op = tf.scatter_update(p_all,
                              tf.expand_dims(batch_index, 0),
                              tf.expand_dims(p_each_batch, 0)) 

for i in range(batch_num):
    feed = {x: testDS.test.next_batch(batch_size), batch_index: i}
    sess.run(update_op, feed_dict=feed)

TensorFlow中的索引操作

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