TypeError：由于 tensor_scatter_update 而预期单个张量时的张量列表

Question

看看下面的代码示例：

def myFun(my_tensor):
        #The following line works
        my_tensor= tf.tensor_scatter_update(my_tensor, tf.constant([[0]]), tf.constant([1]))
        #The following line leads to error
        p = tf.cond(tf.math.equal(0, 0), lambda: 1, lambda: 1)
        my_tensor= tf.tensor_scatter_update(my_tensor, tf.constant([[p]]), tf.constant([1]))

我用一个简单的案例来描述我面临的问题 此函数（myFun）被称为 tf.while_loop 的主体（如果相关） my_tensor的定义

my_tensor = tf.zeros(5, tf.int32)

如何定义 tf.tensor_scatter_update 的 indices 参数？ 我正在使用 tensorflow1.15

Answer 1

您不能使用张量 p 作为 tf.constant 的参数。也许尝试这样的事情：

%tensorflow_version 1.x
import tensorflow as tf

def myFun(my_tensor):

    my_tensor= tf.tensor_scatter_update(my_tensor, tf.constant([[0]]), tf.constant([1]))
    p = tf.cond(tf.math.equal(0, 0), lambda: 1, lambda: 1)
    new_tensor= tf.tensor_scatter_update(my_tensor, [[p]], tf.constant([1]))

    with tf.Session() as sess:
      p_value = p.eval()
      tensor_values = my_tensor.eval()
      new_tensor_values = new_tensor.eval()

    print('p -->', p_value)
    print('my_tensor -->', tensor_values)
    print('new_tensor -->', new_tensor_values)

my_tensor = tf.zeros(5, tf.int32)
myFun(my_tensor)

p --> 1
my_tensor --> [1 0 0 0 0]
new_tensor --> [1 1 0 0 0]

您也可以将p 包裹在tf.Variable 周围：

def myFun(my_tensor):

    my_tensor= tf.tensor_scatter_update(my_tensor, tf.constant([[0]]), tf.constant([1]))
    p = tf.cond(tf.math.equal(0, 0), lambda: 1, lambda: 1)

    indices = tf.Variable([[p]])       
    new_tensor= tf.tensor_scatter_update(my_tensor, indices, tf.constant([1]))

    with tf.Session() as sess:
      sess.run(indices.initializer)
      p_value = p.eval()
      tensor_values = my_tensor.eval()
      new_tensor_values = new_tensor.eval()