Python 如何以张量为范围运行循环？（在张量流中）
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How can I run a loop with a tensor as its range? (in tensorflow)
提问by Poorya Pzm
I want to have a for loop that the number of its iterations is depend on a tensor value. For example:
我想要一个 for 循环，它的迭代次数取决于张量值。例如：
for i in tf.range(input_placeholder[1,1]):
# do something
However I get the following error:
但是我收到以下错误：
"TypeError: 'Tensor' object is not iterable"
“类型错误：‘张量’对象不可迭代”
What should I do?
我该怎么办？
回答by keveman
The type of the return value of TensorFlow Python API functions, including tf.rangeis a Tensor. A Tensoris a symbolic handle to node in a graph that represents computation. You perform the actual computation by calling the evalmethod on a Tensor, or by passing the object to runmethod of a Session. In your case, perhaps what you intended to do was simply iterate over numpy's range.
的类型的TensorFlow Python的API函数的返回值的，包括tf.range是一个Tensor。ATensor是表示计算的图中节点的符号句柄。您可以通过调用执行实际的计算eval上的方法Tensor，或通过将对象run的方法Session。在您的情况下，也许您打算做的只是迭代numpy's range。
for in in np.range(...):
# do something
回答by patapouf_ai
To do this you will need to use the tensorflow while loop (tf.while_loop) as follows:
为此，您需要使用 tensorflow while 循环 ( tf.while_loop)，如下所示：
i = tf.constant(0)
while_condition = lambda i: tf.less(i, input_placeholder[1, 1])
def body(i):
# do something here which you want to do in your loop
# increment i
return [tf.add(i, 1)]
# do the loop:
r = tf.while_loop(while_condition, body, [i])
