问题描述
限时送ChatGPT账号..我正在尝试使用 tf.while_loop
并行化循环.正如此处所建议的那样,parallel_iterations
参数在 Eager 模式中没有影响.所以我试图用 tf.function
包装 tf.while_loop
.但是,添加装饰器后,迭代变量的行为发生了变化.
I am trying to parallelize loop using tf.while_loop
. As suggested here, the parallel_iterations
argument doesn't make a difference in the eager mode. So I attempted to wrap tf.while_loop
with tf.function
. However, after adding the decorator,the behavior of the iteration variable changes.
例如,这段代码有效.
result = np.zeros(10)
iteration = tf.constant(0)
c = lambda i: tf.less(i, 10)
def print_fun(iteration):
result[iteration] = iteration
iteration += 1
return (iteration,)
tf.while_loop(c, print_fun, [iteration])
如果我添加装饰器,就会出现错误.
If I add the decorator, bug occurs.
result = np.zeros(10)
iteration = tf.constant(0)
c = lambda i: tf.less(i, 10)
def print_fun(iteration):
result[iteration] = iteration
iteration += 1
return (iteration,)
@tf.function
def run_graph():
iteration = tf.constant(0)
tf.while_loop(c, print_fun, [iteration])
run_graph()
从我的调试过程中,我发现变量 iteration
从张量变为占位符.这是为什么?我应该如何修改代码以消除错误?
From my debugging process, I found that variable iteration
changes from a tensor to a placeholder. Why is that? How should I modify the code to eliminate the bug?
谢谢.
推荐答案
您的第一个代码段(没有 @tf.function
的代码段)中的代码利用了 TensorFlow 2 的急切执行来操作numpy 数组(即,您的外部 iteration
对象)直接.使用 @tf.function
,这不起作用,因为@tf.function 试图将您的代码编译成 tf.Graph,它不能直接对 numpy 数组进行操作(它只能处理 tensorflow 张量).要解决此问题,请使用 tf.Variable 并继续为其切片赋值.
The code in your first snippet (the one without the @tf.function
) takes advantage of TensorFlow 2's eager execution to manipulate a numpy array (i.e., your outer iteration
object) directly. With @tf.function
, this doesn't work because @tf.function tries to compile your code into a tf.Graph, which cannot operate on a numpy array directly (it can only process tensorflow tensors). To get around this issue, use a tf.Variable and keep assigning value into its slices.
使用 @tf.function
,您实际上可以通过利用 @tf.function
的自动 Python-to-图形转换功能(称为 AutoGraph).你只需要写一个普通的 Python while 循环(使用 tf.less()
代替 <
运算符),while 循环会被 AutoGraph 编译成一个 tf.while_loop 在幕后.
With @tf.function
, what you are trying to do is actually achievable with simpler code, by taking advantage of @tf.function
's automatic Python-to-graph transformation feature (known as AutoGraph). You just write a normal Python while loop (using tf.less()
in lieu of the <
operator), and the while loop will be compiled by AutoGraph into a tf.while_loop under the hood.
代码看起来像:
result = tf.Variable(np.zeros([10], dtype=np.int32))
@tf.function
def run_graph():
i = tf.constant(0, dtype=tf.int32)
while tf.less(i, 10):
result[i].assign(i) # Performance may require tuning here.
i += 1
run_graph()
print(result.read_value())
这篇关于Tensorflow 2.0 中的 tf.function 和 tf.while 循环的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
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