如何将张量列表作为输入传递给张量流中的图形? [英] How to pass list of tensors as a input to the graph in tensorflow?

问题描述

我使用 [None, None, 10] 形状的 tf.placeholder() 将我的输入传递给张量.现在我想遍历输入的第一个维度，并对该维度中的每个切片应用一些函数.但是，当我尝试使用 Python for 循环执行此操作时，我收到一条错误消息，指出 Tensor 对象不可迭代".

I am passing my input to tensors using a tf.placeholder() of shape [None, None, 10]. Now I want to iterate over the first dimension of the input, and apply some function to each slice in that dimension. However, when I try to do this using a Python for loop, I get an error saying that Tensor objects are "not iterable".

有什么方法可以将输入作为形状 [None, 10] 的张量列表传递，我如何将此列表分配给占位符?或者是否有其他方法可以迭代 Tensor 的维度?

Is there any way I can pass the input as the list of tensors of shape [None, 10], and how could I assign this list to the placeholder? Or is there some other way to iterate over a dimension of a Tensor?

推荐答案

这可以使用新的 tf.map_fn(), tf.foldl() tf.foldr() 或(最常见的)tf.scan() 高阶运算符，它们被添加到TensorFlow 0.8 版.您将使用的特定运算符取决于您要执行的计算.例如，如果您想对张量的每一行执行相同的功能并将元素打包回单个张量，您将使用 tf.map_fn():

p = tf.placeholder(tf.float32, shape=[None, None, 100])

def f(x):
    # x will be a tensor of shape [None, 100].
    return tf.reduce_sum(x)

# Compute the sum of each [None, 100]-sized row of `p`.
# N.B. You can do this directly using tf.reduce_sum(), but this is intended as 
# a simple example.
result = tf.map_fn(f, p)

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