在 TensorFlow 中制作一个列表并附加到它 [英] Making a list and appending to it in TensorFlow

问题描述

我是 TensorFlow 的新手.我无法理解如何在 TensorFlow 中创建动态pythonic"列表.基本上，我对张量对象 (train_data[i]) 执行一些计算并将其附加到列表"X，我希望它成为形状为 <代码>(100,)

I am new to TensorFlow. I'm not able to understand how to create a dynamic "pythonic" list in TensorFlow. Basically, I perform some computation on a tensor object (train_data[i]) and append it to a "list" X, which I want to be a tensor with shape (100,)

我想做这样的事情:

X = []
for i in range(100):
    q = tf.log(train_data[i]) 
    print(q)    #Tensor("Log:0", shape=(), dtype=float32) 
    X.append(q)

我希望 X 是一个形状为 (100,) 的张量，基本上是一个列向量，它是一个张量对象.如果我运行上面的代码，我会得到一个 TensorObjects 的 python 列表.

I want X to be a Tensor with shape (100,), basically a column vector which is a tensor object. If I run the code above, I instead get a python list of TensorObjects.

推荐答案

如果要将 X 转换为 (100,) 张量，可以添加 X = tf.stack(X) 在 for 循环之后:

If you want to convert X to a (100,) tensor you could add X = tf.stack(X) after your for loop:

X = []
for i in range(100):
  q = tf.log(train_data[i]) 
  print(q)    #Tensor("Log:0", shape=(), dtype=float32) 
  X.append(q)
X = tf.stack(X)

这是一个有用的构造，您可能希望 tf.unstack 一些张量，循环遍历结果列表，然后使用 tf.stack 返回一个单张量.

This is a useful construct where you may want to tf.unstack some tensor, loop over the resulting list, and then use tf.stack to get back to a single tensor.

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