Python 将列表输入 TensorFlow 中的 feed_dict 时发出问题
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Issue feeding a list into feed_dict in TensorFlow
提问by d-roy
I'm trying to pass a list into feed_dict, however I'm having trouble doing so. Say I have:
我正在尝试将列表传递给feed_dict，但是我在这样做时遇到了麻烦。说我有：
inputs = 10 * [tf.placeholder(tf.float32, shape=(batch_size, input_size))]
where inputs is fed into some function outputsthat I want to compute. So to run this in tensorflow, I created a session and ran the following:
其中输入被输入到outputs我想要计算的某个函数中。因此，为了在 tensorflow 中运行它，我创建了一个会话并运行以下命令：
sess.run(outputs, feed_dict = {inputs: data})
#data is my list of inputs, which is also of length 10
but I get an error, TypeError: unhashable type: 'list'.However, I'm able to pass the data element-wise like so:
但是我收到一个错误，TypeError: unhashable type: 'list'.但是，我可以像这样按元素传递数据：
sess.run(outputs, feed_dict = {inputs[0]: data[0], ..., inputs[9]: data[9]})
So I'm wondering if there's a way I can solve this issue. I've also tried to construct a dictionary(using a forloop), however this results in a dictionary with a single element, where they key is:
tensorflow.python.framework.ops.Tensor at 0x107594a10
所以我想知道是否有办法解决这个问题。我还尝试构建一个字典（使用for循环），但是这会生成一个包含单个元素的字典，它们的键是：
tensorflow.python.framework.ops.Tensor at 0x107594a10
采纳答案by mrry
There are two issues that are causing problems here:
这里有两个问题导致问题：
The first issue is that the Session.run()call only accepts a small number of types as the keys of the feed_dict. In particular, lists of tensors are notsupported as keys, so you have to put each tensor as a separate key.*One convenient way to do this is using a dictionary comprehension:
第一个问题是Session.run()调用只接受少量类型作为feed_dict. 特别是，不支持张量列表作为键，因此您必须将每个张量作为单独的键。*一种方便的方法是使用字典理解：
inputs = [tf.placeholder(...), ...]
data = [np.array(...), ...]
sess.run(y, feed_dict={i: d for i, d in zip(inputs, data)})
The second issue is that the 10 * [tf.placeholder(...)]syntax in Python creates a list with ten elements, where each element is the same tensor object(i.e. has the same nameproperty, the same idproperty, and is reference-identical if you compare two elements from the list using inputs[i] is inputs[j]). This explains why, when you tried to create a dictionary using the list elements as keys, you ended up with a dictionary with a single element - because all of the list elements were identical.
第二个问题是10 * [tf.placeholder(...)]Python中的语法创建了一个包含 10 个元素的列表，其中每个元素都是相同的张量对象（即具有相同的name属性，相同的id属性，如果使用 比较列表中的两个元素，则引用相同inputs[i] is inputs[j]） . 这解释了为什么当您尝试使用列表元素作为键创建字典时，您最终会得到一个具有单个元素的字典 - 因为所有列表元素都是相同的。
To create 10 different placeholder tensors, as you intended, you should instead do the following:
要按照您的意图创建 10 个不同的占位符张量，您应该执行以下操作：
inputs = [tf.placeholder(tf.float32, shape=(batch_size, input_size))
for _ in xrange(10)]
If you print the elements of this list, you'll see that each element is a tensor with a different name.
如果打印此列表的元素，您将看到每个元素都是一个具有不同名称的张量。
EDIT:*You can now pass tuplesas the keys of a feed_dict, because these may be used as dictionary keys.
编辑：*您现在可以将元组作为 a 的键传递feed_dict，因为它们可以用作字典键。
回答by Salvador Dali
Here is a correct example:
这是一个正确的例子：
batch_size, input_size, n = 2, 3, 2
# in your case n = 10
x = tf.placeholder(tf.types.float32, shape=(n, batch_size, input_size))
y = tf.add(x, x)
data = np.random.rand(n, batch_size, input_size)
sess = tf.Session()
print sess.run(y, feed_dict={x: data})
And here is a strange things I see in your approach. For some reason you use 10 * [tf.placeholder(...)], which creates 10 tensors of size (batch_size, input_size). No idea why do you do this, if you can just create on Tensor of rank 3 (where the first dimension is 10).
这是我在你的方法中看到的奇怪的事情。出于某种原因，您使用10 * [tf.placeholder(...)]，这会创建 10 个大小为 的张量(batch_size, input_size)。不知道为什么要这样做，如果您可以在 3 级张量上创建（其中第一个维度是 10）。
Because you have a list of tensors (and not a tensor), you can not feed your data to this list (but in my case I can feed to my tensor).
因为您有一个张量列表（而不是张量），所以您不能将您的数据提供给该列表（但在我的情况下，我可以提供给我的张量）。
回答by tansg
feed_dict can be provided by preparing a dictionary beforehand as follows
feed_dict 可以通过预先准备字典来提供，如下所示
n = 10
input_1 = [tf.placeholder(...) for _ in range(n)]
input_2 = tf.placeholder(...)
data_1 = [np.array(...) for _ in range(n)]
data_2 = np.array(...)
feed_dictionary = {}
for i in range(n):
feed_dictionary[input_1[i]] = data_1[i]
feed_dictionary[input_2] = data_2
sess.run(y, feed_dict=feed_dictionary)
