Python TypeError：'Tensor' 对象不支持 TensorFlow 中的项目分配
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TypeError: 'Tensor' object does not support item assignment in TensorFlow
提问by Nils Cao
I try to run this code:
我尝试运行此代码：
outputs, states = rnn.rnn(lstm_cell, x, initial_state=initial_state, sequence_length=real_length)
tensor_shape = outputs.get_shape()
for step_index in range(tensor_shape[0]):
word_index = self.x[:, step_index]
word_index = tf.reshape(word_index, [-1,1])
index_weight = tf.gather(word_weight, word_index)
outputs[step_index, :, :]=tf.mul(outputs[step_index, :, :] , index_weight)
But I get error on last line:
TypeError: 'Tensor' object does not support item assignmentIt seems I can not assign to tensor, how can I fix it?
但是我在最后一行出现错误：
TypeError: 'Tensor' object does not support item assignment似乎我无法分配给张量，我该如何解决？
回答by mrry
In general, a TensorFlow tensor object is not assignable*, so you cannot use it on the left-hand side of an assignment.
通常，TensorFlow 张量对象不可分配*，因此您不能在分配的左侧使用它。
The easiest way to do what you're trying to do is to build a Python list of tensors, and tf.stack()them together at the end of the loop:
做你想做的最简单的方法是构建一个张量的 Python 列表，并tf.stack()在循环结束时将它们组合在一起：
outputs, states = rnn.rnn(lstm_cell, x, initial_state=initial_state,
sequence_length=real_length)
output_list = []
tensor_shape = outputs.get_shape()
for step_index in range(tensor_shape[0]):
word_index = self.x[:, step_index]
word_index = tf.reshape(word_index, [-1,1])
index_weight = tf.gather(word_weight, word_index)
output_list.append(tf.mul(outputs[step_index, :, :] , index_weight))
outputs = tf.stack(output_list)
* With the exception of tf.Variableobjects, using the Variable.assign()etc. methods. However, rnn.rnn()likely returns a tf.Tensorobject that does not support this method.
* 除tf.Variable对象外，使用Variable.assign()等方法。但是，rnn.rnn()可能返回tf.Tensor不支持此方法的对象。
回答by xiangshu lin
Another way you can do it like this.
另一种方法你可以这样做。
aa=tf.Variable(tf.zeros(3, tf.int32))
aa=aa[2].assign(1)
then the output is:
那么输出是：
array([0, 0, 1], dtype=int32)
数组([0, 0, 1], dtype=int32)
ref:https://www.tensorflow.org/api_docs/python/tf/Variable#assign
参考：https: //www.tensorflow.org/api_docs/python/tf/Variable#assign
回答by yuvaraj8blr
When you have a tensor already, convert the tensor to a list using tf.unstack(TF2.0) and then use tf.stack like @mrry has mentioned. (when using a multi-dimensional tensor, be aware of the axis argument in unstack)
当您已经有了张量时，使用tf.unstack(TF2.0)将张量转换为列表，然后像 @mrry 提到的那样使用 tf.stack。（使用多维张量时，注意 unstack 中的轴参数）
a_list = tf.unstack(a_tensor)
a_list[50:55] = [np.nan for i in range(6)]
a_tensor = tf.stack(a_list)
