## Issue

I try to run this code:

```
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 assignment`

It seems I can not assign to tensor, how can I fix it?

## Solution

In general, a TensorFlow tensor object is not assignable, so you cannot use it on the left-hand side of an assignment.

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:

```
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.Variable`

objects, using the `Variable.assign()`

etc. methods. However, `rnn.rnn()`

likely returns a `tf.Tensor`

object that does not support this method.

Answered By – mrry

Answer Checked By – Senaida (Easybugfix Volunteer)