TypeError: 'Tensor' object does not support item assignment in TensorFlow

Question

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?

Asked By: Nils Cao

||

Source

Answer 1

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 2

Another way you can do it is like this.

aa=tf.Variable(tf.zeros(3, tf.int32))
aa=aa[2].assign(1)

then the output is:

array([0, 0, 1], dtype=int32)

ref:https://www.tensorflow.org/api_docs/python/tf/Variable#assign

Answered By: xiangshu lin

Answer 3

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)

a_list = tf.unstack(a_tensor)

a_list[50:55] = [np.nan for i in range(6)]

a_tensor = tf.stack(a_list)

Answered By: yuva-rajulu

Answer 4

As this comment says, a workaround would be to create a NEW tensor with the previous one and a new one on the zones needed.

Create a mask of shape outputs with 0’s on the indices you want to replace and 1’s elsewhere (Can work also with True and False)
Create new matrix of shape outputs with the new desired value: new_values
Replace only the needed indexes with: outputs_new = outputs* mask + new_values * (1 - mask)

If you would provide me with an MWE I could do the code for you.

A good reference is this note: How to Replace Values by Index in a Tensor with TensorFlow-2.0

Answered By: Agustin Barrachina

Answer 5

Neither tf.Tensor nor tf.Variable is element-wise-assignable.
There is a trick however which is not the most efficient way of
course, especially when you do it iteratively.

You can create a mask and a new_layer tensor with new values and
then

do a Hadamard product (element-wise product).
```
x = original * mask + new_layer * (1-mask)
```
The original * mask part sets the specified values of original
to 0 and the second part, new_layer*(1-mask) assigns new_layer
tensor whatever you want without modifying the elements assigned to
0 by the mask tensor in the previous step.
Another way is to use numpy instead:
```
x = np.zeros((tensor dimensions)) 
```
Use Pytorch:
```
x = torch.zeros((tensor dimensions))
```

Answered By: Soran Ghaderi

TypeError: 'Tensor' object does not support item assignment in TensorFlow

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