CODEWITHSUNDEEP
pythontensorflow
Nils Cao
Nils Cao

Reputation: 1419

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

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?

Upvotes: 52

Views: 105924

Answers (5)

Soran Ghaderi
Soran Ghaderi

Reputation: 237

  1. 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.

  2. Another way is to use numpy instead:

    x = np.zeros((tensor dimensions))

  3. Use Pytorch:

    x = torch.zeros((tensor dimensions))

Upvotes: 1

xiangshu lin
xiangshu lin

Reputation: 209

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

Upvotes: 20

mrry
mrry

Reputation: 126154

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.

Upvotes: 51

J Agustin Barrachina
J Agustin Barrachina

Reputation: 4090

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.

  1. 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)
  2. Create new matrix of shape outputs with the new desired value: new_values
  3. 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

Upvotes: 0

yuva-rajulu
yuva-rajulu

Reputation: 436

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)

Upvotes: 8

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