How do you replace values by index in a tensor?

How do you replace values by index in a tensor?

1. Identify the indices to remove.
2. Convert this vector into two arrays containing the actual indices (idx_keep, idx_replace).
3. Create a vector with the values to be replaced.
4. Create a vector with the values to be kept.
5. Create sparse vectors of the two collections above.

What is detach in PyTorch?

detach() tensor. detach() creates a tensor that shares storage with tensor that does not require grad. In order to enable automatic differentiation, PyTorch keeps track of all operations involving tensors for which the gradient may need to be computed (i.e., require_grad is True).

How do you make a tensor in PyTorch?

There are three ways to create a tensor in PyTorch:

1. By calling a constructor of the required type.
2. By converting a NumPy array or a Python list into a tensor. In this case, the type will be taken from the array’s type.
3. By asking PyTorch to create a tensor with specific data for you. For example, you can use the torch.

Is tensor a PyTorch?

PyTorch: Tensors A PyTorch Tensor is basically the same as a numpy array: it does not know anything about deep learning or computational graphs or gradients, and is just a generic n-dimensional array to be used for arbitrary numeric computation. To run operations on the GPU, just cast the Tensor to a cuda datatype.

How do I update tensor in TensorFlow?

To change an immutable tensor value, one has to change the source tensors (placeholders or variables). In your particular case, it’d be easier to make a tfZ a variable as well. By the way, tf. Variable.

Is detach inplace PyTorch?

detach_() is the inplace operation of detach() . They might yield the same result, but in specific edge cases you might get different and unexpected results: An optimizer, which doesn’t hold certain parameters, won’t update them. However other optimizers could still update these parameters, if they are passed.

How does PyTorch detach work?

The detach() method constructs a new view on a tensor which is declared not to need gradients, i.e., it is to be excluded from further tracking of operations, and therefore the subgraph involving this view is not recorded. This can be easily visualised using the torchviz package.

How do you convert to tensor?

Use tensorflow. convert_to_tensor() to convert the array to a tensor

1. print(an_array)
2. data_tensor = tf. convert_to_tensor(an_array)
3. sess = tf. InteractiveSession()
4. print(data_tensor. eval())
5. sess.

How to replace zeros and non-zeros in PyTorch?

In order to replace zeros and non-zeros, you can just chain them together. Just be sure to use a copy of the tensor, since they get modified:

How to change certain values in a torch tensor?

As you can see here two tensors. The first has the q values I want but, some values need to be changed to zeros because of it an end state. The second tensor shows where it will be zeros. At the index where the Boolean value is false is the equivalent spot for where the upper tensor needs to be zeros.

How to replace values by Index in a tensor with TensorFlow?

I think the easiest way to see this operation in the light of TensorFlow operations is to filter the elements that one wants to keep in one vector, create another vector to “fill the blanks” with the masked values that one wants to replace ( e.g. zero, infinity, some other default value), and finally give the result as the sum of the two.

Can a tensor array be represented by a tensor?

It turns out that this apparently straightforward operation is not permitted in TensorFlow if the array is represented by a tensor (but it is if the array is a tf.Variable object). If you try, the most likely outcome is an error like this: