treeLSTM | Personal implementation of the ACL 19 paper | Machine Learning library
kandi X-RAY | treeLSTM Summary
kandi X-RAY | treeLSTM Summary
Personal implementation of the ACL 19 paper "Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations" with pytorch.
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Top functions reviewed by kandi - BETA
- Evaluate the model at dev
- Forward computation
- Perform the forward computation
- Get the hidden buffer
treeLSTM Key Features
treeLSTM Examples and Code Snippets
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QUESTION
I am trying to train an actor-critic model, but when I reach the backprop for the critic I get this error:
RuntimeError: invalid gradient at index 0 - expected type torch.cuda.FloatTensor but got torch.FloatTensor
I am failing to identify which gradient the error refers to. Can anyone help?
Here is the Stack trace:
...ANSWER
Answered 2020-Jan-31 at 19:42The error says it's expecting a cuda tensor and got a non-cuda tensor, so that's what I'd look for.
Calls like grad_output.cuda()
returns a cuda tensor. It's not an inplace operation. You probably wanted grad_output = grad_output.cuda()
, so I'd start by fixing calls like that.
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Install treeLSTM
You can use treeLSTM like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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