TreeLSTM | : christmas_tree : TreeLSTM 's python version

by sunprinceS Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | TreeLSTM Summary

TreeLSTM is a Python library. TreeLSTM has no bugs, it has no vulnerabilities and it has low support. However TreeLSTM build file is not available. You can download it from GitHub.

:christmas_tree: TreeLSTM's python version

Support

Quality

Security

License

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Support

TreeLSTM has a low active ecosystem.

It has 7 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

TreeLSTM has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TreeLSTM is current.

Quality

TreeLSTM has no bugs reported.

Security

TreeLSTM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

TreeLSTM does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

TreeLSTM releases are not available. You will need to build from source code and install.

TreeLSTM has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed TreeLSTM and discovered the below as its top functions. This is intended to give you an instant insight into TreeLSTM implemented functionality, and help decide if they suit your requirements.

Perform a forward pass on the given sentence
Returns the child of the node
True if the node is a leaf
Forward a single pass node
Backprop propagation
Calculate back - propagation
Batch training
Batch train method
Batch train method
An iterator over all sentences in the corpus
Updates the parameters of the model
Reset gradients
Load the GloVec

Get all kandi verified functions for this library.

TreeLSTM Key Features

No Key Features are available at this moment for TreeLSTM.

TreeLSTM Examples and Code Snippets

No Code Snippets are available at this moment for TreeLSTM.

Community Discussions

Trending Discussions on TreeLSTM

How to debug invalid gradient at index 0? - PyTorch

QUESTION

How to debug invalid gradient at index 0? - PyTorch

Asked 2020-Jan-31 at 19:42

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 19:42

The 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.

Source https://stackoverflow.com/questions/60009868

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TreeLSTM

You can download it from GitHub.
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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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