T-RNN | run code in py2 | Machine Learning library

by uestc-db Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | T-RNN Summary

T-RNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. T-RNN has no bugs, it has no vulnerabilities and it has low support. However T-RNN build file is not available. You can download it from GitHub.

run code in py2.7 environment with pytorch.

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Quality

Security

License

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Support

T-RNN has a low active ecosystem.

It has 12 star(s) with 6 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of T-RNN is current.

Quality

T-RNN has no bugs reported.

Security

T-RNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

T-RNN 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

T-RNN releases are not available. You will need to build from source code and install.

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Evaluate the model
Convert f_e_e_e_e
Compute the solution of the given solution
Solve the equation
Forward computation
Perform the forward attention
Perform the forward computation
Compute the loss for the given tree node
Add leaf embedding
Test the forward recursively
Evaluate a single node
Predict forward recursively
Forward normal mode
Forward normal step
Construct binary tree
Split the data_dict into train and test ids
Compute the decoder
Formats a GdTree object
Extract number and alignments
Load checkpoint from path
Verify equation 1
Prepare the embedding for this model
Prepare the training tensor
Process encoder hidden
Pre - order preorder
Calculate the mid - order list

Get all kandi verified functions for this library.

T-RNN Key Features

No Key Features are available at this moment for T-RNN.

T-RNN Examples and Code Snippets

No Code Snippets are available at this moment for T-RNN.

Community Discussions

Trending Discussions on T-RNN

Runtime error while running PyTorch model on local machine

ValueError: Tensor Tensor("Const:0", shape=(), dtype=float32) may not be fed with tf.placeholder

tensorflowVariable RNNLM/RNNLM/embedding/Adam_2/ does not exist

QUESTION

Runtime error while running PyTorch model on local machine

Asked 2020-Jun-16 at 07:59

I'm running this notebook locally

https://github.com/udacity/deep-learning-v2-pytorch/blob/master/sentiment-rnn/Sentiment_RNN_Solution.ipynb everything was working just until I started training the model

...

ANSWER

Answered 2020-Jun-16 at 07:59

You are trying to embed the inputs, which are given as ints (torch.int). Only integers (torch.long) can be embedded, since they need to be indices, which cannot be float.

inputs need to be converted to torch.long:

Source https://stackoverflow.com/questions/62400718

QUESTION

ValueError: Tensor Tensor("Const:0", shape=(), dtype=float32) may not be fed with tf.placeholder

Asked 2017-Nov-13 at 06:04

I'm trying to make speech recognition system with tensorflow.

Input data is an numpy array of size 50000 X 1.
Output data (mapping data) is an numpy array of size 400 X 1.

Input and mapping data is passed in batches of 2 in a list.

I've used this tutorial to design the neural network. Following is the code snippet:

For RNN:

...

ANSWER

Answered 2017-Nov-13 at 06:04

You defined keep_prob as a tf.constant, but then trying to feed the value into it. Replace keep_prob = tf.constant(1.0) with keep_prob = tf.placeholder(tf.float32,[]) or keep_prob = tf.placeholder_with_default(1.0,[])

Source https://stackoverflow.com/questions/47257518

QUESTION

tensorflowVariable RNNLM/RNNLM/embedding/Adam_2/ does not exist

Asked 2017-Jun-12 at 18:41

My problem is quite similar to tensorflow embeddings don't exist after first RNN example. But I don't think I get a answer.

I posted my entire file on https://paste.ubuntu.com/24253170/. But I believe the following code really matter.

I get this error message:

...

ANSWER

Answered 2017-Mar-27 at 10:29

I know what's going on here, this is constructor code:(BTW, I use tensorflow 1.0)

Source https://stackoverflow.com/questions/43013951

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

Vulnerabilities

No vulnerabilities reported

Install T-RNN

You can download it from GitHub.
You can use T-RNN 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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