T-LSTM | Time-Aware LSTM | Machine Learning library

by illidanlab Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | T-LSTM Summary

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

Time-Aware LSTM

Support

Quality

Security

License

Reuse

Support

T-LSTM has a low active ecosystem.

It has 115 star(s) with 53 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 9 have been closed. On average issues are closed in 32 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of T-LSTM is current.

Quality

T-LSTM has 0 bugs and 0 code smells.

Security

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

T-LSTM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

T-LSTM is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

T-LSTM saves you 234 person hours of effort in developing the same functionality from scratch.

It has 570 lines of code, 35 functions and 4 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Gets the loss of the decoder
Gets the representation of the encoder
Get the output of the decoder
Get the initial state of decoder
Get decoder states
Train the model
Get all hidden states
Get the list of outputs
Calculates the cost of the loss function
Get the representation of the encoder
Get the ini state cell
Get encoder states
Transformer LSTM decoder
Computes the log of the given time
Concatenate TSTM
Elapse time t
Tensor layer
Tensor of LSTM decoder
Generate batch data
Runs test on input data
LSTM encoder

Get all kandi verified functions for this library.

T-LSTM Key Features

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

T-LSTM Examples and Code Snippets

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

Community Discussions

Trending Discussions on T-LSTM

stacking LSTM layer on top of BERT encoder in Keras

Get output layer Tensorflow 1.14

Building CNN + LSTM in Keras for a regression problem. What are proper shapes?

PyTorch LSTM crashing on colab gpu (works fine on cpu)

QUESTION

stacking LSTM layer on top of BERT encoder in Keras

Asked 2022-Mar-23 at 12:24

I have been trying to stack a single LSTM layer on top of Bert embeddings, but whilst my model starts to train it fails on the last batch and throws the following error message:

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ANSWER

Answered 2022-Mar-23 at 12:24

You should use tf.keras.layers.Reshape in order to reshape bert_output into a 3D tensor and automatically taking into account the batch dimension.

Simply changing:

Source https://stackoverflow.com/questions/71586498

QUESTION

Get output layer Tensorflow 1.14

Asked 2022-Jan-12 at 12:44

I want to create an multi-modal machine learning model using TLSTM for time-variant data. In order to concatinate time-variant with time-invariant data I need to get the output vector of the TLSTM.
I´m using this TLSTM Model: https://github.com/illidanlab/T-LSTM
I updated the repo to be compatible with Tensorflow 1.14 and Python 3.7.12.

I assume you can exreact the output vector at the get_output function:

...

ANSWER

Answered 2022-Jan-12 at 11:11

If you just want to print your output tensor, most of the time tf.print(output) would give you the required result.

Source https://stackoverflow.com/questions/70667664

QUESTION

Building CNN + LSTM in Keras for a regression problem. What are proper shapes?

Asked 2020-Jun-03 at 11:01

I am working on a regression problem where I feed a set of spectograms to CNN + LSTM - architecture in keras. My data is shaped as (n_samples, width, height, n_channels). The question I have how to properly connect the CNN to the LSTM layer. The data needs to be reshaped in some way when the convolution is passed to the LSTM. There are several ideas, such as use of TimeDistributed-wrapper in combination with reshaping but I could not manage to make it work. .

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ANSWER

Answered 2020-Jun-03 at 11:01

One possible solution is setting the LSTM input to be of shape (num_pixels, cnn_features). In your particular case, having a cnn with 32 filters, the LSTM would receive (256*256, 32)

Source https://stackoverflow.com/questions/62169725

QUESTION

PyTorch LSTM crashing on colab gpu (works fine on cpu)

Asked 2020-May-01 at 02:28

Hello I have following LSTM which runs fine on a CPU.

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ANSWER

Answered 2020-May-01 at 02:28

I had to explicitly call CUDA. Once I did that it worked.

Source https://stackoverflow.com/questions/61451339

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

Vulnerabilities

No vulnerabilities reported

Install T-LSTM

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