on-lstm | Keras implement of ON-LSTM | Machine Learning library

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kandi X-RAY | on-lstm Summary

on-lstm is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. on-lstm has no bugs, it has no vulnerabilities and it has low support. However on-lstm build file is not available. You can download it from GitHub.

Keras implement of ON-LSTM (Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks)

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on-lstm has a low active ecosystem.

It has 145 star(s) with 23 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of on-lstm is current.

Quality

on-lstm has 0 bugs and 0 code smells.

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on-lstm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

on-lstm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

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on-lstm does not have a standard license declared.

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

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

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

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Cumulative softmax function
Generate dataset data
Convert a string into a list of ids
Generate text from question txt file
Tokenize a string
Returns a json string
Build a tree

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on-lstm Key Features

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on-lstm Examples and Code Snippets

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Community Discussions

Trending Discussions on on-lstm

High accuracy on LSTM-RNN model implemented on time-series forecasting

ValueError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes:

Python: Formatting timeseries data for machine learning

Structure diagram of the keras LSTM

QUESTION

High accuracy on LSTM-RNN model implemented on time-series forecasting

Asked 2022-Jan-20 at 08:01

I am new in LSTM-RNN. I have tested many RNN-LSTM python code with .csv files for time-series. None of them had the accuracy that this guy here: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ has. How can he achieves that with just 4 LSTM cells?

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ANSWER

Answered 2022-Jan-20 at 08:01

I remember this article from years ago (it's from 2016). Don't expect too much from this. It's just a tutorial with toy data.

The author later half-acknowledged that data was too small, had bias and the model was greatly overfitted, which is easily spottable from the graph where predictions and ground truth are just lagging from each other. It's always a bad sign.

You can get that from the comments if you search for "bias, "lookahead" or "overfit".

Source https://stackoverflow.com/questions/70664959

QUESTION

ValueError: Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes:

Asked 2021-Jun-22 at 15:36

Done

I am just trying to run and replicate the following project: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ . Basically until this point I have done everything as it is in the linked project but than I got the following issue:

My Own Dataset - I have tried with the dataframe:

I have tried with his original dataset fully 100% his code but I still have the same error
A.) having the 2 columns (1st column date and 2nd column target values),
B.) time code in to the index and dataframe only containing the target value.

INPUT CODE:

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ANSWER

Answered 2021-Jun-22 at 15:36

Solution

I switched to AWS EC2 SageMaker "Python [conda env:tensorflow2_p36] " so this is the exact pre made environment "tensorflow2_p36"
As I ahev read it in some palces it is probably library collision maybe with NumPy.

Source https://stackoverflow.com/questions/68036975

QUESTION

Python: Formatting timeseries data for machine learning

Asked 2020-Nov-25 at 22:45

I am working with NFL play positional tracking data where there are multiple rows per play. Such I want to organize my data as such:

x_train = [[a1,b1,c1,...],[a2,b2,c2,...],...,[an,bn,cn,...]] y_train = [y1,y2,...,yn]

Where x_train holds tracking data from a play and y_train holds the outcome of the play.

I saw examples of using imdb data for sentiment analysis with a Keras LSTM model and wanted to try the same with my tracking data. But, I am having issues formatting my x_train.

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ANSWER

Answered 2020-Nov-25 at 22:10

I have worked with the Keras LSTM layer in the past, and this seems like a very interesting application of it. I would like to help, but there are many things that go into formatting data for the LSTM layer and before getting it to work properly I would like to clarify the goal of this application.

The positional play data, is that where players are located on the field?

The play outcome data, is this the results of the play i.e. yards gained/lost, passing/running play, etc.?

What are the values you hope to get out of this? (Categorical or numerical)

EDIT/Answer:

Use the .append() method on a list to add to it.

Source https://stackoverflow.com/questions/65012138

QUESTION

Structure diagram of the keras LSTM

Asked 2020-Nov-17 at 14:09

I was reading this post https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ and i want draw in my mind the structure of the LSTM network. Analyzing this part of the code:

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ANSWER

Answered 2020-Nov-17 at 14:09

No, you still have one LSTM layer with four LSTM Neurons.

BTW: If you're looking for a fast way to visualize an ANN: Netron

Source https://stackoverflow.com/questions/64876315

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

Vulnerabilities

No vulnerabilities reported

Install on-lstm

You can download it from GitHub.
You can use on-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.

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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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