TrafficFlowPrediction | Traffic Flow Prediction with Neural Networks | Machine Learning library

by xiaochus Python Version: Current License: MIT

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

kandi X-RAY | TrafficFlowPrediction Summary

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

Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).

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Quality

Security

License

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Support

TrafficFlowPrediction has a low active ecosystem.

It has 397 star(s) with 214 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 14 open issues and 2 have been closed. On average issues are closed in 124 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TrafficFlowPrediction is current.

Quality

TrafficFlowPrediction has 0 bugs and 0 code smells.

Security

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

TrafficFlowPrediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TrafficFlowPrediction is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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

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

Train the model for each model
Train model
Get the Ses for the given layers
Builds the Sae
Regress the variance of the variance
Calculate the MAPE coefficient
Process data from train and test
Plot prediction results
Train the model

Get all kandi verified functions for this library.

TrafficFlowPrediction Key Features

No Key Features are available at this moment for TrafficFlowPrediction.

TrafficFlowPrediction Examples and Code Snippets

No Code Snippets are available at this moment for TrafficFlowPrediction.

Community Discussions

Trending Discussions on TrafficFlowPrediction

GRU load model error, ValueError: GRU(reset_after=False) is not compatible with GRU(reset_after=True)

QUESTION

GRU load model error, ValueError: GRU(reset_after=False) is not compatible with GRU(reset_after=True)

Asked 2020-Oct-12 at 22:32

I am new to ML frameworks and also python. I got the source code for a keras-tensorflow project from https://github.com/xiaochus/TrafficFlowPrediction and Also I installed All CUDA and Cudnn right versions. but after loading gru model it raise an error :

ValueError: GRU(reset_after=False) is not compatible with GRU(reset_after=True).

can anyone help me please? thanks. it seems there is an overloading for this function with options. should I add some options like reset_after to enable/disable it? I am just guessing.

...

ANSWER

Answered 2020-Oct-12 at 22:32

I solved it by changing this part of creating model

model.add(GRU(units[1], input_shape=(units[0], 1), return_sequences=True))

model.add(GRU(units[1], input_shape=(units[0], 1), return_sequences=True, reset_after=True))

Source https://stackoverflow.com/questions/64324317

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

Vulnerabilities

No vulnerabilities reported

Install TrafficFlowPrediction

You can download it from GitHub.
You can use TrafficFlowPrediction 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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