Kaggle-Recursion-Cellular | https :

by ngxbac Python Version: Current License: No License

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kandi X-RAY | Kaggle-Recursion-Cellular Summary

Kaggle-Recursion-Cellular is a Python library. Kaggle-Recursion-Cellular has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

https://www.kaggle.com/c/recursion-cellular-image-classification

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Quality

Security

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Support

Kaggle-Recursion-Cellular has a low active ecosystem.

It has 37 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Kaggle-Recursion-Cellular has no issues reported. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Kaggle-Recursion-Cellular is current.

Quality

Kaggle-Recursion-Cellular has 0 bugs and 0 code smells.

Security

Kaggle-Recursion-Cellular has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Kaggle-Recursion-Cellular code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Kaggle-Recursion-Cellular 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

Kaggle-Recursion-Cellular releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Kaggle-Recursion-Cellular saves you 503 person hours of effort in developing the same functionality from scratch.

It has 1183 lines of code, 63 functions and 15 files.

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

Top functions reviewed by kandi - BETA

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

Predict all sites in the model
Creates a Cellensenet
Calculate softmax predictions
Return a valid image
Creates a Cellsenet
Compute loss
Compute the loss for the given criterion
Add loss to runner state
Combine the metadata files into a pandas dataframe
Apply layer dropout to an image
Load a pandas dataframe
Evaluate the ensemble
Computes the k - fold predictions for a given model
Load one fold
Get all available datasets
Train the model
Constructs a cell lattice
Generate the organization

Get all kandi verified functions for this library.

Kaggle-Recursion-Cellular Key Features

No Key Features are available at this moment for Kaggle-Recursion-Cellular.

Kaggle-Recursion-Cellular Examples and Code Snippets

No Code Snippets are available at this moment for Kaggle-Recursion-Cellular.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install Kaggle-Recursion-Cellular

Thing you should know about the project.
We run experiments via bash files which are located in bin folder.
The config files (yml) are located in configs folder which are corresponding to each bash files. Ex: train_control.sh should go with config_control.yml
The yml config file allows changing either via bash scripts for the flexible settings or directly modification for the fixed settings. Ex: stages/data_params/train_csv can be ./csv/train_0.csv, ./csv/train_2.csv,... etc. So when training K-Fold we make a for loop for the convinent.

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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