cnn_finetune | Fine-tune CNN in Keras | Machine Learning library

by flyyufelix Python Version: Current License: MIT

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

kandi X-RAY | cnn_finetune Summary

cnn_finetune is a Python library typically used in Artificial Intelligence, Machine Learning, Ruby On Rails, Keras applications. cnn_finetune has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However cnn_finetune build file is not available. You can download it from GitHub.

Fine-tune CNN in Keras

Support

Quality

Security

License

Reuse

Support

cnn_finetune has a medium active ecosystem.

It has 920 star(s) with 418 fork(s). There are 35 watchers for this library.

It had no major release in the last 6 months.

There are 31 open issues and 27 have been closed. On average issues are closed in 25 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cnn_finetune is current.

Quality

cnn_finetune has 0 bugs and 0 code smells.

Security

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

cnn_finetune code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

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

Reuse

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

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

Embed inception v3 model
Batch Normalization
Gramlenet model
Generate a DenseNet - learn model
Convolution block
Transition a block of data
Concatenate dense layer
Constructs a DenseNet - 11 dataset
Generate a Densenet - 1 model
Resnet50
Construct the identity block
VGG19 model
Generate a resnet101 model
Resnet 2D image
Create a VGG16 model
Constructs an inception model
Base layer
Blockimplementation of blockinception
Perform block invasion
Loads cifar10 training and validation sets

Get all kandi verified functions for this library.

cnn_finetune Key Features

No Key Features are available at this moment for cnn_finetune.

cnn_finetune Examples and Code Snippets

No Code Snippets are available at this moment for cnn_finetune.

Community Discussions

Trending Discussions on cnn_finetune

How to add data via directories for training images

QUESTION

How to add data via directories for training images

Asked 2018-Oct-12 at 17:55

I have been going through git repository by flyyufelix "https://github.com/flyyufelix/cnn_finetune" to fine tune an inception v3 network I want to train network to detect a disease so I have 2 set of images one with disease and without disease. The git says X_train, Y_train, X_valid, Y_valid = load_data() he loads the cifar dataset ,The git asks us to create our own load_data() function.The author has the code as below

...

ANSWER

Answered 2018-Jan-04 at 01:24

Use Keras' ImageDataGenerator() class and call flow_from_directory() on it. The labels will be automatically inferred from the directory names. So if you have a directory titled "disease," then Keras would infer that all images within that directory are labeled as "disease," and the same thing would be true for another directory titled "no disease," for example.

I demonstrate how to prepare image data for training a CNN in Keras in this video. The first half of the video is about image organization on disk, and then the second half goes through the process described above.

Source https://stackoverflow.com/questions/48083105

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

Vulnerabilities

No vulnerabilities reported

Install cnn_finetune

You can download it from GitHub.
You can use cnn_finetune 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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