Transfer-Learning-in-keras---custom-data | Implementing Transfer Learning for custom data using VGG | Machine Learning library
kandi X-RAY | Transfer-Learning-in-keras---custom-data Summary
kandi X-RAY | Transfer-Learning-in-keras---custom-data Summary
Transfer-Learning-in-keras---custom-data is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. Transfer-Learning-in-keras---custom-data has no bugs, it has no vulnerabilities and it has low support. However Transfer-Learning-in-keras---custom-data build file is not available. You can download it from GitHub.
Implementing Transfer Learning for custom data using VGG-16 and Resnet-50
Implementing Transfer Learning for custom data using VGG-16 and Resnet-50
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Transfer-Learning-in-keras---custom-data has a low active ecosystem.
It has 139 star(s) with 101 fork(s). There are 7 watchers for this library.
It had no major release in the last 6 months.
There are 6 open issues and 1 have been closed. On average issues are closed in 3 days. There are 8 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of Transfer-Learning-in-keras---custom-data is current.
Quality
Transfer-Learning-in-keras---custom-data has 0 bugs and 0 code smells.
Security
Transfer-Learning-in-keras---custom-data has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Transfer-Learning-in-keras---custom-data code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Transfer-Learning-in-keras---custom-data 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.
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Transfer-Learning-in-keras---custom-data releases are not available. You will need to build from source code and install.
Transfer-Learning-in-keras---custom-data has no build file. You will be need to create the build yourself to build the component from source.
Transfer-Learning-in-keras---custom-data saves you 245 person hours of effort in developing the same functionality from scratch.
It has 596 lines of code, 6 functions and 6 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Transfer-Learning-in-keras---custom-data and discovered the below as its top functions. This is intended to give you an instant insight into Transfer-Learning-in-keras---custom-data implemented functionality, and help decide if they suit your requirements.
- Construct ResNet50
- Convolution block layer
- Computes the identity block
- Constructs a VGG16 image
- Preprocess input
- Decode predictions from a batch of predictions
Get all kandi verified functions for this library.
Transfer-Learning-in-keras---custom-data Key Features
No Key Features are available at this moment for Transfer-Learning-in-keras---custom-data.
Transfer-Learning-in-keras---custom-data Examples and Code Snippets
No Code Snippets are available at this moment for Transfer-Learning-in-keras---custom-data.
Community Discussions
Trending Discussions on Transfer-Learning-in-keras---custom-data
QUESTION
Graph disconnected: cannot obtain value for tensor KerasTensor() Transfer learning
Asked 2021-Mar-24 at 15:04
I'm trying to implement transfer learning on my own model but failing. My implementation follows the guides here
https://keras.io/guides/transfer_learning/
How to do transfer-learning on our own models?
tensoflow 2.4.1
Keras 2.4.3
Old Model (Works really well):
...ANSWER
Answered 2021-Mar-21 at 08:57Here a simple way to operate transfer learning with your model
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Transfer-Learning-in-keras---custom-data
You can download it from GitHub.
You can use Transfer-Learning-in-keras---custom-data 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.
You can use Transfer-Learning-in-keras---custom-data 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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