keras-squeeze-excite-network | Implementation of Squeeze and Excitation Networks | Machine Learning library
kandi X-RAY | keras-squeeze-excite-network Summary
kandi X-RAY | keras-squeeze-excite-network Summary
keras-squeeze-excite-network is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Keras applications. keras-squeeze-excite-network has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Implementation of Squeeze and Excitation Networks in Keras
Implementation of Squeeze and Excitation Networks in Keras
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keras-squeeze-excite-network has a low active ecosystem.
It has 362 star(s) with 106 fork(s). There are 13 watchers for this library.
It had no major release in the last 6 months.
There are 4 open issues and 16 have been closed. On average issues are closed in 3 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of keras-squeeze-excite-network is current.
Quality
keras-squeeze-excite-network has 0 bugs and 0 code smells.
Security
keras-squeeze-excite-network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
keras-squeeze-excite-network code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
keras-squeeze-excite-network 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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keras-squeeze-excite-network 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 are not available. Examples and code snippets are available.
keras-squeeze-excite-network saves you 693 person hours of effort in developing the same functionality from scratch.
It has 1604 lines of code, 53 functions and 11 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed keras-squeeze-excite-network and discovered the below as its top functions. This is intended to give you an instant insight into keras-squeeze-excite-network implemented functionality, and help decide if they suit your requirements.
- Construct SERES network
- Construct a SLSNet
- Resnet block
- Create ResNet
- Generate the next image
- Create next image convolution
- Construct a convolution block of input tensors
- Bottleneck block
- A SEINception v3
- Return the input shape
- Layer normalization
- Construct an EMobile Network
- A block of convolution layer
- Depth - wise Convolution block
- A SEINception v2
- 2D convolution layer
- Inverse Resnet block
- Squeeze an excite block
- R Reshape an excite block
- Generates a sequence of Tensors
- Create the next convolution block
- Construct SERES
- Constructs a SERESNet
- An SEDenseNet
- Constructs an SEDenseNet image
- Construct SEDenseNet
Get all kandi verified functions for this library.
keras-squeeze-excite-network Key Features
No Key Features are available at this moment for keras-squeeze-excite-network.
keras-squeeze-excite-network Examples and Code Snippets
No Code Snippets are available at this moment for keras-squeeze-excite-network.
Community Discussions
Trending Discussions on keras-squeeze-excite-network
QUESTION
Is sigmoid function only applicable after dense() layer?
Asked 2021-Nov-02 at 20:02
I am making a network which is similar to SE-Net(https://github.com/titu1994/keras-squeeze-excite-network/blob/master/se.py) using keras, but quite different with it.
Suppose that I want to make some layer sequence like :
...ANSWER
Answered 2021-Nov-02 at 20:02you can for sure experiment sigmoid as an activation for cnn layers too but the reason why sigmoid is not used with cnn layers are:
1. Sigmoid function is monotonic but it's derivative is not therefore there is a possibility that your training can be stuck
2. Sigmoid range:[0,1]
if you are experimenting sigmoid with cnn layers then I would suggest you to use it only for few layers. You can give swish a try.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install keras-squeeze-excite-network
You can download it from GitHub.
You can use keras-squeeze-excite-network 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 keras-squeeze-excite-network 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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