keras-squeezenet | SqueezeNet implementation with Keras Framework | Machine Learning library
kandi X-RAY | keras-squeezenet Summary
kandi X-RAY | keras-squeezenet Summary
keras-squeezenet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-squeezenet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install keras-squeezenet' or download it from GitHub, PyPI.
SqueezeNet implementation with Keras Framework
SqueezeNet implementation with Keras Framework
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Quality
Security
License
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Support
keras-squeezenet has a low active ecosystem.
It has 389 star(s) with 152 fork(s). There are 17 watchers for this library.
It had no major release in the last 12 months.
There are 17 open issues and 3 have been closed. On average issues are closed in 61 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of keras-squeezenet is v1.0
Quality
keras-squeezenet has 0 bugs and 0 code smells.
Security
keras-squeezenet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
keras-squeezenet code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
keras-squeezenet 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
keras-squeezenet releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
keras-squeezenet saves you 46 person hours of effort in developing the same functionality from scratch.
It has 122 lines of code, 2 functions and 3 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed keras-squeezenet and discovered the below as its top functions. This is intended to give you an instant insight into keras-squeezenet implemented functionality, and help decide if they suit your requirements.
- Squeeze a tensor .
- Fire module .
Get all kandi verified functions for this library.
keras-squeezenet Key Features
No Key Features are available at this moment for keras-squeezenet.
keras-squeezenet Examples and Code Snippets
No Code Snippets are available at this moment for keras-squeezenet.
Community Discussions
Trending Discussions on keras-squeezenet
QUESTION
ImportError: cannot import name '_obtain_input_shape' in keras
Asked 2022-Jan-28 at 15:43
When I try to import keras_squeezenet I get this error:
...ANSWER
Answered 2022-Jan-28 at 15:43Did you tried the new version ? (see : https://github.com/rcmalli/keras-squeezenet)
you can install it with :
pip install git+https://github.com/rcmalli/keras-squeezenet.git
QUESTION
ImportError: cannot import name 'get_config' in keras_squeezenet
Asked 2022-Jan-28 at 12:26
I'm trying to import keras_squeezenet into my project, but I'm getting this error:
...ANSWER
Answered 2022-Jan-28 at 12:26try upgrading tensorflow:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install keras-squeezenet
You can install using 'pip install keras-squeezenet' or download it from GitHub, PyPI.
You can use keras-squeezenet 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-squeezenet 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 .
Find more information at:
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