keras-examples | Keras样例解析 | Machine Learning library
kandi X-RAY | keras-examples Summary
kandi X-RAY | keras-examples Summary
keras-examples is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Keras applications. keras-examples has no bugs, it has no vulnerabilities and it has low support. However keras-examples build file is not available. You can download it from GitHub.
Keras样例解析
Keras样例解析
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Quality
Security
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Support
keras-examples has a low active ecosystem.
It has 40 star(s) with 26 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of keras-examples is current.
Quality
keras-examples has 0 bugs and 0 code smells.
Security
keras-examples has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
keras-examples code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
keras-examples 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
keras-examples releases are not available. You will need to build from source code and install.
keras-examples has no build file. You will be need to create the build yourself to build the component from source.
keras-examples saves you 253 person hours of effort in developing the same functionality from scratch.
It has 616 lines of code, 10 functions and 9 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed keras-examples and discovered the below as its top functions. This is intended to give you an instant insight into keras-examples implemented functionality, and help decide if they suit your requirements.
- Read stories from a file
- Parse a list of stories
- Decode X into a string
- Tokenize a string
- Adds ngrams to the token index
- Encodes the given matrix C
- Vectorize stories
- Sample from probability distribution
- Create a set containing n - gram in input_list
Get all kandi verified functions for this library.
keras-examples Key Features
No Key Features are available at this moment for keras-examples.
keras-examples Examples and Code Snippets
No Code Snippets are available at this moment for keras-examples.
Community Discussions
Trending Discussions on keras-examples
QUESTION
Keras Tensorflow Validation Accuracy different when using Subclass Syntax vs Functional or Sequential
Asked 2020-Feb-22 at 08:51
I have reimplemented the Keras MINST CNN example using Sequential, Functional and SubClass syntax.
- https://keras.io/examples/mnist_cnn/
- https://github.com/JamesMcGuigan/kaggle-digit-recognizer/tree/master/src/keras/examples
Everything compiles and runs fine, but I have noticed a major difference in validation accuracy when using SubClass syntax (35%) compared to Sequential/Functional syntax (75%). The model architecture should be the same, so this is confusing me.
...ANSWER
Answered 2020-Feb-22 at 08:51I think in ClassCNN last layer activation is 'relu' which should be 'softmax' as is the case with other models... It is just a human mistake ..... Thankyou...
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install keras-examples
You can download it from GitHub.
You can use keras-examples 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-examples 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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