image-classification-rnn | Classify MNIST image dataset into 10 classes | Machine Learning library
kandi X-RAY | image-classification-rnn Summary
kandi X-RAY | image-classification-rnn Summary
image-classification-rnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. image-classification-rnn has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However image-classification-rnn build file is not available. You can download it from GitHub.
Classify MNIST image dataset into 10 classes. Build an image classifier with Recurrent Neural Network (RNN: LSTM) on Tensorflow.
Classify MNIST image dataset into 10 classes. Build an image classifier with Recurrent Neural Network (RNN: LSTM) on Tensorflow.
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image-classification-rnn has a low active ecosystem.
It has 72 star(s) with 47 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
There are 2 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 image-classification-rnn is current.
Quality
image-classification-rnn has 0 bugs and 0 code smells.
Security
image-classification-rnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
image-classification-rnn code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
image-classification-rnn is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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image-classification-rnn releases are not available. You will need to build from source code and install.
image-classification-rnn has no build file. You will be need to create the build yourself to build the component from source.
It has 109 lines of code, 4 functions and 2 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed image-classification-rnn and discovered the below as its top functions. This is intended to give you an instant insight into image-classification-rnn implemented functionality, and help decide if they suit your requirements.
- Train the model
- RNN model
- Predict new images
Get all kandi verified functions for this library.
image-classification-rnn Key Features
No Key Features are available at this moment for image-classification-rnn.
image-classification-rnn Examples and Code Snippets
No Code Snippets are available at this moment for image-classification-rnn.
Community Discussions
Trending Discussions on image-classification-rnn
QUESTION
ValueError: Cannot feed value of shape (64,) for Tensor 'Placeholder_1:0', which has shape '(?, 10)'
Asked 2017-Aug-21 at 15:26
I'm new to tensorflow, and I'm trying to reproduce the experiments that released on the site https://github.com/jiegzhan/image-classification-rnn
My TensorFlow version is 1.0.1, so I slightly changed his code. The code I used is below.
...ANSWER
Answered 2017-Apr-29 at 09:09The problem is batch_y is a tensor containing labels (numbers from 0 to 9) specifying the correct class, while your placeholder
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install image-classification-rnn
You can download it from GitHub.
You can use image-classification-rnn 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 image-classification-rnn 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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