Tensorflow-Classifier | image classifier using a convolutional neural net | Machine Learning library

by stefanbo92 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Tensorflow-Classifier Summary

Tensorflow-Classifier is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. Tensorflow-Classifier has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Tensorflow-Classifier build file is not available. You can download it from GitHub.

This is an implementation of an image classifier using a convolutional neural net with tensorflow.

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Tensorflow-Classifier has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Tensorflow-Classifier is current.

Quality

Tensorflow-Classifier has no bugs reported.

Security

Tensorflow-Classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Tensorflow-Classifier 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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Tensorflow-Classifier releases are not available. You will need to build from source code and install.

Tensorflow-Classifier has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed Tensorflow-Classifier and discovered the below as its top functions. This is intended to give you an instant insight into Tensorflow-Classifier implemented functionality, and help decide if they suit your requirements.

Basic convolution layer .
Convert an RGB image to a grayscale .
Print shape of x .

Get all kandi verified functions for this library.

Tensorflow-Classifier Key Features

No Key Features are available at this moment for Tensorflow-Classifier.

Tensorflow-Classifier Examples and Code Snippets

No Code Snippets are available at this moment for Tensorflow-Classifier.

Community Discussions

Trending Discussions on Tensorflow-Classifier

Creating many feature columns in Tensorflow

Model trained with TF-slim works perfectly with python inference but gives totally wrong results with C++ one

QUESTION

Creating many feature columns in Tensorflow

Asked 2020-Apr-08 at 23:20

I'm getting started on a Tensorflow project, and am in the middle of defining and creating my feature columns. However, I have hundreds and hundreds of features- it's a pretty extensive dataset. Even after preprocessing and scrubbing, I have a lot of columns.

The traditional way of creating a feature_column is defined in the Tensorflow tutorial and even this StackOverflow post. You essentially declare and initialize a Tensorflow object for each feature column:

...

ANSWER

Answered 2020-Apr-08 at 23:20

What you have makes sense to me. :) copying from your own code:

Source https://stackoverflow.com/questions/46834680

QUESTION

Model trained with TF-slim works perfectly with python inference but gives totally wrong results with C++ one

Asked 2018-May-27 at 20:23

I have trained a two-class MobileNet_v1 using TF-slim on my own custom data. Testing the model with eval_image_classifier.py code, this command, results in 0.936 accuracy. As my actual goal is to deploy the model with c++ code, I froze the model using freeze_graph.py with the following command:

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ANSWER

Answered 2018-May-27 at 20:23

I managed to solve the problem. Turns out, you shouldn't use the graph.pbtxt to freeze the model. In order to do the freezing the right way, we first need to export an inference graph:

Source https://stackoverflow.com/questions/50447649

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Tensorflow-Classifier

You can download it from GitHub.
You can use Tensorflow-Classifier 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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