Binary-Classifier | simple cat-dog classifier | Machine Learning library

by RakshithGB Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Binary-Classifier Summary

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

A simple cat-dog classifier based on ResNet 50 using keras with tensorflow backend.

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Support

Binary-Classifier has a low active ecosystem.

It has 7 star(s) with 4 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. On average issues are closed in 15 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Binary-Classifier is current.

Quality

Binary-Classifier has no bugs reported.

Security

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

License

Binary-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.

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Wait for the program to finish .
Start animation .
Predict a model .
Terminate animation .

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Binary-Classifier Key Features

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

Binary-Classifier Examples and Code Snippets

Update the confusion matrix with the given parameters .

python

Lines of Code : 200

License : Non-SPDX (Apache License 2.0)

Copy

def _update_confusion_matrix_variables_optimized(
    variables_to_update,
    y_true,
    y_pred,
    thresholds,
    multi_label=False,
    sample_weights=None,
    label_weights=None,
    thresholds_with_epsilon=False):
  """Update confusion matri

Community Discussions

Trending Discussions on Binary-Classifier

Editing TensorFlow Source to fix unbalanced data

QUESTION

Editing TensorFlow Source to fix unbalanced data

Asked 2017-Jun-04 at 17:04

I have highly unbalanced data in a two class problem that I am trying to use TensorFlow to solve with a NN. I was able to find a posting that exactly described the difficulty that I'm having and gave a solution which appears to address my problem. However I'm working with an assistant, and neither of us really knows python and so TensorFlow is being used like a black box for us. I have extensive (decades) of experience working in a variety of programming languages in various paradigms. That experience allows me to have a pretty good intuitive grasp of what I see happening in the code my assistant cobbled together to get a working model, but neither of us can follow what is going on enough to be able to tell exactly where in TensorFlow we need to make edits to get what we want.

I'm hoping someone with a good knowledge of Python and TensorFlow can look at this and just tell us something like, "Hey, just edit the file called xxx and at the lines at yyy," so we can get on with it.

Below, I have a link to the solution we want to implement, and I've also included the code my assistant wrote that initially got us up and running. Our code produces good results when our data is balanced, but when highly imbalanced, it tends to classify everything skewed to the larger class to get better results.

Here is a link to the solution we found that looks promising:

Loss function for class imbalanced binary classifier in Tensor flow

I've included the relevant code from this link below. Since I know that where we make these edits will depend on how we are using TensorFlow, I've also included our implementation immediately under it in the same code block with appropriate comments to make it clear what we want to add and what we are currently doing:

...

ANSWER

Answered 2017-Jun-04 at 17:04

The answer you want:

You should add these codes in your train_neural_network(x) function.

Source https://stackoverflow.com/questions/44330980

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

Vulnerabilities

No vulnerabilities reported

Install Binary-Classifier

You can download it from GitHub.
You can use Binary-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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