tf_classification | testing code for image classification | Machine Learning library

by visipedia Python Version: Current License: MIT

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

kandi X-RAY | tf_classification Summary

tf_classification is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tf_classification has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Training, evaluation and testing code for image classification using TensorFlow

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tf_classification has a low active ecosystem.

It has 128 star(s) with 35 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 4 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 tf_classification is current.

Quality

tf_classification has 0 bugs and 0 code smells.

Security

tf_classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf_classification code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf_classification 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

tf_classification releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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

tf_classification saves you 2481 person hours of effort in developing the same functionality from scratch.

It has 5399 lines of code, 185 functions and 36 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Exports the given checkpoint
Inception resnet v2
Block8
Train model
Apply the image
Calculates the size of the largest_side
Apply a random selector to the given function
Compute classification
Inception v3
Base function for inception v3
Returns the kernel size for a small input
Convert input tensor to 1x1x1
Visualize training inputs
Calculate the Linenet V1 network
Inception V2
Inception V4
Parse command line arguments
Inception v1 layer
Runs prediction
Process classification prediction
Profile a model
Resnet model
Resnet block
Extract features and save them to disk
Parse a config file
Wrap a partial function

Get all kandi verified functions for this library.

tf_classification Key Features

No Key Features are available at this moment for tf_classification.

tf_classification Examples and Code Snippets

No Code Snippets are available at this moment for tf_classification.

Community Discussions

Trending Discussions on tf_classification

How do I convert a Tensorflow model to .mlmodel?

`estimator.train` with num_steps in Tensorflow

QUESTION

How do I convert a Tensorflow model to .mlmodel?

Asked 2018-Dec-21 at 18:38

I want to convert a Tensorflow model with the following structure to a .mlmodel file for use in an iOS app:

...

ANSWER

Answered 2018-Dec-21 at 17:16

None of that stuff is used by the Core ML model. The yaml files etc are used only to train the TF model.

All you need to provide is a frozen graph (a .pb file) and then convert it to an mlmodel using tfcoreml.

It looks like your project doesn't have a frozen graph but checkpoints. There is a TF utility that you can use to convert the checkpoint to a frozen graph, see https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py

Source https://stackoverflow.com/questions/53887554

QUESTION

`estimator.train` with num_steps in Tensorflow

Asked 2018-Jan-15 at 08:03

I have made a custom estimator in Tensorflow 1.4. In estimator.trainfunction, I see a steps parameter, which I am using as a way to stop the training and then evaluate on my validation dataset.

...

ANSWER

Answered 2018-Jan-15 at 08:03

The issue

The issue comes from the fact that what you plot in TensorBoard is the accuracy or AUC computed since the beginning of estimator.train.

Here is what happens in details:

you create a summary based on the second output of tf.metrics.accuracy

Source https://stackoverflow.com/questions/48241370

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

Vulnerabilities

No vulnerabilities reported

Install tf_classification

You can download it from GitHub.
You can use tf_classification 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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