TensorFlow2x_Engineering_Implementation

by aianaconda Python Version: Current License: No License

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kandi X-RAY | TensorFlow2x_Engineering_Implementation Summary

TensorFlow2x_Engineering_Implementation is a Python library. TensorFlow2x_Engineering_Implementation has no bugs, it has no vulnerabilities and it has low support. However TensorFlow2x_Engineering_Implementation build file is not available. You can download it from GitHub.

TensorFlow2x_Engineering_Implementation

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TensorFlow2x_Engineering_Implementation has a low active ecosystem.

It has 19 star(s) with 10 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

TensorFlow2x_Engineering_Implementation has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlow2x_Engineering_Implementation is current.

Quality

TensorFlow2x_Engineering_Implementation has no bugs reported.

Security

TensorFlow2x_Engineering_Implementation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

TensorFlow2x_Engineering_Implementation 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

TensorFlow2x_Engineering_Implementation releases are not available. You will need to build from source code and install.

TensorFlow2x_Engineering_Implementation 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 TensorFlow2x_Engineering_Implementation and discovered the below as its top functions. This is intended to give you an instant insight into TensorFlow2x_Engineering_Implementation implemented functionality, and help decide if they suit your requirements.

Train text cnn model
Get user history
Generate a feed dictionary for a given kge
Generate a dictionary for feed data
Generate the main dataset
Generate the model for the given image
Reshape convolutional block
Compute Q sequence
Mask input tensor
Calculate the sinkhorn loss
Compute the cost matrix
Create a tf Dataset from features
Computes the gradient of a function
Input function
Creates a test graph
Check for conflicts between att_names
Computes the decoder
Run pre - train prediction
Build an estimator
Create a dataset from a directory
Read ratings from a file
Draws boxes
Generate the dataset
Summarize a tensor collection
Creates a discriminator model
Convert the kg txt file to a json file
Test for testing

Get all kandi verified functions for this library.

TensorFlow2x_Engineering_Implementation Key Features

No Key Features are available at this moment for TensorFlow2x_Engineering_Implementation.

TensorFlow2x_Engineering_Implementation Examples and Code Snippets

No Code Snippets are available at this moment for TensorFlow2x_Engineering_Implementation.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install TensorFlow2x_Engineering_Implementation

You can download it from GitHub.
You can use TensorFlow2x_Engineering_Implementation 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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