tensorflow_notebook | tensorflow notebook\人工智能实践 | Graph Database library

by Tianxiaomo Python Version: Current License: No License

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

kandi X-RAY | tensorflow_notebook Summary

tensorflow_notebook is a Python library typically used in Database, Graph Database, Tensorflow applications. tensorflow_notebook has no bugs, it has no vulnerabilities and it has low support. However tensorflow_notebook build file is not available. You can download it from GitHub.

tensorflow notebook\人工智能实践

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tensorflow_notebook has a low active ecosystem.

It has 35 star(s) with 16 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. On average issues are closed in 310 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow_notebook is current.

Quality

tensorflow_notebook has 0 bugs and 0 code smells.

Security

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

tensorflow_notebook code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow_notebook 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

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

tensorflow_notebook 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.

tensorflow_notebook saves you 157 person hours of effort in developing the same functionality from scratch.

It has 391 lines of code, 7 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Backward computation
Generate random samples
Concatenate regularizer
Creates a weight matrix
Create a bias variable
Creates a weight tensor
Create a bias tensor

Get all kandi verified functions for this library.

tensorflow_notebook Key Features

No Key Features are available at this moment for tensorflow_notebook.

tensorflow_notebook Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow_notebook.

Community Discussions

Trending Discussions on tensorflow_notebook

Linear Regression model On tensorflow can't learn bias

QUESTION

Linear Regression model On tensorflow can't learn bias

Asked 2017-Jun-30 at 10:09

I am trying to train a linear regression model in Tensorflow using some generated data. The model seems to learn the slope of the line, but is unable to learn the bias.

I have tried changing the no. of epochs, the weight(slope) and the biases, but every time , the learnt bias by the model comes out to be zero. I don't know where I am going wrong and some help would be appreciated.

Here is the code.

...

ANSWER

Answered 2017-Jun-30 at 10:09

The main problem is that you are feeding just one sample at a time to the model. This makes your optimizer very inestable, that's why you have to use such a small learning rate. I will suggest you to feed more samples in each step.

If you insist in feeding one sample at a time, maybe you should consider using an optimizer with momentum, like tf.train.AdamOptimizer(learning_rate). This way you can increase the learning rate and reach convergence.

Source https://stackoverflow.com/questions/44842537

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow_notebook

You can download it from GitHub.
You can use tensorflow_notebook 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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