kandi X-RAY | LearnTensorFlow Summary
kandi X-RAY | LearnTensorFlow Summary
LearnTensorFlow is a Python library. LearnTensorFlow has no bugs, it has no vulnerabilities and it has low support. However LearnTensorFlow build file is not available. You can download it from GitHub.
最初从才云科技Caicloud《TensorFlow实战Google深度学习框架》和网上资源学习TensorFlow框架，过了一年多学的杂乱无章。此次温故而知新沉下心系统专研学习一番。在理解相关深度学习概念以及复原代码理解的基础上，不断提升自己学习热情。此项目希望对自己学习深度学习、TensorFlow框架以及其高级keras API有很多的提升与帮助。
最初从才云科技Caicloud《TensorFlow实战Google深度学习框架》和网上资源学习TensorFlow框架，过了一年多学的杂乱无章。此次温故而知新沉下心系统专研学习一番。在理解相关深度学习概念以及复原代码理解的基础上，不断提升自己学习热情。此项目希望对自己学习深度学习、TensorFlow框架以及其高级keras API有很多的提升与帮助。
Support
Quality
Security
License
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Support
LearnTensorFlow has a low active ecosystem.
It has 4 star(s) with 2 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
LearnTensorFlow has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of LearnTensorFlow is current.
Quality
LearnTensorFlow has no bugs reported.
Security
LearnTensorFlow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
LearnTensorFlow 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
LearnTensorFlow releases are not available. You will need to build from source code and install.
LearnTensorFlow 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 LearnTensorFlow and discovered the below as its top functions. This is intended to give you an instant insight into LearnTensorFlow implemented functionality, and help decide if they suit your requirements.
- Train a single training step
- Serialize features
- Calculate the loss between gen_output and target_output
- Computes the discriminator loss
- Generate a dataset
- Upsamples the given filters
- Downsampling of filters
- Train images
- Generate training images
- Train a training step
- Plots a violin plot
- Add feature values to plot
- Plots the attention
- Serializes the features
- Downsampling
- Upsample a set of filters
- Plot an example
- Takes a dataset and prints it to stdout
- Build a keras model
- Generate images
- Generate text
- Compute the image
- Compute the attention graph
- Run one epoch
- Define discriminator
- Create a Keras model
- Performs a trian step
- Plot the training history
Get all kandi verified functions for this library.
LearnTensorFlow Key Features
No Key Features are available at this moment for LearnTensorFlow.
LearnTensorFlow Examples and Code Snippets
No Code Snippets are available at this moment for LearnTensorFlow.
Community Discussions
Trending Discussions on LearnTensorFlow
QUESTION
InvalidArgumentError while coding MNIST tutorial
Asked 2017-Aug-30 at 05:34
these are my first tensorflow steps, and I would like if somebody else had the same issues as me and if there is a way around it.
I am coding the mnist tutorial and my current code-snippet is:
...ANSWER
Answered 2017-Aug-29 at 11:27The error says that you didn't feed a value for a placeholder that is needed. Replace y with y_ on this line sess.run(train_step , feed_dict={x:batch_xs,y:batch_xy}).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install LearnTensorFlow
You can download it from GitHub.
You can use LearnTensorFlow 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.
You can use LearnTensorFlow 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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