sugartensor | slim tensorflow wrapper that provides syntactic sugar | Machine Learning library
kandi X-RAY | sugartensor Summary
kandi X-RAY | sugartensor Summary
sugartensor is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. sugartensor has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install sugartensor' or download it from GitHub, PyPI.
A slim tensorflow wrapper that provides syntactic sugar for tensor variables. This library will be helpful for practical deep learning researchers not beginners.
A slim tensorflow wrapper that provides syntactic sugar for tensor variables. This library will be helpful for practical deep learning researchers not beginners.
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sugartensor has a low active ecosystem.
It has 373 star(s) with 63 fork(s). There are 19 watchers for this library.
It had no major release in the last 12 months.
There are 22 open issues and 11 have been closed. On average issues are closed in 177 days. There are 4 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of sugartensor is 1.0.0.2
Quality
sugartensor has 0 bugs and 0 code smells.
Security
sugartensor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
sugartensor code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
sugartensor is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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sugartensor releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
sugartensor saves you 818 person hours of effort in developing the same functionality from scratch.
It has 1877 lines of code, 173 functions and 30 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed sugartensor and discovered the below as its top functions. This is intended to give you an instant insight into sugartensor implemented functionality, and help decide if they suit your requirements.
- Layer normalization function
- Layer normalization
- Expand dimensions along axis
- Convert tensor to floatx
- Decorator for sg_sg_opt
- Create a tf variable
- Get current context
- Embed tensorflow function
- R Concatenate tensors
- Convolutional layer
- Compute the bce
- Sg tensorflow tensor
- Compute squared squared squared error
- Decorator for parallel GPUs
- Compute ctc loss
- Reuse input tensor
- R Compute tensorflow tensor
- Compute tensorflow tensor
- Transnet layer
- Decorator for sng_rnn_layer_func
- R Linear RNN
- VGG convolution layer
- Trains training function
- Wrapper for tf Upconv
- R Gensenet layer
- 2d convolution layer
- Setup a tensorflow op
Get all kandi verified functions for this library.
sugartensor Key Features
No Key Features are available at this moment for sugartensor.
sugartensor Examples and Code Snippets
No Code Snippets are available at this moment for sugartensor.
Community Discussions
Trending Discussions on sugartensor
QUESTION
Usage of LSTM/GRU and Flatten throws dimensional incompatibility error
Asked 2020-Sep-15 at 20:26
I want to make use of a promising NN I found at towardsdatascience for my case study.
The data shapes I have are:
...ANSWER
Answered 2020-Aug-17 at 18:14I cannot reproduce your error, check if the following code works for you:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install sugartensor
Dependencies ( Will be installed automatically ). docker installation : See docker README.md.
Requirements tensorflow == 1.0.0
Dependencies ( Will be installed automatically ) tqdm >= 4.8.4
Installation
Requirements tensorflow == 1.0.0
Dependencies ( Will be installed automatically ) tqdm >= 4.8.4
Installation
Support
You can train your model with multiple GPUs using sg_parallel decorator as follow:.
Find more information at:
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