recurrent-visual-attention | PyTorch Implementation of `` Recurrent Models | Machine Learning library

by kevinzakka Python Version: Current License: MIT

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kandi X-RAY | recurrent-visual-attention Summary

recurrent-visual-attention is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. recurrent-visual-attention has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However recurrent-visual-attention build file is not available. You can download it from GitHub.

In this paper, the attention problem is modeled as the sequential decision process of a goal-directed agent interacting with a visual environment. The agent is built around a recurrent neural network: at each time step, it processes the sensor data, integrates information over time, and chooses how to act and how to deploy its sensor at the next time step.

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recurrent-visual-attention has a low active ecosystem.

It has 425 star(s) with 115 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 26 have been closed. On average issues are closed in 193 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of recurrent-visual-attention is current.

Quality

recurrent-visual-attention has 0 bugs and 0 code smells.

Security

recurrent-visual-attention has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

recurrent-visual-attention code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

recurrent-visual-attention 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

recurrent-visual-attention releases are not available. You will need to build from source code and install.

recurrent-visual-attention 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.

recurrent-visual-attention saves you 388 person hours of effort in developing the same functionality from scratch.

It has 924 lines of code, 48 functions and 11 files.

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

Top functions reviewed by kandi - BETA

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

Get train validation loader
Plot images
Train the model
Loads the checkpoint
Saves checkpoint
Resets the state of the model
Test the model
Returns a test loader
Concatenate a tensor
Extracts a batch of patches from x l
Denormalize the mesh
Resize a numpy array
Convert an array to an Image
Forward to fv
Concatenate a set of patches
Convert image to array
Saves the model configuration
Parse command line arguments
Creates a bounding box with given coordinates
Add an argument group to the parser
Prepare required directories

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recurrent-visual-attention Key Features

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recurrent-visual-attention Examples and Code Snippets

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Community Discussions

Trending Discussions on recurrent-visual-attention

Training Error in PyTorch - RuntimeError: Expected object of type FloatTensor vs ByteTensor

QUESTION

Training Error in PyTorch - RuntimeError: Expected object of type FloatTensor vs ByteTensor

Asked 2018-Jun-20 at 09:40

A minimal working sample will be difficult to post here but basically I am trying to modify this project http://torch.ch/blog/2015/09/21/rmva.html which works smoothly with MNIST. I am trying to run it with my own dataset with a custom dataloader.py as below:

...

ANSWER

Answered 2018-Jun-20 at 09:40

As far as I can tell, it seems that as you commented the normalize / transforms.Normalize operations applied to your dataset, your images don't have their values normalize to float between [0, 1], and are instead keeping their byte values between [0, 255].

Try applying data normalization or at least converting your images to float (32-bit, not 64) values (e.g. in ToTensor, add image = image.float() or while it is still a numpy array using data.astype(numpy.float32)) before feeding them to your network.

Source https://stackoverflow.com/questions/50930300

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

Vulnerabilities

No vulnerabilities reported

Install recurrent-visual-attention

You can download it from GitHub.
You can use recurrent-visual-attention 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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