convNet.pytorch | ConvNet training using pytorch | Machine Learning library

by eladhoffer Python Version: Current License: MIT

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

kandi X-RAY | convNet.pytorch Summary

convNet.pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. convNet.pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

ConvNet training using pytorch

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convNet.pytorch has a low active ecosystem.

It has 294 star(s) with 81 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 7 have been closed. On average issues are closed in 116 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of convNet.pytorch is current.

Quality

convNet.pytorch has 0 bugs and 73 code smells.

Security

convNet.pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

convNet.pytorch code analysis shows 0 unresolved vulnerabilities.

There are 16 security hotspots that need review.

License

convNet.pytorch 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

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

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

convNet.pytorch saves you 2189 person hours of effort in developing the same functionality from scratch.

It has 4792 lines of code, 321 functions and 35 files.

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

Top functions reviewed by kandi - BETA

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

Main worker function
Set the epoch
Get the loader
Get a dataset by name
Returns the current settings
Reduces a list of values into multiple ranges
Reduces multiple values
Removes weight norm norm from module
Remove the parameter from a module
Compute the linear function
Linear Binomial problem
Plot a comparison between two experiments
Return options for multi line options
Forward computation
Compute the standard deviation of a p
Compute the features
Compute the features of the model
Apply layer reduction
Dump the contents of a given stream
Forward forward computation
Annotate the indices of x and y_values
Computes the gradient of the gradients
Returns a list of loaders
Forward the training function
Perform the forward computation
Bounded weight norm function
Set the current epoch

Get all kandi verified functions for this library.

convNet.pytorch Key Features

No Key Features are available at this moment for convNet.pytorch.

convNet.pytorch Examples and Code Snippets

No Code Snippets are available at this moment for convNet.pytorch.

Community Discussions

Trending Discussions on convNet.pytorch

AttributeError: 'Image' object has no attribute 'new' occurs when trying to use Pytorchs AlexNet Lighting preprocessing

QUESTION

AttributeError: 'Image' object has no attribute 'new' occurs when trying to use Pytorchs AlexNet Lighting preprocessing

Asked 2018-Aug-11 at 16:51

I tried to train my model on ImageNet using inception and Alexnet like preprocessing. I used Fast-ai imagenet training script provided script. Pytorch has support for inception like preprocessing but for AlexNets Lighting, we have to implement it ourselves :

...

ANSWER

Answered 2018-Aug-11 at 16:51

Thanks to @iacolippo's comment, I finally found the cause!

Unlike the example I wrote here, in my actual script, I had used transforms.ToTensor() after the lighting() method. Doing so resulted in a PIL image being sent as the input for lightining()which expects a Tensor and that's why the error occurs.

So basically the snippet I posted in the question is correct and .ToTensor has to be used prior to calling Lighting().

Source https://stackoverflow.com/questions/51685753

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

Vulnerabilities

No vulnerabilities reported

Install convNet.pytorch

You can download it from GitHub.
You can use convNet.pytorch 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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