cnaps | Flexible Multi-Task Classification Using Conditional Neural | Machine Learning library

by cambridge-mlg Python Version: Current License: MIT

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

kandi X-RAY | cnaps Summary

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

This repository contains the code to reproduce the few-shot classification experiments carried out in Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes and TASKNORM: Rethinking Batch Normalization for Meta-Learning. The code has been authored by: John Bronskill, Jonathan Gordon, and James Reqeima.

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Support

cnaps has a low active ecosystem.

It has 105 star(s) with 16 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 12 have been closed. On average issues are closed in 9 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cnaps is current.

Quality

cnaps has no bugs reported.

Security

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

License

cnaps 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

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

cnaps has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Run training
Runs the test set
Save checkpoint
Prepare a task
Forward the classification
Returns the classifier params
Extract the indices of the given class
Compute the mean pooling for each class
Perform forward computation
Compute the mean and variance of x
Helper function to normalize data
Compute the weighted mean and variance of x
Construct a film resnet model
Get a normalization layer
Retrieve log files for training
Verify that the checkpoint directory exists
Forward convolution
Internal film function
Generate a list of hyperparameters
Get layer output
Process a numpy ndist file
Process a CIFAR file
Create a single layer
Construct a ResNet - 18 model
Load checkpoint
Register extra parameters

Get all kandi verified functions for this library.

cnaps Key Features

No Key Features are available at this moment for cnaps.

cnaps Examples and Code Snippets

No Code Snippets are available at this moment for cnaps.

Community Discussions

Trending Discussions on cnaps

Can't figure out where my routing error is coming from

QUESTION

Can't figure out where my routing error is coming from

Asked 2017-Dec-08 at 14:34

So I am trying to implement a polymorphic association for the first time and I'm running into a little bit of trouble.

I am trying to allow users to leave a note on a contact or organization. But after I submit a note I run into a routing error.

Here is the error I'm receiving (image)

Here are my routes:

Here are my routes (screenshot)

Here is my routes.rb file:

...

ANSWER

Answered 2017-Dec-08 at 14:34

Try changing this:

Source https://stackoverflow.com/questions/47708571

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

Vulnerabilities

No vulnerabilities reported

Install cnaps

Clone or download this repository.
Configure Meta-Dataset: Follow the the "User instructions" in the Meta-Dataset repository (https://github.com/google-research/meta-dataset) for "Installation" and "Downloading and converting datasets". This will take some time.
Install additional test datasets (MNIST, CIFAR10, CIFAR100): Change to the $DATASRC directory: cd $DATASRC Download the MNIST test images: wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz Download the MNIST test labels: wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz Download the CIFAR10 dataset: wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz Extract the CIFAR10 dataset: tar -zxvf cifar-10-python.tar.gz Download the CIFAR100 dataset: wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz Extract the CIFAR10 dataset: tar -zxvf cifar-100-python.tar.gz Change to the cnaps/src directory in the repository. Run: python prepare_extra_datasets.py