Meta-Graph | Meta-Learning for Few Shot Link Prediction | Machine Learning library

by joeybose Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | Meta-Graph Summary

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

Meta-Learning for Few Shot Link Prediction

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Quality

Security

License

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Support

Meta-Graph has a low active ecosystem.

It has 26 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 3 have been closed. On average issues are closed in 8 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Meta-Graph is current.

Quality

Meta-Graph has 0 bugs and 0 code smells.

Security

Meta-Graph has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Meta-Graph code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Meta-Graph 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

Meta-Graph releases are not available. You will need to build from source code and install.

Meta-Graph 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.

Meta-Graph saves you 1678 person hours of effort in developing the same functionality from scratch.

It has 3721 lines of code, 156 functions and 16 files.

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

Top functions reviewed by kandi - BETA

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

Perform a meta gradient step
Split edges into upper triangular edges
Performs negative sampling
Calculates the loss of the decoder
Perform a test
Performs a meta gradient step
Calculate ADAR score
Calculates the DeepWalk score
Resets the parameters
Process a single line
Generate random embeddings
Propagate the forward function
Check if graph is present in graph
Process files
Train the model
Compute the loss function
Sets random seed
Encodes the model
Get gradients data
Forward function
Get data
Perform validation
Run the analysis
This function is used to plot the plot
Load a dataset
Plot a figure

Get all kandi verified functions for this library.

Meta-Graph Key Features

No Key Features are available at this moment for Meta-Graph.

Meta-Graph Examples and Code Snippets

No Code Snippets are available at this moment for Meta-Graph.

Community Discussions

Trending Discussions on Meta-Graph

How to create a meta graph in Azure Cosmos DB with Gremlin API

Loading Tensorflow Graph in other file not giving the same accuracy

C++ parameter match/deducing

Weights in tensorflow model don't seem to change when printed

In Tensorflow, how to use a restored meta-graph if the meta graph was feeding with TFRecord input (without placeholders)

QUESTION

How to create a meta graph in Azure Cosmos DB with Gremlin API

Asked 2020-Jan-23 at 21:25

I am trying to figure out how to create a meta model for a graph database on Azure Cosmos DB using the Gremlin API, such as the meta graph in neo4j, but I haven't been able to find a way so far.

I want to be able to see the entities of my database as nodes, and the relationships among them as edges, without having to load any data yet (so that I can map these nodes and edges programmatically to the data sources, and the sources are only called -and the data loaded- when there is a matching query).

The only information that's relatively close to this that I've managed to find, is about visualizing the whole graph but not its meta structure (although even this seems to not be possible yet, or only possible through external visualization platforms).

Is it actually possible to do so? Or Cosmos DB being a schema-free database means that it indeed isn't?

...

ANSWER

Answered 2020-Jan-23 at 21:25

There isn't a way to specify a meta-graph in Azure Cosmos DB's Gremlin API - usually Azure Data Factory, or other application-level solutions are recommended.

Source https://stackoverflow.com/questions/59862023

QUESTION

Loading Tensorflow Graph in other file not giving the same accuracy

Asked 2018-Sep-05 at 22:18

I trained a CNN in Tensorflow and it tested with 92% accuracy. I saved it as a typical ckpt file.

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ANSWER

Answered 2018-Sep-05 at 22:18

You are assigning the wrong method to saver. From the TF Guide you can see that you want to init session and then upload through tensorflow.train.Saver().

Source https://stackoverflow.com/questions/52186812

QUESTION

C++ parameter match/deducing

Asked 2018-Jun-04 at 07:16

I read some code that I quite don't understand in Android Oreo sourcecode.

First, class IOMXNode has a function:

...

ANSWER

Answered 2018-Jun-04 at 06:18

When a copy constructor is defined with just one argument, it is a converting constructor.

From cpp reference

It is said that a converting constructor specifies an implicit conversion from the types of its arguments (if any) to the type of its class

So when you pass mem to useBuffer as second parameter, it is being converted to OMXBuffer& by using the converting constructor.

Source https://stackoverflow.com/questions/50674282

QUESTION

Weights in tensorflow model don't seem to change when printed

Asked 2018-Mar-28 at 20:08

I'm trying to print weights before and after training in Tensorflow. I'm confused about what I get because weights don't seem to change even if training shows a decreasing cost. My code is:

...

ANSWER

Answered 2018-Mar-28 at 19:51

Your weight has changed in your training. I'm not sure but I think you didn't see it because you just printed out parts of the weight and found that those parts are the same. I changed your code a little bit to add numpy.array_equal comparison and add checking in the training loop as follow:

Source https://stackoverflow.com/questions/49540395

QUESTION

In Tensorflow, how to use a restored meta-graph if the meta graph was feeding with TFRecord input (without placeholders)

Asked 2018-Jan-12 at 06:03

I trained a network with TFRecord input pipeline. In other words, there was no placeholders. Simple example would be:

...

ANSWER

Answered 2017-Jun-27 at 22:48

You can build a graph that uses placeholder_with_default() for the inputs, so can use both TFRecord input pipeline as well as feed_dict{}.

An example:

Source https://stackoverflow.com/questions/44769126

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

Vulnerabilities

No vulnerabilities reported

Install Meta-Graph

You can download it from GitHub.
You can use Meta-Graph 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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