Incremental-Learning | Incremental Learning with Adaptive Resonance | Machine Learning library

by Uehwan Python Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Incremental-Learning Summary

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

Incremental Learning with Adaptive Resonance Theory (ART) & Developmental Resonance networks.

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Quality

Security

License

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Support

Incremental-Learning has a low active ecosystem.

It has 8 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Incremental-Learning has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Incremental-Learning is current.

Quality

Incremental-Learning has 0 bugs and 0 code smells.

Security

Incremental-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Incremental-Learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Incremental-Learning 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

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

Incremental-Learning 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.

Top functions reviewed by kandi - BETA

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

Train the model
Add a category
Calculate the distance between two points
Calculate rrn activation
Compute the prediction with the given sequence
Increase the input field
Expand a weight matrix
Encode a sequence of indices
Compute a prediction for a given sequence
Read the episode from the given index
Encodes a sequence of indices
Decode a sequence into a sequence of integers
Read the event from the episode layer
Read out the given index
Read out event at given index
Decode a sequence of integers to a sequence of integers
Make 2D sequence data
Make data for clustering
Make the data for clustering
Return a numpy array of categories
Compute the label for each sample
Compute purity score
Generate synthetic data
Read episode from episode layer

Get all kandi verified functions for this library.

Incremental-Learning Key Features

No Key Features are available at this moment for Incremental-Learning.

Incremental-Learning Examples and Code Snippets

First contact with Keras

pypi

Lines of Code : 37

License : No License

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from tensorflow.keras.models import Sequential

model = Sequential()


from tensorflow.keras.layers import Dense

model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=10, activation='softmax'))


model.compile(loss='categorical_crossen

Community Discussions

Trending Discussions on Incremental-Learning

How can update trained IsolationForest model with new datasets/datafarmes in python?

Incremental learning in keras

QUESTION

How can update trained IsolationForest model with new datasets/datafarmes in python?

Asked 2022-Mar-02 at 20:42

Let's say I fit IsolationForest() algorithm from scikit-learn on time-series based Dataset1 or dataframe1 df1 and save the model using the methods mentioned here & here. Now I want to update my model for new dataset2 or df2.

My findings:

this workaround about Incremental learning from sklearn:

...learn incrementally from a mini-batch of instances (sometimes called “online learning”) is key to out-of-core learning as it guarantees that at any given time, there will be only a small amount of instances in the main memory. Choosing a good size for the mini-batch that balances relevancy and memory footprint could involve tuning.

but Sadly IF algorithm doesn't support estimator.partial_fit(newdf)

auto-sklearn offers refit() is also not suitable for my case based on this post.

How I can update the trained on Dataset1 and saved IF model with a new Dataset2?

...

ANSWER

Answered 2022-Mar-02 at 17:41

You can simply reuse the .fit() call available to the estimator on the new data.

This would be preferred, especially in a time series, as the signal changes and you do not want older, non-representative data to be understood as potentially normal (or anomalous).

If old data is important, you can simply join the older training data and newer input signal data together, and then call .fit() again.

Also sidenote, according to sklearn documentation, it is better to use joblib than pickle

An MRE with resources below:

Source https://stackoverflow.com/questions/71326545

QUESTION

Incremental learning in keras

Asked 2020-Nov-12 at 00:45

I am looking for a keras equivalent of scikit-learn's partial_fit : https://scikit-learn.org/0.15/modules/scaling_strategies.html#incremental-learning for incremental/online learning.

I finally found the train_on_batch method but I can't find an example that shows how to properly implement it in a for loop for a dataset that looks like this :

...

ANSWER

Answered 2020-Nov-12 at 00:45

You should feed your data batch-wise. You are giving a single instance but model expecting batch data. So, you need to expand the input dimension for batch size.

Source https://stackoverflow.com/questions/63907380

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

Vulnerabilities

No vulnerabilities reported

Install Incremental-Learning

You can download it from GitHub.
You can use Incremental-Learning 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

Episodic memory incrementally learns user behaviors and event sequences. However, conventional episodic memory fails to stably perform over a long period of time. In addition, they cannot not accept user feedback. The proposed SF-EM stably performs over a long period of time and accepts user feedback. The following are the key features of SF-EM:.

Find more information at: