scikit-neuralnetwork | Deep neural networks without the learning cliff | Machine Learning library

by aigamedev Python Version: 0.7 License: BSD-3-Clause

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

kandi X-RAY | scikit-neuralnetwork Summary

scikit-neuralnetwork is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. scikit-neuralnetwork has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However scikit-neuralnetwork has 3 bugs. You can install using 'pip install scikit-neuralnetwork' or download it from GitHub, PyPI.

Deep neural networks without the learning cliff! Classifiers and regressors compatible with scikit-learn.

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Quality

Security

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scikit-neuralnetwork has a medium active ecosystem.

It has 1204 star(s) with 229 fork(s). There are 75 watchers for this library.

It had no major release in the last 12 months.

There are 47 open issues and 126 have been closed. On average issues are closed in 7 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of scikit-neuralnetwork is 0.7

Quality

scikit-neuralnetwork has 3 bugs (2 blocker, 0 critical, 1 major, 0 minor) and 65 code smells.

Security

scikit-neuralnetwork has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

scikit-neuralnetwork code analysis shows 0 unresolved vulnerabilities.

There are 4 security hotspots that need review.

License

scikit-neuralnetwork is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

scikit-neuralnetwork releases are available to install and integrate.

Deployable package is available in PyPI.

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

scikit-neuralnetwork saves you 1220 person hours of effort in developing the same functionality from scratch.

It has 2748 lines of code, 313 functions and 32 files.

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

Top functions reviewed by kandi - BETA

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

Extract an attribute .

Get all kandi verified functions for this library.

scikit-neuralnetwork Key Features

No Key Features are available at this moment for scikit-neuralnetwork.

scikit-neuralnetwork Examples and Code Snippets

Error during building of exe using cx_freez

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

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from PyQt5.QtGui import QIcon

Back propagation and Structure of a Neural Network in scikit-neuralnetwork

Python

Lines of Code : 18

License : Strong Copyleft (CC BY-SA 4.0)

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nn.fit(X_train, y_train)

y_train = [0,0,0,1,2,]
X_train = [[ 7.1  3.   5.9  2.1]
           [ 5.9  3.   4.2  1.5]
           [ 5.5  2.4  3.7  1. ]
           [ 6.1  2.8  4.7  1.2]
           [ 5.   2.3  3.3  1. ]]

Community Discussions

Trending Discussions on scikit-neuralnetwork

How can I fix an failed install of sklearn for python

Back propagation and Structure of a Neural Network in scikit-neuralnetwork

QUESTION

How can I fix an failed install of sklearn for python

Asked 2020-Aug-22 at 01:00

I am trying to install sklearn for Python, however whenever I attempt to install something which has files from it as a requirement (such as scikit-neuralnetwork) or I attempt to import sklearn in a Python file, I get errors. In the first scenario I receive the error message below marked A, and for the second I receive an error saying I have no module named sklearn.utils (I've already commented out the correct install check). I've tried reinstalling the libraries but the issue remains the same.

ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: 'C:\Users\Charles\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\sklearn\datasets\tests\data\openml\292\api-v1-json-data-list-data_name-australian-limit-2-data_version-1-status-deactivated.json.gz'

...

ANSWER

Answered 2020-Aug-22 at 01:00

Download Anaconda. It has all the libraries already downloaded.

Source https://stackoverflow.com/questions/63528167

QUESTION

Back propagation and Structure of a Neural Network in scikit-neuralnetwork

Asked 2017-Feb-17 at 03:59

I am trying to learn Neural Networks using scikit-neuralnetwork framework and I know basics about Neural Networks and now trying to implement it with scikit-learn. but I am confused on 2 points.

1- what is the structure of this NN given below? Somehow, in some examples felt to me, some people don't put input layer as a layer. Otherwise, I am thinking this as a 2 layer NN has input layer with 100 nodes and 1 node at the ouput layer.

...

ANSWER

Answered 2017-Feb-08 at 11:36

1. Assuming that each training example in X_train has M features, and there are C classes in y_train: The input layer (not explicitly shown in the code) has M nodes. The hidden layer has 100 nodes. The output layer has C nodes (each one encoding the score for each class).

2. .fit() is a method that does that - feeds forward the training examples and uses back propagation to train the NN.

Also: perhaps you have to add units=C for the final layer - I assume this is a classification problem. If you need one value only (a score, not a class label), then use Regressor.

Source https://stackoverflow.com/questions/42098700

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

Vulnerabilities

No vulnerabilities reported

Install scikit-neuralnetwork

You can install using 'pip install scikit-neuralnetwork' or download it from GitHub, PyPI.
You can use scikit-neuralnetwork 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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