Multi-Layer-Perceptron

by alexbrillant Python Version: Current License: Apache-2.0

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

kandi X-RAY | Multi-Layer-Perceptron Summary

Multi-Layer-Perceptron is a Python library. Multi-Layer-Perceptron 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.

Multi-Layer-Perceptron

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Multi-Layer-Perceptron has a low active ecosystem.

It has 2 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Multi-Layer-Perceptron has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Multi-Layer-Perceptron is current.

Quality

Multi-Layer-Perceptron has 0 bugs and 0 code smells.

Security

Multi-Layer-Perceptron has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Multi-Layer-Perceptron code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Multi-Layer-Perceptron is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

Multi-Layer-Perceptron 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.

It has 337 lines of code, 25 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Main function for wine quality .
Creates a tensorflow model .
Runs the trial .
Create a optimizer for the given step .
Handle the inverse transform .
Compute the loss of the loss function .
Transform the data_inputs .
Compute the precision of the input and expected outputs .
Compute the recall score .
Return a metric for classification .

Get all kandi verified functions for this library.

Multi-Layer-Perceptron Key Features

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Multi-Layer-Perceptron Examples and Code Snippets

No Code Snippets are available at this moment for Multi-Layer-Perceptron.

Community Discussions

Trending Discussions on Multi-Layer-Perceptron

Which parameter configuration is Keras using by default for predictions after training a model for multiple epochs

How many nodes in input and output layers of sklearn's MLPClassifier for MNIST digits classification task

QUESTION

Which parameter configuration is Keras using by default for predictions after training a model for multiple epochs

Asked 2022-Feb-04 at 11:06

I have a general question about Keras. When training a Artificial Neural Network (e.g. a Multi-Layer-Perceptron or a LSTM) with a split of training, validation and test data (e.g. 70 %, 20 %, 10 %), I would like to know which parameter configuration the trained model is eventually using for predictions?

Here I have an exmaple from a training process with 11 epoch:

I could think about 3 possible parameter configurations (surely there are also others):

The configuration that led to the lowest error in the training dataset (which would be after the 11th epoch)
The configuration after the last epoch (which would the after the 11th epoch, as in 1.)
The configuration that led to the lowest error in the validation dataset (which would be after the 3rd epoch)

If you just build the model without for example like this:

...

ANSWER

Answered 2022-Feb-04 at 11:06

It would be the configuration after the last epoch (the 2nd possible configuration that you have mentioned).

Source https://stackoverflow.com/questions/70984921

QUESTION

How many nodes in input and output layers of sklearn's MLPClassifier for MNIST digits classification task

Asked 2020-Jan-11 at 04:13

I am following the example on https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py and I'm trying to figure out if my understanding is correct on the number of nodes in the input and output layers in the example. The code required is as follows:

...

ANSWER

Answered 2020-Jan-11 at 04:13

Your understanding is correct. The image size of MNIST digits data is 28x28 which is flatten to 784 and output size is 10 (one for each number from 0 to 9). MLPClassifier implicitly designs the input and output layer based on the provided data in Fit method.

Your NN configuration will look like: Input: 200 x 784 Hidden layer: 784 x 50 (feature size: 200 x 50) Output layer: 50 x 10 (feature size: 200 x 10)

Batch size is 200 by default in MLPClassifier as the training data size is 60000

Source https://stackoverflow.com/questions/59691481

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

Vulnerabilities

No vulnerabilities reported

Install Multi-Layer-Perceptron

You can download it from GitHub.
You can use Multi-Layer-Perceptron 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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