simple-neural-network | simple Python script showing how the backpropagation | Machine Learning library

by mattm Python Version: Current License: No License

X-Ray Key Features Code Snippets(2)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | simple-neural-network Summary

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

A simple Python script showing how the backpropagation algorithm works.

Support

Quality

Security

License

Reuse

Support

simple-neural-network has a medium active ecosystem.

It has 969 star(s) with 534 fork(s). There are 62 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 1098 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of simple-neural-network is current.

Quality

simple-neural-network has no bugs reported.

Security

simple-neural-network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

simple-neural-network 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

simple-neural-network releases are not available. You will need to build from source code and install.

simple-neural-network has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Train the model
Feed forward computation
Calculate the PD error wrt output
Calculates the total net input
Calculate the weight of a given index
Calculates the total error of each training set
Calculate the error
Calculate the outputs of each neuron
Calculate the output
Squared squash

Get all kandi verified functions for this library.

simple-neural-network Key Features

No Key Features are available at this moment for simple-neural-network.

simple-neural-network Examples and Code Snippets

First contact with Keras

pypi

Lines of Code : 37

License : No License

Copy

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

Auto-vectorization with

pypi

Lines of Code : 14

License : No License

Copy

def predict(params, input_vec):
  assert input_vec.ndim == 1
  activations = input_vec
  for W, b in params:
    outputs = jnp.dot(W, activations) + b  # `activations` on the right-hand side!
    activations = jnp.tanh(outputs)        # inputs to the

Community Discussions

Trending Discussions on simple-neural-network

How to test a trained Neural network in python?

Correct practice and approach for reporting the training and generalization performance

How much freedom does TensorFlow take away from pure Python?

uTensor model output does not equal to expected output

Code is not working after translating it from Python to Julia

Exporting a neural network created in Python to CoreML, is that possible?

Confusion in Creating Simple Neural Network in MATLAB

How is this simple Keras neural-network calculating result?

How to label test data using trained model with keras?

What does the derivative of the sigmoided output of an ANN tell me?

QUESTION

How to test a trained Neural network in python?

Asked 2020-Aug-25 at 11:10

I have trained a simple NN by modifying the following code

https://www.kaggle.com/ancientaxe/simple-neural-network-from-scratch-in-python

I would now like to test it on another sample dataset. how should i proceed with it ?

...

ANSWER

Answered 2020-Aug-25 at 11:10

I see you use a model from scratch. In this case, you should run this code, as indicated in the notebook, after setting your X and y for your new test set. For more information, see the the notebook as I did not put here everything:

Source https://stackoverflow.com/questions/63577390

QUESTION

Correct practice and approach for reporting the training and generalization performance

Asked 2020-Jan-27 at 08:33

I am trying to learn the correct procedure for training a neural network for classification. Many tutorials are there but they never explain how to report for the generalization performance. Can somebody please tell me if the following is the correct method or not. I am using first 100 examples from the fisheriris data set that has labels 1,2 and call them as X and Y respectively. Then I split X into trainData and Xtest with a 90/10 split ratio. Using trainData I trained the NN model. Now the NN internally further splits trainData into tr,val,test subsets. My confusion is which one is usually used for generalization purpose when reporting the performance of the model to unseen data in conferences/Journals? The dataset can be found in the link: https://www.mathworks.com/matlabcentral/fileexchange/71468-simple-neural-networks-with-k-fold-cross-validation-manner

...

ANSWER

Answered 2020-Jan-27 at 08:33

There are a few issues with the code. Let's deal with them before answering your question. First, you set a threshold of 0.5 for making decisions (Yhat_train = (train_predict >= 0.5);) while all points of your net prediction are above 0.5. This means you only get zeros in your confusion matrices. You can plot the scores to choose a better threshold:

Source https://stackoverflow.com/questions/59905814

QUESTION

How much freedom does TensorFlow take away from pure Python?

Asked 2019-Dec-17 at 19:38

I'm trying to get into ML and Deep Learning. It's been educational for me to start out with this in Python rather than a different language where I would may lose focus of exactly what is really going on. I've looked across the internet for tutorials on Neural Networks in Python and TensorFlow seems to be dominating the field, is this true? I enjoy doing things on my own (pure language) but I haven't seen a lot of tutorials that teach this that don't involve TensorFlow or some other library (Keras, Scikit-learn, etc.); so now I've decided to look into these.

The question I have is: does TensorFlow take away from pure Python?

For example, this code is from a tutorial and it creates a simple neural net that predicts what the output will be based on three numbers (NOTE: I haven't checked how this code trains and could probably make this better if I did I were to make it again myself):

...

ANSWER

Answered 2019-Dec-17 at 19:37

For anyone reading this I am going to share my thoughts. I chose to take the route of pure python with nothing like TensorFlow. I heard from other people that TensorFlow could take away an understanding that beginners need. At first, it was really hard for me to find anything that could help, but then I found everything all at once. The following links are the resources I used.

Theory:

The first 2-4 videos of this series by 3blue1brown: https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr

All of the videos in this series by 3blue1brown: https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

Implementation:

This video by the coding train: https://www.youtube.com/watch?v=jc2IthslyzM&list=PLRqwX-V7Uu6bCN8LKrcMa6zF4FPtXyXYj&index=8

Additional links for the calculus are: https://www.symbolab.com/cheat-sheets/Derivatives#, https://www.mathsisfun.com/calculus/derivatives-partial.html

And this entire series by the coding train: https://www.youtube.com/watch?v=XJ7HLz9VYz0&list=PLRqwX-V7Uu6Y7MdSCaIfsxc561QI0U0Tb

Summary

And that is it, that is all I really needed to get started it is awesome! One more thing I should note is that I eventually ended up using Processing.py, if you don't know what it is, it is not a library for neural networks, but instead allows for really easy graphics in python, there are functions like ellipse which just make a circle, this is the python implementation of what the coding train uses.

Source https://stackoverflow.com/questions/59042440

QUESTION

uTensor model output does not equal to expected output

Asked 2019-Sep-24 at 07:24

I'm currently working on a project where uTensor is required. uTensor seems to work correctly, however I ran into an issue which I (apparently) cannot fix myself.

Problem definition

I've created a simple python script which generates and saves a model to a file. This saved model can later be converted into C++ code by using uTensor-cli. The generated C++ code will be ran on a ARM dev board.

Everything runs fine, no errors. However, when I create a model like: "xW+b" the output of the model on the devboard always equals some static value, which is not equal to the output of the model from the python script.

The thing is, when a simple model like "W+b" is used (no input tensor used here) the output on the ARM devboard equals the output of the python script. And everything works as expected.

My findings

When using an input tensor (nothing large just a 1 dimensional array, like [1,0]) the ARM devboard always outputs some weird value compared to the output of the python script. When not using the input tensor, everything works as expected.

Other info

Because no wiki on uTensor exists yet, I've used a tutorial to learn about uTensor. The tutorial I've used can be found here: https://blog.hackster.io/simple-neural-network-on-mcus-a7cbd3dc108c The code I've written is based on the tutorial and does not include any cost/loss function and is not capable of 'learning' anything. The code is just for debugging.

The question

What is the reason an input tensor makes the application output unexpected values? How can I possibly fix this?

Code and output

Python script

...

ANSWER

Answered 2019-Sep-24 at 07:24

Changing the datatype in the C++ code to float did the trick! I'm still not quite sure how I did not think about this.

Source https://stackoverflow.com/questions/58061538

QUESTION

Code is not working after translating it from Python to Julia

Asked 2019-Apr-13 at 18:30

I'm new to the Julia programming language, and still learning it by writing code that I've already written in Python (or, at least, tried out in Python).

There is an article which explains how to make a very simple neural network: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1.

I tried the code in this article out in Python, at it's working fine. However, I haven't used linear algebra things in Python before (like dot). Now I'm trying to translate this code to Julia, but there are some things I can't understand. Here is my Julia code:

...

ANSWER

Answered 2019-Apr-13 at 18:30

Here is the code adjusted to Julia:

Source https://stackoverflow.com/questions/55664062

QUESTION

Exporting a neural network created in Python to CoreML, is that possible?

Asked 2019-Feb-05 at 16:55

Is that possible to export a neural network algorithm, like this one published by this guy to a CoreML model?

from numpy import exp, array, random, dot

...

ANSWER

Answered 2019-Feb-05 at 16:55

Yes, this is possible. You can use the NeuralNetworkBuilder class from coremltools for this.

Source https://stackoverflow.com/questions/54530191

QUESTION

Confusion in Creating Simple Neural Network in MATLAB

Asked 2018-Oct-01 at 16:42

I was trying to create a Simple Neural Network through MATLAB (reference https://becominghuman.ai/making-a-simple-neural-network-2ea1de81ec20, although the author has coded in JavaScript, I wanted to do the same using MATLAB). I created my own MATLAB Live Script, but I am really confused as to my weights vector I created does not update. I am trying to add a learning rate of 0.20 to the weights(3) element, so as to make it reach 1 (I am trying 6 trials for training the network). I am new to using MATLAB and generally code in Python, so if I the mistake I am making/the thing I am missing is kindly explained or what line of code is wrong, I would be grateful. Thanks a lot!

Here is my piece of code:-

...

ANSWER

Answered 2018-Sep-30 at 16:07

Sounds like you missed a loop in the learn function, double check with the original article.

Source https://stackoverflow.com/questions/52578672

QUESTION

How is this simple Keras neural-network calculating result?

Asked 2018-Jul-26 at 05:02

I'm trying to understand how a simple forward neural network works... starting off with the example here I have simplified it to make a trainer that generally gives a 100% accurate "AND" neuron:

...

ANSWER

Answered 2018-Jul-26 at 05:02

The result you see in your print statements are after the softmax operation.

[1 1] . [-.70 .77; -.64 .81] + [1.9 -0.62] = [.53 .96]

Then

exp(.53) = 1.69, exp(.96) = 2.62

so result is

[1.69/(1.69+2.62) 2.62/(1.69+2.62)] = [.39 .61]

(obviously with rounding errors)

Also note that before taking the softmax technically you have a relu activation, but since that is the identity for positive numbers, it doesn't have an effect for the [1 1] example. It would make a difference for the [1 0] example, as the second component of your Wx + b is negative, which would then be zeroed out.

Source https://stackoverflow.com/questions/51530846

QUESTION

How to label test data using trained model with keras?

Asked 2017-Oct-25 at 07:07

I am working on the following keras convolutional neural network tutorial https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d

After training the model I want to test the model on sample images, and also label the images. I realize that I have to use predict method that generates an array that shows which label gets what score for a particular image. But I am having trouble using this method. If the images are in the folder test_images and there are 20 of them, how do I test these images and get the prediction?

This is how I've gotten with one image (even though I want it for multiple images):

...

ANSWER

Answered 2017-Oct-25 at 07:07

model.predict works by processing an array of samples, not just one image, so you are missing the batch/samples dimension, which in your case would only be just one image. You just have to reshape the array:

Source https://stackoverflow.com/questions/46923704

QUESTION

What does the derivative of the sigmoided output of an ANN tell me?

Asked 2017-Oct-19 at 18:17

I got really interested in artificial neural networks (ANN) and red into it. But there is one thing i just can't seem to figure out.

When evaluating by how much a weight should be adjusted to reduce the error the ANN makes into account:

the error (obviously), *
the input (self-explanatory to me as well) *
and the derivative of the sigmoid of the output (???).

But how does the last point even matter? I know it's supposed to represent how confident the ANN is with this particular weight, but isn't the error the key to how much the weight should be adjusted? Why would I care about how confident my ANN is with this weight if the output is just wrong?

I got my insights (and my confusion) from this post.

...

ANSWER

Answered 2017-Feb-01 at 17:55

Intuitions about how something works are in generally right, but in specifics they might not be right. This is the case for your intuitions of the error function, which are not right. Let's remember how the error is constructed, the output of a single neuron is:

Source https://stackoverflow.com/questions/41982174

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

Vulnerabilities

No vulnerabilities reported

Install simple-neural-network

You can download it from GitHub.
You can use simple-neural-network 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.