simple-neural-network | Python implementation of a simple neural network | Machine Learning library

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

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):

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.

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.

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.

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.

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

Python script

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:

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

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:-

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:

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):

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.