NeuralNetworks | Neural Network that can detect | Machine Learning library

I am trying to define a function which gets some natural number n as input. Depending on this input the function should have different constraints. This constraints get calculate with a type family. The number has to be converted to GHC.TypeNats because the constraints are for Data.Vector.Sized. I asked a similar question here, but the answer won't work in the case of GHC.TypeNats and arbitrary n.

I tried the UNat type from Clash.

This is the relevant code from clash:

I have a list where I need each individual elements as a character.

I saw that TF Lite has a delegate that uses NNAPI for hardware acceleration. I was reading up on NNAPI and I saw that it has a compilation setting called ANEURALNETWORKS_PREFER_LOW_POWER, which compiles a given NNAPI model to prioritize low power consumption. Does TF Lite have a setting that utilizes this, or any, power-saving mode? I'm developing for mobile using TF Lite for Android on Android Studio.

I was working on a project where I was training a neural network and displaying information using a GUI in Python. However, I have a constant problem with importing modules. This is the error I get when trying to run Visualizer.Main

I'm building a Twitter bot and I use a random query term. I save the date and query term to a text file. I would like to check if the current query term is in the last 5 values of my memory dictionary.

I read the .txt file, split into key and values of a dictionary and I'm not sure what to do next. In psuedo code, I'd like to say query = d.values(last value in dictionary : or in the last 5 values) and then I'd pick a new query.

2019-09-23,computerscience 2019-09-24,python 2019-09-25,computerprogrammer 2019-09-26,computerscience 2019-09-27,AI 2019-09-28,machinelearning 2019-09-29,neuralnetworks 2019-09-30,computerscience

I'm trying to tackle the classic handwritten digit recognition problem with a feed forward neural network and backpropagation, using the MNIST dataset. I'm using Michael Nielsen's book to learn the essentials and 3Blue1Brown's youtube video for the backpropagation algorithm.

I finished writing it some time ago and been debugging since, because the results are quite bad. At its best the network can recognize ~4000/10000 samples after 1 epoch and that number only drops on the following epochs, which lead me to believe there's some issue with the backpropagation algorithm. I've been drowning in index hell trying to debug this for the last few days and can't figure out where the issue is, I'd appreciate any help in pointing it out.

A bit of background: 1) I'm not using any matrix multiplication and no external frameworks, but doing everything with for loops because that's how I learned it from the video. 2) Unlike the book, I'm storing both weights and biases in the same array. The biases for every layer are a column at the end of the weight matrix for that layer.

And finally for the code, this is the Backpropagate method of the NeuralNetwork class, which is called in UpdateMiniBatch, which itself is called in SGD:

I need help understanding venv and project management on a Windows 7 system.

I have installed Python 3.7.4. Running the command 'pip list' results in the following:

I am working on a small neural network evolutionary learning project. When I try to randomly mutate the weights and biases on multiple instances of my NeuralNetwork script, the scripts seem to affect each other for no apparent reason.

The two main scripts are the GameManager script which manages the learning process and many instances of the NeuralNetwork script which each have their own weights and biases.

The problem is that when a new generation is spawned and each NeuralNetwork script gets the same new weights and biases, which they later mutate randomly and uniquely using Mutate() function in the NeuralNetwork script, they all end up with the same weights and biases. I have checked that the Mutate() function works correctly and changes weights and biases independently for each NeuralNetwork instance, however they all get overridden by the "last" script instance to mutate.

I have tried to randomly delay the Mutate() function in each instance of NeuralNetwork, have tried not to instantiate the NeuralNetork script through a prefab, have tried to call Mutate() function from both the GameManager directly and the NeuralNetwork script using a "proxy" boolean. I have also tried to move the Mutate() function to the GameManager. I even tried turning off the GameManager on runtime right before the mutation. Nothing seems to help.

This is the function used to spawn NeuralNetwork at the start of runtime.

I am following the book, 'Make your own neural network' and https://sudeepraja.github.io/Neural/ . I am doing the neural network without numpy, scipy, scikitlearn etc. I am trying to check if my algorithm is correct by training the following network using same input-output combination multiple times. However, no matter how many steps I take or increase the loop counter, the network isn't learning the output at all.

I hope the code is readable as I am assuming that the code is transliteration of the github source.

Specifically the value of first output neuron never changes (ideally, it should converge to 0.01 but is stuck at 0.5). The code is available at http://tpcg.io/ruufxJ and presented below in case the link expires. I've also tried https://github.com/Horopter/NeuralNetworks-2018/blob/master/NeuralNetwork.py but the code is just wrong over there [doesn't scale up to multiple layers]