I am using a teachable machine to do my wake word recognition, which runs in electron. Now the model works fine, but I want to visualize it. I have seen tutorials on how to visualize TensorFlow ,Keras models etc.. But what to do for a teachable machine model.
Related
I'm looking for services or scripts that can help to generate images for computer vision machine learning tasks. Not something like this where they just put together several layers of objects over other objects but more a like a 3D generation of objects in different environments.
I'm not aware of an out-of-the-box solution where you can create arbitrary objects and environments but if you are fine using Blender, here is an example how you can use Blender to automatically create images as a training set for machine learning algorithms.
I have seen various methods of deploying machine learning models on the cloud, but I just wanted to know if we can create a program that uses machine learning not from the cloud but instead uses the model trained on the same computer. What I mean is can I create a simple Tic-Tac-Toe game where the computer uses machine learning to play against the human player, with both the game file and the model residing on the same computer, one communicating with the other?
Yep! You can. It’s called reinforcement learning. There are some pretty good tutorials about it here:
https://github.com/simoninithomas/Deep_reinforcement_learning_Course
I have a project which I should classify the data coming from several sensors(time series based data) like gyroscope to several classes. I have used several classifiers including SVM, decision tree, neural networks, KNN,... in a batch scenario. My ultimate goal is to find a real-time classifier which is accurate, light and also has the ability to improve itself to implement it on my device which has limited sources(CPU, RAM,..). I was thinking a semi-supervised classifier since I can save a few labeled data on my device and use the future data points to improve my classifier. Does anyone have any recommendation or experience in this regard?
Online learning is very challenging. I recommend you steer away from now and use batch learning. You can always update the model as you update the mobile app or just make the app look for a new updated model on your server every x days.
Now, how to run a machine learning algorithm efficiently on a phone with limited resources. First, you have to identify which platform you are using. I assume you want to get a platform agnostic answer. Most ML algorithms (except lazy learning ones) can run efficiently on smartphone, have a look at this benchmarking experiment.
You have several options here:
iOS: Here's a list of all machine learning libraries available publicly.
Android: Weka for Android, this lib has a huge number of ML algorithms.
Platform agnostic deep learning: Tensorflow, you can export your models to TensorFlow lite (tutorial) and deploy them on any mobile OS and Caffe2 to train deep learning models and export them to any smartphone OS.
Currently I'm still working on that age classification project using the Convolutional Neural Network built in Tensorflow. How, and in what format, do I save the state of my current model trained in my PC to be able to use said model in the Tensorflow in my mobile app, or even OpenCV's tiny-dnn (dnn_modern)? Because I'm not sure if the checkpoint file would work in OpenCV's tiny-dnn.
I'm using a bunch of images to train my Tensorflow Image recognition project using this tutorial https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#4
Actually I need a lot of Cpu to train my model and it takes a lot of time on my laptop.
I have registered a Google ML account and started this tutorial:
https://cloud.google.com/ml/docs/quickstarts/training
Everything is set up and running but this is for mnist sample code. There is no image_retraining sample code like the retrain.py from tensorflow.
Looking for some examples on how to to run the Tensorflow Image Recognition script retrain in Google ML.