tensorflow.js

I have 2 Tensors of same length, data and groupIds . I want to split data into several groups by the corresponding values in groupId . For example, const data = tf.tensor([1,2,3,4,5]); const groupIds = tf.tensor([0,1,1,0,0]); // expected result: [tf.tensor([1,4,5]), tf.tensor([2,3])] In Tensorflow there is tf.dynamic_partition which does exactly that. Tensorflow.js doesn't seem to have a similar method. I also looked into mask or filtering as work-arounds, but they don't exist either. Does anyone have an idea how to implement this? To partition your tensor, you can first iterate over your ids

need help with async/await. currently studying https://github.com/tensorflow/tfjs-converter . and I'm stumped at this part of the code (loading my python converted saved js model for use in the browser): import * as tf from '@tensorflow/tfjs'; import {loadFrozenModel} from '@tensorflow/tfjs-converter'; /*1st model loader*/ const MODEL_URL = './model/web_model.pb'; const WEIGHTS_URL = '.model/weights_manifest.json'; const model = await loadFrozenModel(MODEL_URL, WEIGHTS_URL); /*2nd model execution in browser*/ const cat = document.getElementById('cat'); model.execute({input: tf.fromPixels(cat)}

I have this sequential model: this.model = tf.sequential() this.model.add(tf.layers.dense({units : 16, useBias : true, inputDim : 7})) // input this.model.add(tf.layers.dense({units : 16, useBias : true, activation: 'sigmoid'})) // hidden this.model.add(tf.layers.dense({units : 3, useBias : true, activation: 'sigmoid'})) // hidden 2 I checked API for tensorflow.js, but there's nothing about getting weights(kernels) of neural network. So, how can I get weights and then change them, to apply new weights?(for unsupervised learning) Dhruv Guliani It seems like there is probably a simpler and

I used tf.expandDims() to add dimensions. Since I'm able to go into model.fit(), but got stuck due to this error Cannot start training because another fit() call is ongoing. and Cannot read property 'length' of undefined. You can find my code here // Train the model using the data. let tesnor_dim =[]; let tensr;for(var j=0; j<2; j++){ console.log('resize_image',resize_image); tensr = tf.expandDims(ysarr[j], 0); tesnor_dim.push(tensr); console.log('tesnor_dim',tesnor_dim); model.fit(resize_image[j], tesnor_dim[j], {epochs: 100}).then((loss) => { console.log('resize_image[j]',resize_image[j]);

I've trained a model with Azure Custom Vision and downloaded the TensorFlow files for Android (see: https://docs.microsoft.com/en-au/azure/cognitive-services/custom-vision-service/export-your-model ). How can I use this with tensorflow.js ? I need a model (pb file) and weights (json file). However Azure gives me a .pb and a textfile with tags. From my research I also understand that there are also different pb files, but I can't find which type Azure Custom Vision exports. I found the tfjs converter . This is to convert a TensorFlow SavedModel (is the *.pb file from Azure a SavedModel?) or

I am trying to train a Tensor-flow js model on images coming in from my web cam. Basically I'm trying to recreate the pac-man tensor-flow game. The model isn't converging and is pretty much useless after training. I have a feeling its how I'm prepping the data. Grabbing the image from the canvas function takePhoto(label) { let canv = document.getElementById("canv") let cont = canv.getContext("2d") cont.drawImage(video, 0, 0, width, height) let data = tf.browser.fromPixels(canv, 3) data.toFloat().div(tf.scalar(127)).sub(tf.scalar(1)) return data } function addExample(label){ let data =