## Getting weights from tensorflow.js neural network

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)

It seems like there is probably a simpler and cleaner way to do what you want, but regardless:

Calling `this.model.getWeights()`

will give you an array of Variables that correspond to layer weights and biases. Calling `data()`

on any of these array elements will return a promise that you can resolve to get the weights.

I haven't tried manually setting the weights, but there is a `this.model.setWeights()`

method.

Goodluck.

**Save and load models,** 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 Learn why Neural Networks need activation functions and how should you initialize their weights. - curiousily/Simple-Neural-Network-with-TensorFlow-js Build a simple Neural Network model in TensorFlow.js to make a laptop buying decision.

Here is a simple way to print off all the weights:

for (let i = 0; i < model.getWeights().length; i++) { console.log(model.getWeights()[i].dataSync()); }

**TensorFlow.js API,** A binary file carrying the weight values named [my-model].weights.bin . You can change the name [my-model] to get files with a different name. Because TL;DR Build a simple Neural Network model in TensorFlow.js to make a laptop buying decision. Learn why Neural Networks need activation functions and how should you initialize their weights. It is in the middle night, and you’re dreaming some rather alarming dreams with a smile on your face.

To access the weights (kernel and bias) of the first dense layer:

const model = tf.sequential(); model.add(tf.layers.dense({units: 4, inputShape: [8]})); model.add(tf.layers.dense({units: 4})); model.compile({ optimizer: 'sgd', loss: 'meanSquaredError' }); // kernel: model.layers[0].getWeights()[0].print() // bias: model.layers[0].getWeights()[1].print()

**Is there a way to access weights and biases in tfjs layers api ,** is there a way to access weights and biases of a layer in tfjs with the layers api? to get and also set them to use a generic algorithm on my neural net. subscribed to the Google Groups "TensorFlow.js Discussion" group. Build a simple Neural Network for Breast Cancer Detection using Tensorflow.js Alex Donea November 28, 2019 There's more and more research done on detecting all types of cancers in early stages and thus increasing probability of survival.

**Manually setting weights for model · Issue #465 · tensorflow/tfjs ,** TensorFlow.js version 0.11.6 Browser version Describe the problem or Is this the equivalent of getting each layer's weights individually and To get a deeper understanding of how neural networks work, and a broader understanding of how to apply them to different problems, the book Deep Learning with JavaScript is a great place to start. It is accompanied by a large number of examples from GitHub so you can practice working with machine learning in JavaScript.

**Build a simple Neural Network with TensorFlow.js,** Build a simple Neural Network model in TensorFlow.js to make a laptop This leads to Duration: 17:04
Posted: Jul 16, 2019 Get Started. TensorFlow.js is a JavaScript Library for training and deploying machine learning models in the browser and in Node.js. See the sections below for different ways you can get started.

**18 Tips for Training your own Tensorflow.js Models in the Browser,** However, with deep learning models running directly in the browser, we are of model weights down to a clients browser in a simple web application. tips to get started with training your own convolutional neural network Neural networks are trained by gradient descent. The weights in each layer begin with random values, and these are iteratively improved over time to make the network more accurate. A loss function is used to quantify how inaccurate the network is, and a procedure called backpropagation is used to determine whether each weight should be