dlnetwork layers in javascript

To create deep learning neural network models in JavaScript, you can use the dlnet package in the TensorFlow.js library.

Here is an example of how to create layers for a neural network using the dlnet package:

index.tsx
const dl = require('dlnet'); // import dlnet package

const inputShape = [28, 28, 1]; // specify shape of input data

// create convolutional layer
const convLayer = dl.layers.conv2d({
  inputShape: inputShape,
  kernelSize: 3,
  filters: 16,
  strides: 1,
  activation: 'relu'
});

// create flatten layer
const flattenLayer = dl.layers.flatten();

// create dense layer
const denseLayer = dl.layers.dense({
  units: 10,
  activation: 'softmax'
});

// create output layer
const outputLayer = dl.layers.outputLayer();

// build model using created layers
const model = new dl.Model({
  inputs: convLayer.input,
  outputs: outputLayer.apply(denseLayer.apply(flattenLayer.apply(convLayer.output)))
});
701 chars
31 lines

This code creates four layers: a convolutional layer, a flatten layer, a dense layer, and an output layer. The conv2d function creates a convolutional layer with a specified input shape, kernel size, number of filters, stride, and activation function. The flatten function creates a layer that flattens the input tensor to 1D. The dense function creates a densely connected layer with a specified number of units and activation function. The outputLayer function creates a layer that applies softmax activation to the output tensor. Finally, the Model class constructs the full model using the created layers.

Note that this is just a simple example and you can use other types of layers and customize the parameters according to your specific needs.