import * as netsaur from "https://deno.land/x/netsaur@0.4.0-patch/packages/core/mod.ts";
Classes
Sequential Neural Network | |
A generic N-dimensional tensor. |
Enums
Activation functions are used to transform the output of a layer into a new output. | |
BackendType represents the type of backend to use. | |
E Cost | |
E Init | Init represents the type of initialization to use. |
E Rank | Rank Types. |
Variables
v AUTO | The AUTO backend is chosen automatically based on the environment. |
v CPU | CPU Backend written in Rust. |
v GPU | GPU Backend written in Rust. |
v WASM | Web Assembly Backend written in Rust & compiled to Web Assembly. |
Functions
Creates an average pooling layer. Pooling layers are used for downsampling. See https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer | |
Creates a BatchNorm1D layer. BatchNorm1D layers normalize the input. They are usually used to improve the speed, performance, and stability of neural networks. See https://en.wikipedia.org/wiki/Batch_normalization | |
Creates a BatchNorm2D layer. BatchNorm2D layers normalize the input. They are usually used to improve the speed, performance, and stability of neural networks. See https://en.wikipedia.org/wiki/Batch_normalization | |
Creates a convolutional layer. Convolutional layers are used for feature extraction. They are commonly used in image processing. See https://en.wikipedia.org/wiki/Convolutional_neural_network | |
Creates a dense layer (also known as a fully connected layer). Dense layers feed all outputs from the previous layer to all its neurons, each neuron providing one output to the next layer. See https://en.wikipedia.org/wiki/Feedforward_neural_network#Fully_connected_network | |
Creates a dropout layer. Dropout is a regularization technique for reducing overfitting. The technique temporarily drops units (artificial neurons) from the network, along with all of those units' incoming and outgoing connections. See https://en.wikipedia.org/wiki/Dropout_(neural_networks) | |
Creates a dropout layer. Dropout is a regularization technique for reducing overfitting. The technique temporarily drops units (artificial neurons) from the network, along with all of those units' incoming and outgoing connections. See https://en.wikipedia.org/wiki/Dropout_(neural_networks) | |
Creates an Elu layer. Elu layers use the elu activation function. | |
Creates a Flatten layer. Flatten layers flatten the input. They are usually used to transition from convolutional layers to dense layers. | |
Creates a leaky relu layer. Leaky relu layers use the leaky relu activation function. | |
Creates a max pooling layer. Pooling layers are used for downsampling. See https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer | |
The OPTION function is used to choose a backend from a list of options. | |
Creates a pooling layer. Pooling layers are used for downsampling. See https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer | |
Creates a relu6 layer. Relu6 layers use the relu6 activation function. | |
Creates a relu layer. Relu layers use the relu activation function. | |
Creates a Selu layer. Selu layers use the selu activation function. | |
setupBackend loads the backend and sets it up. | |
Creates a sigmoid layer. Sigmoid layers use the sigmoid activation function. See https://en.wikipedia.org/wiki/Sigmoid_function | |
Creates a softmax layer. Softmax layers are used for classification. See https://en.wikipedia.org/wiki/Softmax_function | |
Creates a tanh layer. Tanh layers use the tanh activation function. | |
Create an nth rank tensor from the given nthD array and shape. | |
Create a 1D tensor from the given 1D array. | |
Create a 2D tensor from the given 2D array. | |
Create a 3D tensor from the given 3D array. | |
Create a 4D tensor from the given 4D array. | |
Create a 5D tensor from the given 5D array. | |
Create a 6D tensor from the given 6D array. |
Interfaces
The Backend is responsible for eveything related to the neural network. | |
Base Neural Network Structure. All Neural Networks should implement this. |
Type Aliases
1D Array. | |
2D Array. | |
3D Array. | |
4D Array. | |
5D Array. | |
6D Array. | |
Array Map Types. | |
DataSet is a container for training data. | |
NetworkConfig represents the configuration of a neural network. | |
Shape Type | |
1st dimentional shape. | |
2nd dimentional shape. | |
3th dimentional shape. | |
4th dimentional shape. | |
5th dimentional shape. | |
6th dimentional shape. | |