Sequential Neural Network

A generic N-dimensional tensor.

Activation functions are used to transform the output of a layer into a new output.

BackendType represents the type of backend to use.

Init represents the type of initialization to use.

Rank Types.

CPU Backend Type.

Web Assembly Backend Type.

Creates a convolutional layer.

Creates a dense layer.

Creates an Elu layer.

Creates a Flatten layer.

Creates a leaky relu layer.

Creates a pooling layer.

Creates a relu6 layer.

Creates a relu layer.

Creates a Selu layer.

setupBackend loads the backend and sets it up.

Creates a sigmoid layer.

Creates a softmax layer.

Creates a tanh layer.

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.

The Backend is responsible for eveything related to the neural network.

InitFn is a function that initializes a tensor.

Shape Interface

TensorData is the data type for the Tensor based on the backend.

1D Array.

2D Array.

3D Array.

4D Array.

5D Array.

6D Array.

Array Map Types.

Tensor for the CPU backend.

DataSet is a container for training data.

Tensor for the GPU backend.

NetworkConfig represents the configuration of a neural network.

1st dimentional shape.

2nd dimentional shape.

3th dimentional shape.

4th dimentional shape.

5th dimentional shape.

6th dimentional shape.