package owl-base

You can search for identifiers within the package.

in-package search v0.2.0

On This Page

Init neuron
Input neuron
Activation neuron
Linear neuron
LinearNoBias neuron
Recurrent neuron
LSTM neuron
GRU neuron
Conv1D neuron
Conv2D neuron
Conv3D neuron
DilatedConv1D neuron
DilatedConv2D neuron
DilatedConv3D neuron
TransposeConv1D neuron
TransposeConv2D neuron
TransposeConv3D neuron
FullyConnected neuron
MaxPool1D neuron
MaxPool2D neuron
AvgPool1D neuron
AvgPool2D neuron
GlobalMaxPool1D neuron
GlobalMaxPool2D neuron
GlobalAvgPool1D neuron
GlobalAvgPool2D neuron
UpSampling1D neuron
UpSampling2D neuron
UpSampling3D neuron
Padding1D neuron
Padding2D neuron
Padding3D neuron
Lambda neuron
LambdaArray neuron
Dropout neuron
Reshape neuron
Flatten neuron
Add neuron
Mul neuron
Dot neuron
Max neuron
Average neuron
Concatenate neuron
Normalisation neuron
GaussianNoise neuron
GaussianDropout neuron
AlphaDropout neuron
Embedding neuron
Masking neuron
Core functions

Module `Graph.Neuron`

module Optimise : Owl_optimise_generic_sig.Sig

Init neuron

module Init : sig ... end

Input neuron

module Input : sig ... end

Activation neuron

module Activation : sig ... end

Linear neuron

module Linear : sig ... end

LinearNoBias neuron

module LinearNoBias : sig ... end

Recurrent neuron

module Recurrent : sig ... end

LSTM neuron

module LSTM : sig ... end

GRU neuron

module GRU : sig ... end

Conv1D neuron

module Conv1D : sig ... end

Conv2D neuron

module Conv2D : sig ... end

Conv3D neuron

module Conv3D : sig ... end

DilatedConv1D neuron

module DilatedConv1D : sig ... end

DilatedConv2D neuron

module DilatedConv2D : sig ... end

DilatedConv3D neuron

module DilatedConv3D : sig ... end

TransposeConv1D neuron

module TransposeConv1D : sig ... end

TransposeConv2D neuron

module TransposeConv2D : sig ... end

TransposeConv3D neuron

module TransposeConv3D : sig ... end

FullyConnected neuron

module FullyConnected : sig ... end

MaxPool1D neuron

module MaxPool1D : sig ... end

MaxPool2D neuron

module MaxPool2D : sig ... end

AvgPool1D neuron

module AvgPool1D : sig ... end

AvgPool2D neuron

module AvgPool2D : sig ... end

GlobalMaxPool1D neuron

module GlobalMaxPool1D : sig ... end

GlobalMaxPool2D neuron

module GlobalMaxPool2D : sig ... end

GlobalAvgPool1D neuron

module GlobalAvgPool1D : sig ... end

GlobalAvgPool2D neuron

module GlobalAvgPool2D : sig ... end

UpSampling1D neuron

module UpSampling1D : sig ... end

UpSampling2D neuron

module UpSampling2D : sig ... end

UpSampling3D neuron

module UpSampling3D : sig ... end

Padding1D neuron

module Padding1D : sig ... end

Padding2D neuron

module Padding2D : sig ... end

Padding3D neuron

module Padding3D : sig ... end

Lambda neuron

module Lambda : sig ... end

LambdaArray neuron

module LambdaArray : sig ... end

Dropout neuron

module Dropout : sig ... end

Reshape neuron

module Reshape : sig ... end

Flatten neuron

module Flatten : sig ... end

Add neuron

module Add : sig ... end

Mul neuron

module Mul : sig ... end

Dot neuron

module Dot : sig ... end

Max neuron

module Max : sig ... end

Average neuron

module Average : sig ... end

Concatenate neuron

module Concatenate : sig ... end

Normalisation neuron

module Normalisation : sig ... end

GaussianNoise neuron

module GaussianNoise : sig ... end

GaussianDropout neuron

module GaussianDropout : sig ... end

AlphaDropout neuron

module AlphaDropout : sig ... end

Embedding neuron

module Embedding : sig ... end

Masking neuron

module Masking : sig ... end

Core functions

type neuron =

| Input of Input.neuron_typ
| Linear of Linear.neuron_typ
| LinearNoBias of LinearNoBias.neuron_typ
| Embedding of Embedding.neuron_typ
| LSTM of LSTM.neuron_typ
| GRU of GRU.neuron_typ
| Recurrent of Recurrent.neuron_typ
| Conv1D of Conv1D.neuron_typ
| Conv2D of Conv2D.neuron_typ
| Conv3D of Conv3D.neuron_typ
| DilatedConv1D of DilatedConv1D.neuron_typ
| DilatedConv2D of DilatedConv2D.neuron_typ
| DilatedConv3D of DilatedConv3D.neuron_typ
| TransposeConv1D of TransposeConv1D.neuron_typ
| TransposeConv2D of TransposeConv2D.neuron_typ
| TransposeConv3D of TransposeConv3D.neuron_typ
| FullyConnected of FullyConnected.neuron_typ
| MaxPool1D of MaxPool1D.neuron_typ
| MaxPool2D of MaxPool2D.neuron_typ
| AvgPool1D of AvgPool1D.neuron_typ
| AvgPool2D of AvgPool2D.neuron_typ
| GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
| GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
| GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
| GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
| UpSampling2D of UpSampling2D.neuron_typ
| Padding2D of Padding2D.neuron_typ
| Dropout of Dropout.neuron_typ
| Reshape of Reshape.neuron_typ
| Flatten of Flatten.neuron_typ
| Lambda of Lambda.neuron_typ
| LambdaArray of LambdaArray.neuron_typ
| Activation of Activation.neuron_typ
| GaussianNoise of GaussianNoise.neuron_typ
| GaussianDropout of GaussianDropout.neuron_typ
| AlphaDropout of AlphaDropout.neuron_typ
| Normalisation of Normalisation.neuron_typ
| Add of Add.neuron_typ
| Mul of Mul.neuron_typ
| Dot of Dot.neuron_typ
| Max of Max.neuron_typ
| Average of Average.neuron_typ
| Concatenate of Concatenate.neuron_typ
(*
Types of neuron.
*)

val get_in_out_shape : neuron -> int array * int array

Get both input and output shapes of a neuron.

val get_in_shape : neuron -> int array

Get the input shape of a neuron.

val get_out_shape : neuron -> int array

Get the output shape of a neuron.

val connect : int array array -> neuron -> unit

Connect this neuron to others in a neural network.

val init : neuron -> unit

Initialise the neuron and its parameters.

val reset : neuron -> unit

Reset the parameters in a neuron.

val mktag : int -> neuron -> unit

Tag the neuron, used by ``Algodiff`` module.

val mkpar : neuron -> Optimise.Algodiff.t array

Assemble all the trainable parameters in an array, used by ``Optimise`` module.

val mkpri : neuron -> Optimise.Algodiff.t array

Assemble all the primal values in an array, used by ``Optimise`` module.

val mkadj : neuron -> Optimise.Algodiff.t array

Assemble all the adjacent values in an array, used by ``Optimise`` module.

val update : neuron -> Optimise.Algodiff.t array -> unit

Update trainable parameters in a neuron, used by ``Optimise`` module.

val load_weights : neuron -> Optimise.Algodiff.t array -> unit

Load both trainable and non-trainable parameters into the neuron.

val save_weights : neuron -> Optimise.Algodiff.t array

Assemble both trainable and non-trainable parameters of the neuron.

val copy : neuron -> neuron

Make a deep copy of the neuron and its parameters.

val run : Optimise.Algodiff.t array -> neuron -> Optimise.Algodiff.t

Execute the computation in this neuron.

val to_string : neuron -> string

Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

val to_name : neuron -> string

Return the name of the neuron.