package owl-base

  1. Overview
  2. Docs
On This Page
  1. Init neuron
  2. Input neuron
  3. Activation neuron
  4. Linear neuron
  5. LinearNoBias neuron
  6. Recurrent neuron
  7. LSTM neuron
  8. GRU neuron
  9. Conv1D neuron
  10. TransposeConv1D neuron
  11. Conv2D neuron
  12. TransposeConv2D neuron
  13. Conv3D neuron
  14. TransposeConv3D neuron
  15. FullyConnected neuron
  16. MaxPool1D neuron
  17. MaxPool2D neuron
  18. AvgPool1D neuron
  19. AvgPool2D neuron
  20. GlobalMaxPool1D neuron
  21. GlobalMaxPool2D neuron
  22. GlobalAvgPool1D neuron
  23. GlobalAvgPool2D neuron
  24. UpSampling1D neuron
  25. UpSampling2D neuron
  26. UpSampling3D neuron
  27. Padding1D neuron
  28. Padding2D neuron
  29. Padding3D neuron
  30. Lambda neuron
  31. Dropout neuron
  32. Reshape neuron
  33. Flatten neuron
  34. Add neuron
  35. Mul neuron
  36. Dot neuron
  37. Max neuron
  38. Average neuron
  39. Concatenate neuron
  40. Normalisation neuron
  41. GaussianNoise neuron
  42. GaussianDropout neuron
  43. AlphaDropout neuron
  44. Embedding neuron
  45. Masking neuron
  46. Core functions
package owl-base
Legend:
Library
Module
Module type
Parameter
Class
Class type
include Owl_algodiff_generic_sig.Sig
Type definition
type arr

General ndarray type

type elt

Scalar type

type trace_op

Trace type

type t =
  1. | F of float
  2. | Arr of arr
  3. | DF of t * t * int
  4. | DR of t * t ref * trace_op * int ref * int
    (*

    Abstract number type

    *)
Supported Maths functions
module Maths : sig ... end
module Mat : sig ... end
module Arr : sig ... end
Core functions
val diff : (t -> t) -> t -> t

``diff f x`` returns the exat derivative of a function ``f : scalar -> scalar`` at point ``x``. Simply calling ``diff f`` will return its derivative function ``g`` of the same type, i.e. ``g : scalar -> scalar``.

Keep calling this function will give you higher-order derivatives of ``f``, i.e. ``f |> diff |> diff |> diff |> ...``

val diff' : (t -> t) -> t -> t * t

similar to ``diff``, but return ``(f x, diff f x)``.

val grad : (t -> t) -> t -> t

gradient of ``f`` : (vector -> scalar) at ``x``, reverse ad.

val grad' : (t -> t) -> t -> t * t

similar to ``grad``, but return ``(f x, grad f x)``.

val jacobian : (t -> t) -> t -> t

jacobian of ``f`` : (vector -> vector) at ``x``, both ``x`` and ``y`` are row vectors.

val jacobian' : (t -> t) -> t -> t * t

similar to ``jacobian``, but return ``(f x, jacobian f x)``

val jacobianv : (t -> t) -> t -> t -> t

jacobian vector product of ``f`` : (vector -> vector) at ``x`` along ``v``, forward ad. Namely, it calcultes ``(jacobian x) v``

val jacobianv' : (t -> t) -> t -> t -> t * t

similar to ``jacobianv'``, but return ``(f x, jacobianv f x v)``

val jacobianTv : (t -> t) -> t -> t -> t

transposed jacobian vector product of ``f : (vector -> vector)`` at ``x`` along ``v``, backward ad. Namely, it calculates ``transpose ((jacobianv f x v))``.

val jacobianTv' : (t -> t) -> t -> t -> t * t

similar to ``jacobianTv``, but return ``(f x, transpose (jacobianv f x v))``

val hessian : (t -> t) -> t -> t

hessian of ``f`` : (scalar -> scalar) at ``x``.

val hessian' : (t -> t) -> t -> t * t

simiarl to ``hessian``, but return ``(f x, hessian f x)``

val hessianv : (t -> t) -> t -> t -> t

hessian vector product of ``f`` : (scalar -> scalar) at ``x`` along ``v``. Namely, it calculates ``(hessian x) v``.

val hessianv' : (t -> t) -> t -> t -> t * t

similar to ``hessianv``, but return ``(f x, hessianv f x v)``.

val laplacian : (t -> t) -> t -> t

laplacian of ``f : (scalar -> scalar)`` at ``x``.

val laplacian' : (t -> t) -> t -> t * t

simiar to ``laplacian``, but return ``(f x, laplacian f x)``.

val gradhessian : (t -> t) -> t -> t * t

return ``(grad f x, hessian f x)``, ``f : (scalar -> scalar)``

val gradhessian' : (t -> t) -> t -> t * t * t

return ``(f x, grad f x, hessian f x)``

val gradhessianv : (t -> t) -> t -> t -> t * t

return ``(grad f x v, hessian f x v)``

val gradhessianv' : (t -> t) -> t -> t -> t * t * t

return ``(f x, grad f x v, hessian f x v)``

Low-level functions
val pack_flt : elt -> t

TODO

val unpack_flt : t -> elt

TODO

val pack_arr : arr -> t

TODO

val unpack_arr : t -> arr

TODO

val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val adjval : t -> t

TODO

val adjref : t -> t ref

TODO

val tangent : t -> t

TODO

val make_forward : t -> t -> int -> t

TODO

val make_reverse : t -> int -> t

TODO

val reverse_prop : t -> t -> unit

TODO

val type_info : t -> string

TODO

val shape : t -> int array

TODO

val copy_primal' : t -> t

TODO

val clip_by_value : amin:elt -> amax:elt -> t -> t
val clip_by_l2norm : elt -> t -> t
Helper functions
val to_trace : t list -> string

``to_trace t0; t1; ...`` outputs the trace of computation graph on the terminal in a human-readable format.

val to_dot : t list -> string

``to_dot t0; t1; ...`` outputs the trace of computation graph in the dot file format which you can use other tools further visualisation, such as Graphviz.

val pp_num : Format.formatter -> t -> unit

``pp_num t`` pretty prints the abstract number used in ``Algodiff``.

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
TransposeConv1D neuron
module TransposeConv1D : sig ... end
Conv2D neuron
module Conv2D : sig ... end
TransposeConv2D neuron
module TransposeConv2D : sig ... end
Conv3D neuron
module Conv3D : 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
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 =
  1. | Input of Input.neuron_typ
  2. | Linear of Linear.neuron_typ
  3. | LinearNoBias of LinearNoBias.neuron_typ
  4. | Embedding of Embedding.neuron_typ
  5. | LSTM of LSTM.neuron_typ
  6. | GRU of GRU.neuron_typ
  7. | Recurrent of Recurrent.neuron_typ
  8. | Conv1D of Conv1D.neuron_typ
  9. | Conv2D of Conv2D.neuron_typ
  10. | Conv3D of Conv3D.neuron_typ
  11. | TransposeConv1D of TransposeConv1D.neuron_typ
  12. | TransposeConv2D of TransposeConv2D.neuron_typ
  13. | TransposeConv3D of TransposeConv3D.neuron_typ
  14. | FullyConnected of FullyConnected.neuron_typ
  15. | MaxPool1D of MaxPool1D.neuron_typ
  16. | MaxPool2D of MaxPool2D.neuron_typ
  17. | AvgPool1D of AvgPool1D.neuron_typ
  18. | AvgPool2D of AvgPool2D.neuron_typ
  19. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
  20. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
  21. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
  22. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
  23. | Dropout of Dropout.neuron_typ
  24. | Reshape of Reshape.neuron_typ
  25. | Flatten of Flatten.neuron_typ
  26. | Lambda of Lambda.neuron_typ
  27. | Activation of Activation.neuron_typ
  28. | GaussianNoise of GaussianNoise.neuron_typ
  29. | GaussianDropout of GaussianDropout.neuron_typ
  30. | AlphaDropout of AlphaDropout.neuron_typ
  31. | Normalisation of Normalisation.neuron_typ
  32. | Add of Add.neuron_typ
  33. | Mul of Mul.neuron_typ
  34. | Dot of Dot.neuron_typ
  35. | Max of Max.neuron_typ
  36. | Average of Average.neuron_typ
  37. | 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 -> t array

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

val mkpri : neuron -> t array

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

val mkadj : neuron -> t array

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

val update : neuron -> t array -> unit

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

val copy : neuron -> neuron

Make a deep copy of the neuron and its parameters.

val run : t array -> neuron -> 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.

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