Vector (backprop.Backprop.Net.Vector)

include sig ... end

module Var : sig ... end

val var : Algebra.Vector.t Stdlib.ref -> Algebra.Vector.t t

val dup : 
  ?label:string ->
  int ->
  Algebra.Vector.t t ->
  Algebra.Vector.t * (Algebra.Vector.t -> unit)

Make a value useable exactly n times.

val drop : Algebra.Vector.t t -> unit

Should be called when a vector is not used.

val repeat : 
  int ->
  Algebra.Vector.t t ->
  (Algebra.Vector.t * (Algebra.Vector.t -> unit)) array

Make n explicit copies of x

val demux : 
  Algebra.Vector.t array t ->
  (Algebra.Vector.t * (Algebra.Vector.t -> unit)) array

val drop_pair : (Algebra.Vector.t * Algebra.Vector.t) t -> unit

Drop a pair of vectors.

val to_scalar : Algebra.Vector.t t -> float t

val cadd : float -> Algebra.Vector.t t -> Algebra.Vector.t t

Add a constant.

val cmul : float -> Algebra.Vector.t t -> Algebra.Vector.t t

Multiply by a constant.

val add : Algebra.Vector.t t -> Algebra.Vector.t t -> Algebra.Vector.t t

Add two vectors.

val hadamard : Algebra.Vector.t t -> Algebra.Vector.t t -> Algebra.Vector.t t

Hadamard product of two vectors.

val squared_norm : Algebra.Vector.t t -> float t

Squared norm.

val squared_distance_to : Algebra.Vector.t -> Algebra.Vector.t t -> float t

Squared distance to fixed vector.

val bias : 
  Algebra.Vector.t Stdlib.ref ->
  Algebra.Vector.t t ->
  Algebra.Vector.t t

Add a bias vector which can be optimized.

val affine : 
  Algebra.Matrix.t t ->
  Algebra.Vector.t Stdlib.ref ->
  Algebra.Vector.t t ->
  Algebra.Vector.t t

Affine layer.

val sigmoid : Algebra.Vector.t t -> Algebra.Vector.t t

Sigmoid layer.

val tanh : Algebra.Vector.t t -> Algebra.Vector.t t

val relu : Algebra.Vector.t t -> Algebra.Vector.t t

Rectified linear unit.

val log : Algebra.Vector.t t -> Algebra.Vector.t t

Log.

val dot : Algebra.Vector.t t -> Algebra.Vector.t t -> float t

Dot

val sum : Algebra.Vector.t t -> float t

Sum

val cross_entropy : Algebra.Vector.t -> Algebra.Vector.t t -> float t

Cross-entropy

val activation : 
  [< `None | `ReLU | `Sigmoid | `Tanh ] ->
  Algebra.Vector.t t ->
  Algebra.Vector.t t

val bias_fun : 
  Algebra.Vector.t Stdlib.ref ->
  Algebra.Vector.t t ->
  Algebra.Vector.t t

val activation_fun : 
  [< `None | `ReLU | `Sigmoid | `Tanh ] ->
  Algebra.Vector.t t ->
  Algebra.Vector.t t

val neural_network : 
  ?activation:[< `None | `ReLU | `Sigmoid | `Tanh Sigmoid ] ->
  weights:Algebra.Linear.t Stdlib.ref ->
  ?bias:Algebra.Vector.t Stdlib.ref ->
  Algebra.Vector.t t ->
  Algebra.Vector.t t

Neural network layer.

module RNN : sig ... end

Recurrent neural network.

module Matrix : sig ... end