package owl-base

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package owl-base
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Parameters

Signature

Type definition
type node = {
  1. mutable name : string;
  2. mutable prev : node array;
  3. mutable next : node array;
  4. mutable neuron : Neuron.neuron;
  5. mutable output : Optimise.t option;
  6. mutable network : network;
  7. mutable train : bool;
}
and network = {
  1. mutable nnid : string;
  2. mutable size : int;
  3. mutable root : node option;
  4. mutable topo : node array;
}

Type definition of a node and a neural network.

Manipuate networks
val make_network : ?nnid:string -> int -> node option -> node array -> network

Create an empty neural network.

val make_node : ?name:string -> ?train:bool -> node array -> node array -> Neuron.neuron -> Optimise.t option -> network -> node

Create a node in a neural network.

val get_root : network -> node

Get the root of the neural network.

val get_node : network -> string -> node

Get a node in a network with the given name.

val get_network : node -> network

Get the neural network of a given node associated with.

val collect_output : node array -> Optimise.t array

Collect the output values of given nodes.

val connect_pair : node -> node -> unit

Connect two nodes in a neural network.

val connect_to_parents : node array -> node -> unit

Connect a node to a list of parents.

val add_node : ?act_typ:Neuron.Activation.typ -> network -> node array -> node -> node

Add a node to the given network.

Interface to optimisation engine
val init : network -> unit

Initialise the network.

val reset : network -> unit

Reset the network, i.e. all the paramters in the neurons.

val mktag : int -> network -> unit

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

val mkpar : network -> Optimise.t array array

Collect the paramters of neurons, used by ``Optimise`` module.

val mkpri : network -> Optimise.t array array

Collect the primal values of neurons, used by ``Optimise`` module.

val mkadj : network -> Optimise.t array array

Collect the adjacent values of neurons, used by ``Optimise`` module.

val update : network -> Optimise.t array array -> unit

Update the paramters of neurons, used by ``Optimise`` module.

val run : Optimise.t -> network -> Optimise.t

Execute the computations in all the neurons in a network with the given input.

val forward : network -> Optimise.t -> Optimise.t * Optimise.t array array

Run the forward pass of a network.

val backward : network -> Optimise.t -> Optimise.t array array * Optimise.t array array

Run the backward pass of a network.

val copy : network -> network

Make a deep copy of the given network.

Make a deep copy of the given network, excluding the neurons marked with ``training = true``.

Create Neurons
val input : ?name:string -> int array -> node

``input shape`` creates an input node for input data.

Arguments: * ``shape``: shape of input data.

val activation : ?name:string -> Neuron.Activation.typ -> node -> node

Applies an activation function to an output.

Arguments: * ``activation``: name of activation function to use.

val linear : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> node -> node

``linear ?act_typ units node`` adds the regular densely-connected NN node to ``node``.

Arguments: * ``units``: Positive integer, dimensionality of the output space. * ``act_typ``: Activation function to use.

val linear_nobias : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> node -> node

Similar to ``linear``, but does not use the bias vector.

val embedding : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> int -> node -> node

Create a node for embedding neuron.

val recurrent : ?name:string -> ?init_typ:Neuron.Init.typ -> act_typ:Neuron.Activation.typ -> int -> int -> node -> node

Create a node for recurrent neuron.

val lstm : ?name:string -> ?init_typ:Neuron.Init.typ -> int -> node -> node

``lstm units node`` adds a LSTM node on previous ``node``.

Arguments: * ``units``: Positive integer, dimensionality of the output space.

val gru : ?name:string -> ?init_typ:Neuron.Init.typ -> int -> node -> node

``gru units node`` adds a Gated Recurrent Unit node on previous ``node``.

Arguments: * ``units``: Positive integer, dimensionality of the output space.

val conv1d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``conv1d kernels strides node`` adds a 1D convolution node (e.g. temporal convolution) on previous ``node``.

Arguments: * ``kernel``: int array consists of ``h, i, o``. ``h`` specifies the dimension of the 1D convolution window. ``i`` and ``o`` are the dimensionalities of the input and output space. * ``stride``: int array of 1 integer

val conv2d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``conv2d kernels strides node`` adds a 2D convolution node (e.g. spatial convolution over images) on previous ``node``.

Arguments: * ``kernel``: int array consists of ``w, h, i, o``. ``w`` and ``h`` specify the width and height of the 2D convolution window. ``i`` and ``o`` are the dimensionality of the input and output space. * ``stride``: int array of 2 integers

val conv3d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``conv3d kernels strides node`` adds a 3D convolution node (e.g. spatial convolution over volumes) on previous ``node``.

Arguments: * ``kernel``: int array consists of ``w, h, d, i, o``. ``w``, ``h``, and ``d`` specify the 3 dimensionality of the 3D convolution window. ``i`` and ``o`` are the dimensionality of the input and output space. * ``stride``: int array of 3 integers

val transpose_conv1d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``transpose_conv1d kernels strides node`` adds a 1D transpose convolution node (e.g. temporal convolution) on previous ``node``.

Arguments: * ``kernel``: int array consists of ``h, i, o``. ``h`` specifies the dimension of the 1D convolution window. ``i`` and ``o`` are the dimensionalities of the input and output space. * ``stride``: int array of 1 integer

val transpose_conv2d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``transpose_conv2d kernels strides node`` adds a 2D transpose convolution node on previous ``node``.

Arguments: * ``kernel``: int array consists of ``w, h, i, o``. ``w`` and ``h`` specify the width and height of the 2D convolution window. ``i`` and ``o`` are the dimensionality of the input and output space. * ``stride``: int array of 2 integers

val transpose_conv3d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``transpose_conv3d kernels strides node`` adds a 3D transpose convolution node (e.g. spatial convolution over volumes) on previous ``node``.

Arguments: * ``kernel``: int array consists of ``w, h, d, i, o``. ``w``, ``h``, and ``d`` specify the 3 dimensionality of the 3D convolution window. ``i`` and ``o`` are the dimensionality of the input and output space. * ``stride``: int array of 3 integers

val fully_connected : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> node -> node

``fully_connected outputs node`` adds a fully connected node to ``node``.

Arguments: * ``outputs``: integer, the number of output units in the node

val max_pool1d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``max_pool1d ~padding ~act_typ pool_size strides node`` adds a max pooling operation for temporal data to ``node``.

Arguments: * ``pool_size``: Array of one integer, size of the max pooling windows. * ``strides``: Array of one integer, factor by which to downscale.

val max_pool2d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``max_pool2d ~padding ~act_typ pool_size strides node`` adds a max pooling operation for spatial data to ``node``.

Arguments: * ``pool_size``: Array of 2 integers, size of the max pooling windows. * ``strides``: Array of 2 integers, factor by which to downscale.

val avg_pool1d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``avg_pool1d ~padding ~act_typ pool_size strides node`` adds a average pooling operation for temporal data to ``node``.

Arguments: * ``pool_size``: Array of one integer, size of the max pooling windows. * ``strides``: Array of one integer, factor by which to downscale.

val avg_pool2d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node

``avg_pool2d ~padding ~act_typ pool_size strides node`` adds a average pooling operation for spatial data to ``node``.

Arguments: * ``pool_size``: Array of 2 integers, size of the max pooling windows. * ``strides``: Array of 2 integers, factor by which to downscale.

val global_max_pool1d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node

``global_max_pool1d`` adds global max pooling operation for temporal data.

val global_max_pool2d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node

``global_max_poo2d`` global max pooling operation for spatial data.

val global_avg_pool1d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node

``global_avg_pool1d`` adds global average pooling operation for temporal data.

val global_avg_pool2d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node

``global_avg_poo2d`` global average pooling operation for spatial data.

val dropout : ?name:string -> float -> node -> node

``dropout rate node`` applies Dropout to the input to prevent overfitting.

Arguments: * ``rate``: float between 0 and 1. Fraction of the input units to drop.

val gaussian_noise : ?name:string -> float -> node -> node

``gaussian_noise stddev node`` applies additive zero-centered Gaussian noise.

Arguments: * ``stddev``: float, standard deviation of the noise distribution.

val gaussian_dropout : ?name:string -> float -> node -> node

``gaussian_dropout rate node`` applies multiplicative 1-centered Gaussian noise. Only active at training time.

Arguments: * ``rates``: float, drop probability

val alpha_dropout : ?name:string -> float -> node -> node

``alpha_dropout rate node`` applies Alpha Dropout to the input ``node``. Only active at training time.

Arguments: * ``rates``: float, drop probability

val normalisation : ?name:string -> ?axis:int -> ?training:bool -> ?decay:float -> ?mu:Optimise.arr -> ?var:Optimise.arr -> node -> node

``normalisation axis node`` normalise the activations of the previous node at each batch.

Arguments: * ``axis``: Integer, the axis that should be normalised (typically the features axis). Default value is 0.

val reshape : ?name:string -> int array -> node -> node

``reshape target_shape node`` reshapes an output to a certain shape.

Arguments: * ``target_shape``: target shape. Array of integers. Does not include the batch axis.

val flatten : ?name:string -> node -> node

``flatten node`` flattens the input. Does not affect the batch size.

val lambda : ?name:string -> ?act_typ:Neuron.Activation.typ -> (Optimise.t -> Optimise.t) -> node -> node

``lambda func node`` wraps arbitrary expression as a Node object.

Arguments: * ``func``: The function to be evaluated. Takes input tensor as first argument.

val add : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node

Node that adds a list of inputs.

It takes as input an array of nodes, all of the same shape, and returns a single node (also of the same shape).

val mul : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node

Node that multiplies (element-wise) a list of inputs.

It takes as input an array of nodes, all of the same shape, and returns a single node (also of the same shape).

val dot : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node

Node that computes a dot product between samples in two nodes.

val max : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node

Node that computes the maximum (element-wise) a list of inputs.

val average : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node

Node that averages a list of inputs.

It takes as input an array of nodes, all of the same shape, and returns a single node (also of the same shape).

val concatenate : ?name:string -> ?act_typ:Neuron.Activation.typ -> int -> node array -> node

``concatenate axis nodes`` concatenates a array of ``nodes`` and return as a single node.

Arguments: * ``axis``: Axis along which to concatenate.

Helper functions
val to_string : network -> string

Convert a neural network to its string representation.

val pp_network : Format.formatter -> network -> unit

Pretty printing function a neural network.

val print : network -> unit

Print the string representation of a neural network to the standard output.

val save : network -> string -> unit

Serialise a network and save it to the a file with the given name.

val load : string -> network

Load the neural network from a file with the given name.

val save_weights : network -> string -> unit

Save all the weights in a neural network to a file. The weights and the name of their associated neurons are saved as key-value pairs in a hash table.

val load_weights : network -> string -> unit

Load the weights from a file of the given name. Note that the weights and the name of their associated neurons are saved as key-value pairs in a hash table.

Train Networks
val train_generic : ?state:Optimise.Checkpoint.state -> ?params:Optimise.Params.typ -> ?init_model:bool -> network -> Optimise.t -> Optimise.t -> Optimise.Checkpoint.state

Generic function of training a neural network.

val train : ?state:Optimise.Checkpoint.state -> ?params:Optimise.Params.typ -> ?init_model:bool -> network -> Optimise.arr -> Optimise.arr -> Optimise.Checkpoint.state

Train a neural network with various configurations.