package neural_nets_lib

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in-package search v0.2.0

neural_nets_lib
- Ocannl
  - Operation
    
    At
    
    DO
    
    Initial_NTDSL
    
    O
    
    Initial_TDSL
    
    O
    
    NDO
    
    NDO_without_div
    
    NDO_without_pow
    
    NTDSL
    
    TDSL
  - PrintBox_utils
  - Row
    
    Variants_of_bcast
    
    Variants_of_constraint_
    
    Variants_of_dim
    
    Variants_of_dim_constraint
    
    Variants_of_kind
    
    Variants_of_row_constraint
  - Shape
  - Tensor
  - Train
    
    CDSL
    
    IDX

Legend:
Library
Module
Module type
Parameter
Class
Class type

include module type of struct include Initial_TDSL end

val term : 
  label:Base.string Base.list ->
  ?batch_dims:Base.int Base.list ->
  ?input_dims:Base.int Base.list ->
  ?output_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  ?init_op:Tensor.init_op ->
  ?fetch_op:(v:Tensor.tn -> Tensor.fetch_op) ->
  Base.unit ->
  Tensor.t

val number : 
  ?label:Base.string Base.list ->
  ?axis_label:Base.string ->
  Base.float ->
  Tensor.t

val ndarray : 
  ?label:Base.string Base.list ->
  ?batch_dims:Base.int Base.list ->
  ?input_dims:Base.int Base.list ->
  ?output_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?strict:Base.bool ->
  Base.float Base.array ->
  Tensor.t

val param : 
  ?input_dims:Base.int Base.list ->
  ?output_dims:Base.int Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  ?strict:Base.bool ->
  ?values:Base.float Base.array ->
  Base.string ->
  Tensor.t

module O = DO

val einsum : 
  ?label:Base.string list ->
  Base.string ->
  Tensor.t ->
  Tensor.t ->
  Tensor.t

val outer_sum : 
  ?label:Base.string list ->
  Base.string ->
  Tensor.t ->
  Tensor.t ->
  Tensor.t

val einsum1 : ?label:Base.string list -> Base.string -> Tensor.t -> Tensor.t

val range : 
  ?label:Base.string list ->
  ?axis_label:Base.string ->
  Base.Int.t ->
  Tensor.t

val range_of_shape : 
  ?label:Base.string list ->
  ?batch_dims:Base.Int.t Base.List.t ->
  ?input_dims:Base.Int.t Base.List.t ->
  ?output_dims:Base.Int.t Base.List.t ->
  ?batch_axes:(Base.string * Base.Int.t) Base.List.t ->
  ?input_axes:(Base.string * Base.Int.t) Base.List.t ->
  ?output_axes:(Base.string * Base.Int.t) Base.List.t ->
  unit ->
  Tensor.t

val stop_gradient : ?label:Base.string list -> Tensor.t -> Tensor.t

val init_const : 
  l:Base.string ->
  ?strict:Base.bool ->
  ?b:Base.int Base.list ->
  ?i:Base.int Base.list ->
  o:Base.int Base.list ->
  Base.float Base.array ->
  Tensor.t

The input i dimensions default to empty. The batch dimensions will be inferred if omitted. strict controls whether Constant_fill will try to fit the given values in the tensor and contribute to shape inference. If it is not provided explicitly, it will be true if b is omitted, and false otherwise.

val init_param : 
  l:Base.string ->
  ?b:Base.int Base.list ->
  ?i:Base.int Base.list ->
  ?o:Base.int Base.list ->
  Base.float Base.array ->
  Tensor.t

It's like `Tensor.param` but without shape inference.