torch 0.4 · OCaml Package

type t

type t_with_training

val set_training : t_with_training -> is_training:Base.bool -> t

val with_training : t -> t_with_training

type activation =

| Relu
| Softmax
| Log_softmax
| Tanh
| Leaky_relu
| Sigmoid

val linear : 
  Var_store.t ->
  ?activation:activation ->
  ?use_bias:Base.bool ->
  ?w_init:Var_store.Init.t ->
  input_dim:Base.int ->
  Base.int ->
  t

val conv2d : 
  Var_store.t ->
  ksize:(Base.int * Base.int) ->
  stride:(Base.int * Base.int) ->
  ?activation:activation ->
  ?use_bias:Base.bool ->
  ?w_init:Var_store.Init.t ->
  ?padding:(Base.int * Base.int) ->
  ?groups:Base.int ->
  input_dim:Base.int ->
  Base.int ->
  t

val conv2d_ : 
  Var_store.t ->
  ksize:Base.int ->
  stride:Base.int ->
  ?activation:activation ->
  ?use_bias:Base.bool ->
  ?w_init:Var_store.Init.t ->
  ?padding:Base.int ->
  ?groups:Base.int ->
  input_dim:Base.int ->
  Base.int ->
  t

val conv_transpose2d : 
  Var_store.t ->
  ksize:(Base.int * Base.int) ->
  stride:(Base.int * Base.int) ->
  ?activation:activation ->
  ?use_bias:Base.bool ->
  ?w_init:Var_store.Init.t ->
  ?padding:(Base.int * Base.int) ->
  ?output_padding:(Base.int * Base.int) ->
  ?groups:Base.int ->
  input_dim:Base.int ->
  Base.int ->
  t

val conv_transpose2d_ : 
  Var_store.t ->
  ksize:Base.int ->
  stride:Base.int ->
  ?activation:activation ->
  ?use_bias:Base.bool ->
  ?w_init:Var_store.Init.t ->
  ?padding:Base.int ->
  ?output_padding:Base.int ->
  ?groups:Base.int ->
  input_dim:Base.int ->
  Base.int ->
  t

val batch_norm2d : 
  Var_store.t ->
  ?w_init:Var_store.Init.t ->
  ?cudnn_enabled:Base.bool ->
  ?eps:Base.float ->
  ?momentum:Base.float ->
  Base.int ->
  t_with_training

val id : t

val id_ : t_with_training

val of_fn : (Tensor.t -> Tensor.t) -> t

val of_fn_ : (Tensor.t -> is_training:Base.bool -> Tensor.t) -> t_with_training

val fold : t Base.list -> t

val fold_ : t_with_training Base.list -> t_with_training

val apply : t -> Tensor.t -> Tensor.t

val apply_ : t_with_training -> Tensor.t -> is_training:Base.bool -> Tensor.t

module Lstm : sig ... end

package torch