torch 0.17 · OCaml Package

type t = {

train_images : Tensor.t;
train_labels : Tensor.t;
test_images : Tensor.t;
test_labels : Tensor.t;

}

val train_batch : 
  ?device:Device.t ->
  ?augmentation:[ `flip | `crop_with_pad of int | `cutout of int ] list ->
  t ->
  batch_size:int ->
  batch_idx:int ->
  Tensor.t * Tensor.t

train_batch ?device ?augmentation t ~batch_size ~batch_idx returns two tensors corresponding to a training batch. The first tensor is for images and the second for labels. Each batch has a first dimension of size batch_size, batch_idx = 0 returns the first batch, batch_idx = 1 returns the second one and so on. The tensors are located on device if provided. Random data augmentation is performed as specified via augmentation.

val batch_accuracy : 
  ?device:Device.t ->
  ?samples:int ->
  t ->
  [ `test | `train ] ->
  batch_size:int ->
  predict:(Tensor.t -> Tensor.t) ->
  float

batch_accuracy ?device ?samples t test_or_train ~batch_size ~predict computes the accuracy of applying predict to test or train images as specified by test_or_train. Computations are done using batch of length batch_size.

val read_with_cache : cache_file:string -> read:(unit -> t) -> t

read_with_cache ~cache_file ~read either returns the content of cache_file if present or regenerate the file using read if not.

val batches_per_epoch : t -> batch_size:int -> int

batches_per_epoch t ~batch_size returns the total number of batches of size batch_size in t.

val iter : 
  ?device:Device.t ->
  ?augmentation:[ `flip | `crop_with_pad of int | `cutout of int ] list ->
  ?shuffle:bool ->
  t ->
  f:(int -> batch_images:Tensor.t -> batch_labels:Tensor.t -> unit) ->
  batch_size:int ->
  unit

iter ?device ?augmentation ?shuffle t ~f ~batch_size iterates function f on all the batches from t taken with a size batch_size. Random shuffling and augmentation can be specified.

val random_flip : Tensor.t -> Tensor.t

random_flip t applies some random flips to a tensor of dimension N; H; C; W. The last dimension related to width can be flipped.

val random_crop : Tensor.t -> pad:int -> Tensor.t

random_crop t ~pad performs some data augmentation by padding t with zeros on the two last dimensions with pad new values on each side, then performs some random crop to go back to the original shape.

val shuffle : t -> t

shuffle t returns t where training images and labels have been shuffled.

val map : 
  ?device:Device.t ->
  t ->
  f:
    (int ->
      batch_images:Tensor.t ->
      batch_labels:Tensor.t ->
      Tensor.t * Tensor.t) ->
  batch_size:int ->
  t

val print_summary : t -> unit

val read_char_tensor : string -> Tensor.t

read_char_tensor filename returns a tensor of char containing the specified file.

package torch