## Warning in warn_deprecated("Learner$initialize argument 'data_formats'"): +## Learner$initialize argument 'data_formats' is deprecated and will be removed in +## the future.
+mlp$train(task)
mlp$train(task)
## Epoch 1: 1.22 ## Epoch 2: 1.27 ## Epoch 3: 1.48 @@ -250,7 +253,7 @@ Using the Custom Logger## Epoch 5: 1.09
The information that is returnede by state_dict() is now accessible via the Learner’s $model-slot:
state_dict()
Learner
$model
+ mlp$model$callbacks$custom_logger ## [1] 1.090038
mlp$model$callbacks$custom_logger
## [1] 1.090038
The wrapped loss module generator is accessible through the slot $generator.
$generator
@@ -235,7 +238,7 @@ Loss #> clone: function (deep = FALSE, ..., replace_values = TRUE) #> Private: #> .__clone_r6__: function (deep = FALSE) -#> Parent env: <environment: 0x558c2b6c0e38> +#> Parent env: <environment: 0x55d8b28bbac0> #> Locked objects: FALSE #> Locked class: FALSE #> Portable: TRUE
The wrapped torch optimizer can be accessed through the slot generator.
generator
Parameters of TorchOptimizer (but also @@ -404,22 +407,22 @@
TorchOptimizer
prediction_custom$score(msr("regr.mae")) #> regr.mae -#> 9.904111 +#> 7.122375 prediction$score(msr("regr.mae")) #> regr.mae -#> 10.03284
Because we configured the learner to use the history callback, we can find the validation history in its $model slot:
head(mlp_custom$model$callbacks$history$valid) #> epoch regr.mae #> <num> <num> -#> 1: 1 1.048699e+05 -#> 2: 2 2.500048e+06 -#> 3: 3 6.342546e+02 -#> 4: 4 1.688923e+04 -#> 5: 5 8.859892e+01 -#> 6: 6 9.095035e+00
The plot below shows it for the epochs 6 to 30.
Other important information that is stored in the @@ -439,7 +442,7 @@
Site built with pkgdown 2.1.0.
Site built with pkgdown 2.1.1.
torch
A nn_module is constructed from a nn_module_generator. nn_linear is one of the @@ -117,8 +117,8 @@
nn_module
nn_module_generator
nn_linear
A neural network with one (4-unit) hidden layer and two outputs needs the following ingredients
We will now continue with showing how such a neural network can be represented in mlr3torch.
mlr3torch
Note we only use the $train(), since torch modules do not have anything that maps to the state (it is filled by @@ -196,8 +196,8 @@
$train()
state
While this object allows to easily perform a forward pass, it does not inherit from nn_module, which is useful for various @@ -245,8 +245,8 @@
graph_module(input) #> torch_tensor -#> 0.3595 0.1750 0.4655 -#> 0.3147 0.2049 0.4804 +#> 0.3541 0.2318 0.4141 +#> 0.3512 0.2295 0.4192 #> [ CPUFloatType{2,3} ][ grad_fn = <SoftmaxBackward0> ]
ModelDescriptor
The first linear layer that takes “Sepal” input ("linear1") creates a 2x4 tensor (batch size 2, 4 units), @@ -689,14 +689,14 @@
"linear1"
We observe that the po("nn_merge_cat") concatenates these, as expected:
po("nn_merge_cat")
library(mlr3torch) mnist = tsk("mnist") +#> Warning in warn_deprecated("DataBackend$initialize argument 'data_formats'"): +#> DataBackend$initialize argument 'data_formats' is deprecated and will be +#> removed in the future. mnist #> <TaskClassif:mnist> (70000 x 2): MNIST Digit Classification #> * Target: label @@ -119,6 +122,11 @@ mnist$head() #> Processing... #> Done! +#> Warning in warn_deprecated("DataBackend$data_formats"): +#> DataBackend$data_formats is deprecated and will be removed in the future. +#> Warning in warn_deprecated("DataBackendDataTable$data argument 'data_format'"): +#> DataBackendDataTable$data argument 'data_format' is deprecated and will be +#> removed in the future. #> label image #> <fctr> <lazy_tensor> #> 1: 5 <tnsr[1x28x28]> @@ -154,6 +162,9 @@ neurons = c(100, 100), epochs = 10, batch_size = 32 ) +#> Warning in warn_deprecated("Learner$initialize argument 'data_formats'"): +#> Learner$initialize argument 'data_formats' is deprecated and will be removed in +#> the future. mlp #> <LearnerTorchMLP[classif]:classif.mlp>: My Little Powny #> * Model: - @@ -386,7 +397,7 @@ Digging Into Internals#> <DataDescriptor: 1 ops> #> * dataset_shapes: [x: (NA,1)] #> * input_map: (x) -> Graph -#> * pointer: nop.e73617.x.output +#> * pointer: nop.75bcd5.x.output #> * shape: [(NA,1)] The printed output of the data descriptor informs us about: @@ -433,7 +444,7 @@ Digging Into Internals#> <DataDescriptor: 1 ops> #> * dataset_shapes: [x: (NA,1)] #> * input_map: (x) -> Graph -#> * pointer: nop.e73617.x.output +#> * pointer: nop.75bcd5.x.output #> * shape: [(NA,1)]
mnist$head() #> Processing... #> Done! +#> Warning in warn_deprecated("DataBackend$data_formats"): +#> DataBackend$data_formats is deprecated and will be removed in the future. +#> Warning in warn_deprecated("DataBackendDataTable$data argument 'data_format'"): +#> DataBackendDataTable$data argument 'data_format' is deprecated and will be +#> removed in the future. #> label image #> <fctr> <lazy_tensor> #> 1: 5 <tnsr[1x28x28]> @@ -154,6 +162,9 @@ neurons = c(100, 100), epochs = 10, batch_size = 32 ) +#> Warning in warn_deprecated("Learner$initialize argument 'data_formats'"): +#> Learner$initialize argument 'data_formats' is deprecated and will be removed in +#> the future. mlp #> <LearnerTorchMLP[classif]:classif.mlp>: My Little Powny #> * Model: - @@ -386,7 +397,7 @@ Digging Into Internals#> <DataDescriptor: 1 ops> #> * dataset_shapes: [x: (NA,1)] #> * input_map: (x) -> Graph -#> * pointer: nop.e73617.x.output +#> * pointer: nop.75bcd5.x.output #> * shape: [(NA,1)]
The printed output of the data descriptor informs us about:
What happens during materialize(lt[1]) is the following:
materialize(lt[1])
We see that the $graph has a new pipeop with id "poly.x" and the output pointer points to poly.x. Also we see that the shape of the tensor is now @@ -508,7 +519,7 @@
$graph
"poly.x"
pointer
poly.x
library(mlr3torch) imagenet = tsk("tiny_imagenet") +#> Warning in warn_deprecated("DataBackend$initialize argument 'data_formats'"): +#> DataBackend$initialize argument 'data_formats' is deprecated and will be +#> removed in the future. imagenet #> <TaskClassif:tiny_imagenet> (110000 x 2): ImageNet Subset #> * Target: class @@ -175,6 +178,9 @@ t_clbk("checkpoint", freq = 20, path = checkpoint)) %>>% po("torch_model_classif", batch_size = 32, epochs = 100L, device = "cuda") +#> Warning in warn_deprecated("Learner$initialize argument 'data_formats'"): +#> Learner$initialize argument 'data_formats' is deprecated and will be removed in +#> the future. cnn = as_learner(architecture) cnn$id = "cnn"
Fischer S, Binder M (2024). mlr3torch: Deep Learning with 'mlr3'. -R package version 0.1.0-9000, https://github.com/mlr-org/mlr3torch/, https://mlr3torch.mlr-org.com/. +R package version 0.1.1-9000, https://github.com/mlr-org/mlr3torch/, https://mlr3torch.mlr-org.com/.
@Manual{, title = {mlr3torch: Deep Learning with 'mlr3'}, author = {Sebastian Fischer and Martin Binder}, year = {2024}, - note = {R package version 0.1.0-9000, https://github.com/mlr-org/mlr3torch/}, + note = {R package version 0.1.1-9000, https://github.com/mlr-org/mlr3torch/}, url = {https://mlr3torch.mlr-org.com/}, }
NEWS.md
CRAN release: 2024-09-12
PipeOps
po("trafo_resize")
LearnerTabResnet
nn()
CRAN release: 2024-07-08
lt1 = as_lazy_tensor(torch_randn(10, 3)) materialize(lt1, rbind = TRUE) #> torch_tensor -#> 0.5493 -0.5515 -0.5812 -#> -1.2274 0.8850 -0.0685 -#> -0.1044 -0.7159 0.1187 -#> -0.7307 -0.7775 0.9271 -#> -0.7786 0.4380 -0.4887 -#> -0.2497 -0.4458 -0.4004 -#> -0.2404 0.9826 0.1626 -#> -0.5319 -1.7876 0.2970 -#> -1.0201 0.7622 0.8252 -#> 0.4119 0.1936 -1.7774 +#> 0.7826 0.9021 -0.3734 +#> -0.1509 1.2695 -1.4460 +#> -0.4803 -0.3497 -1.1911 +#> 0.6350 -1.0374 0.6134 +#> 1.4071 0.2895 -2.0563 +#> 0.3326 -0.2041 0.8140 +#> 0.4219 -0.1421 -0.2484 +#> -0.3052 -0.5527 1.2542 +#> -0.7892 -1.4757 -0.2113 +#> -0.0047 0.0414 1.5259 #> [ CPUFloatType{10,3} ] materialize(lt1, rbind = FALSE) #> [[1]] #> torch_tensor -#> 0.5493 -#> -0.5515 -#> -0.5812 +#> 0.7826 +#> 0.9021 +#> -0.3734 #> [ CPUFloatType{3} ] #> #> [[2]] #> torch_tensor -#> -1.2274 -#> 0.8850 -#> -0.0685 +#> -0.1509 +#> 1.2695 +#> -1.4460 #> [ CPUFloatType{3} ] #> #> [[3]] #> torch_tensor -#> -0.1044 -#> -0.7159 -#> 0.1187 +#> -0.4803 +#> -0.3497 +#> -1.1911 #> [ CPUFloatType{3} ] #> #> [[4]] #> torch_tensor -#> -0.7307 -#> -0.7775 -#> 0.9271 +#> 0.6350 +#> -1.0374 +#> 0.6134 #> [ CPUFloatType{3} ] #> #> [[5]] #> torch_tensor -#> -0.7786 -#> 0.4380 -#> -0.4887 +#> 1.4071 +#> 0.2895 +#> -2.0563 #> [ CPUFloatType{3} ] #> #> [[6]] #> torch_tensor -#> -0.2497 -#> -0.4458 -#> -0.4004 +#> 0.3326 +#> -0.2041 +#> 0.8140 #> [ CPUFloatType{3} ] #> #> [[7]] #> torch_tensor -#> -0.2404 -#> 0.9826 -#> 0.1626 +#> 0.4219 +#> -0.1421 +#> -0.2484 #> [ CPUFloatType{3} ] #> #> [[8]] #> torch_tensor -#> -0.5319 -#> -1.7876 -#> 0.2970 +#> -0.3052 +#> -0.5527 +#> 1.2542 #> [ CPUFloatType{3} ] #> #> [[9]] #> torch_tensor -#> -1.0201 -#> 0.7622 -#> 0.8252 +#> -0.7892 +#> -1.4757 +#> -0.2113 #> [ CPUFloatType{3} ] #> #> [[10]] #> torch_tensor -#> 0.4119 -#> 0.1936 -#> -1.7774 +#> -0.0047 +#> 0.0414 +#> 1.5259 #> [ CPUFloatType{3} ] #> lt2 = as_lazy_tensor(torch_randn(10, 4)) @@ -219,184 +219,184 @@ Examplesmaterialize(d, rbind = TRUE) #> $lt1 #> torch_tensor -#> 0.5493 -0.5515 -0.5812 -#> -1.2274 0.8850 -0.0685 -#> -0.1044 -0.7159 0.1187 -#> -0.7307 -0.7775 0.9271 -#> -0.7786 0.4380 -0.4887 -#> -0.2497 -0.4458 -0.4004 -#> -0.2404 0.9826 0.1626 -#> -0.5319 -1.7876 0.2970 -#> -1.0201 0.7622 0.8252 -#> 0.4119 0.1936 -1.7774 +#> 0.7826 0.9021 -0.3734 +#> -0.1509 1.2695 -1.4460 +#> -0.4803 -0.3497 -1.1911 +#> 0.6350 -1.0374 0.6134 +#> 1.4071 0.2895 -2.0563 +#> 0.3326 -0.2041 0.8140 +#> 0.4219 -0.1421 -0.2484 +#> -0.3052 -0.5527 1.2542 +#> -0.7892 -1.4757 -0.2113 +#> -0.0047 0.0414 1.5259 #> [ CPUFloatType{10,3} ] #> #> $lt2 #> torch_tensor -#> 0.0377 -0.0322 -1.0237 0.6420 -#> 1.5552 0.5489 0.1991 -1.0462 -#> 1.1983 -1.2508 -0.1582 -0.0274 -#> 0.2271 -1.7810 0.2622 -0.4218 -#> 0.4619 0.5835 0.6739 -0.2246 -#> 0.5479 -0.4525 1.1257 0.9266 -#> -0.5428 0.2070 -0.5259 -1.1674 -#> -0.5740 0.2678 -1.0389 -0.2550 -#> -0.4178 0.4700 -1.5360 0.6578 -#> 0.4972 0.8935 1.1799 -0.1597 +#> 0.1728 -1.7401 0.6610 1.7411 +#> -0.4639 -0.3019 -1.3339 -0.2104 +#> -0.9944 0.5949 -1.0769 0.9698 +#> -0.9466 -1.6516 -0.8122 -0.0687 +#> -0.2945 -0.1360 -1.3794 -2.0543 +#> -0.8444 -1.8168 0.5025 -0.5932 +#> 0.3098 -2.3293 0.6917 0.4684 +#> -0.4623 -0.0945 -0.7702 -2.9021 +#> -1.8322 1.7678 -1.5651 2.0952 +#> -0.2700 0.7575 1.9979 0.0179 #> [ CPUFloatType{10,4} ] #> materialize(d, rbind = FALSE) #> $lt1 #> $lt1[[1]] #> torch_tensor -#> 0.5493 -#> -0.5515 -#> -0.5812 +#> 0.7826 +#> 0.9021 +#> -0.3734 #> [ CPUFloatType{3} ] #> #> $lt1[[2]] #> torch_tensor -#> -1.2274 -#> 0.8850 -#> -0.0685 +#> -0.1509 +#> 1.2695 +#> -1.4460 #> [ CPUFloatType{3} ] #> #> $lt1[[3]] #> torch_tensor -#> -0.1044 -#> -0.7159 -#> 0.1187 +#> -0.4803 +#> -0.3497 +#> -1.1911 #> [ CPUFloatType{3} ] #> #> $lt1[[4]] #> torch_tensor -#> -0.7307 -#> -0.7775 -#> 0.9271 +#> 0.6350 +#> -1.0374 +#> 0.6134 #> [ CPUFloatType{3} ] #> #> $lt1[[5]] #> torch_tensor -#> -0.7786 -#> 0.4380 -#> -0.4887 +#> 1.4071 +#> 0.2895 +#> -2.0563 #> [ CPUFloatType{3} ] #> #> $lt1[[6]] #> torch_tensor -#> -0.2497 -#> -0.4458 -#> -0.4004 +#> 0.3326 +#> -0.2041 +#> 0.8140 #> [ CPUFloatType{3} ] #> #> $lt1[[7]] #> torch_tensor -#> -0.2404 -#> 0.9826 -#> 0.1626 +#> 0.4219 +#> -0.1421 +#> -0.2484 #> [ CPUFloatType{3} ] #> #> $lt1[[8]] #> torch_tensor -#> -0.5319 -#> -1.7876 -#> 0.2970 +#> -0.3052 +#> -0.5527 +#> 1.2542 #> [ CPUFloatType{3} ] #> #> $lt1[[9]] #> torch_tensor -#> -1.0201 -#> 0.7622 -#> 0.8252 +#> -0.7892 +#> -1.4757 +#> -0.2113 #> [ CPUFloatType{3} ] #> #> $lt1[[10]] #> torch_tensor -#> 0.4119 -#> 0.1936 -#> -1.7774 +#> -0.0047 +#> 0.0414 +#> 1.5259 #> [ CPUFloatType{3} ] #> #> #> $lt2 #> $lt2[[1]] #> torch_tensor -#> 0.0377 -#> -0.0322 -#> -1.0237 -#> 0.6420 +#> 0.1728 +#> -1.7401 +#> 0.6610 +#> 1.7411 #> [ CPUFloatType{4} ] #> #> $lt2[[2]] #> torch_tensor -#> 1.5552 -#> 0.5489 -#> 0.1991 -#> -1.0462 +#> -0.4639 +#> -0.3019 +#> -1.3339 +#> -0.2104 #> [ CPUFloatType{4} ] #> #> $lt2[[3]] #> torch_tensor -#> 1.1983 -#> -1.2508 -#> -0.1582 -#> -0.0274 +#> -0.9944 +#> 0.5949 +#> -1.0769 +#> 0.9698 #> [ CPUFloatType{4} ] #> #> $lt2[[4]] #> torch_tensor -#> 0.2271 -#> -1.7810 -#> 0.2622 -#> -0.4218 +#> -0.9466 +#> -1.6516 +#> -0.8122 +#> -0.0687 #> [ CPUFloatType{4} ] #> #> $lt2[[5]] #> torch_tensor -#> 0.4619 -#> 0.5835 -#> 0.6739 -#> -0.2246 +#> -0.2945 +#> -0.1360 +#> -1.3794 +#> -2.0543 #> [ CPUFloatType{4} ] #> #> $lt2[[6]] #> torch_tensor -#> 0.5479 -#> -0.4525 -#> 1.1257 -#> 0.9266 +#> -0.8444 +#> -1.8168 +#> 0.5025 +#> -0.5932 #> [ CPUFloatType{4} ] #> #> $lt2[[7]] #> torch_tensor -#> -0.5428 -#> 0.2070 -#> -0.5259 -#> -1.1674 +#> 0.3098 +#> -2.3293 +#> 0.6917 +#> 0.4684 #> [ CPUFloatType{4} ] #> #> $lt2[[8]] #> torch_tensor -#> -0.5740 -#> 0.2678 -#> -1.0389 -#> -0.2550 +#> -0.4623 +#> -0.0945 +#> -0.7702 +#> -2.9021 #> [ CPUFloatType{4} ] #> #> $lt2[[9]] #> torch_tensor -#> -0.4178 -#> 0.4700 -#> -1.5360 -#> 0.6578 +#> -1.8322 +#> 1.7678 +#> -1.5651 +#> 2.0952 #> [ CPUFloatType{4} ] #> #> $lt2[[10]] #> torch_tensor -#> 0.4972 -#> 0.8935 -#> 1.1799 -#> -0.1597 +#> -0.2700 +#> 0.7575 +#> 1.9979 +#> 0.0179 #> [ CPUFloatType{4} ] #> #> @@ -411,7 +411,7 @@ Examples - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/materialize_internal.html b/dev/reference/materialize_internal.html index be176e3a..0380da8d 100644 --- a/dev/reference/materialize_internal.html +++ b/dev/reference/materialize_internal.html @@ -7,7 +7,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -111,7 +111,7 @@ Details diff --git a/dev/reference/mlr3torch-package.html b/dev/reference/mlr3torch-package.html index f3f82f8a..adf84928 100644 --- a/dev/reference/mlr3torch-package.html +++ b/dev/reference/mlr3torch-package.html @@ -7,7 +7,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -88,7 +88,7 @@ Author< diff --git a/dev/reference/mlr3torch_callbacks.html b/dev/reference/mlr3torch_callbacks.html index 533a7c5e..cd18f2cc 100644 --- a/dev/reference/mlr3torch_callbacks.html +++ b/dev/reference/mlr3torch_callbacks.html @@ -13,7 +13,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -123,7 +123,7 @@ Examples - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr3torch_losses.html b/dev/reference/mlr3torch_losses.html index b5c5d95f..adfa9df1 100644 --- a/dev/reference/mlr3torch_losses.html +++ b/dev/reference/mlr3torch_losses.html @@ -13,7 +13,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -132,7 +132,7 @@ Examples - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr3torch_optimizers.html b/dev/reference/mlr3torch_optimizers.html index 4dc3a335..2798ad8a 100644 --- a/dev/reference/mlr3torch_optimizers.html +++ b/dev/reference/mlr3torch_optimizers.html @@ -13,7 +13,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -134,7 +134,7 @@ Examples - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_backends_lazy.html b/dev/reference/mlr_backends_lazy.html index 5d42c47c..9ca0ae90 100644 --- a/dev/reference/mlr_backends_lazy.html +++ b/dev/reference/mlr_backends_lazy.html @@ -49,7 +49,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -359,6 +359,7 @@ Examples data_formats = "data.table", primary_key = "row_id" ) +#> Warning: DataBackend$initialize argument 'data_formats' is deprecated and will be removed in the future. # Note that the constructor is not called for the calls below # as they can be read from the metadata @@ -373,13 +374,15 @@ Examplescol_info(backend_lazy) #> id type levels #> <char> <char> <list> -#> 1: x numeric -#> 2: y numeric -#> 3: row_id integer +#> 1: x numeric [NULL] +#> 2: y numeric [NULL] +#> 3: row_id integer [NULL] # Only now the backend is constructed backend_lazy$data(1, "x") #> Data is constructed! +#> Warning: DataBackend$data_formats is deprecated and will be removed in the future. +#> Warning: DataBackendDataTable$data argument 'data_format' is deprecated and will be removed in the future. #> x #> <num> #> 1: -0.3872136 @@ -399,7 +402,7 @@ Examples - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_callback_set.checkpoint.html b/dev/reference/mlr_callback_set.checkpoint.html index 542a47e2..627704ae 100644 --- a/dev/reference/mlr_callback_set.checkpoint.html +++ b/dev/reference/mlr_callback_set.checkpoint.html @@ -9,7 +9,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -178,7 +178,7 @@ Arguments - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_callback_set.history.html b/dev/reference/mlr_callback_set.history.html index fc5ea7e5..0e77d9f7 100644 --- a/dev/reference/mlr_callback_set.history.html +++ b/dev/reference/mlr_callback_set.history.html @@ -11,7 +11,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -156,7 +156,7 @@ Arguments - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_callback_set.html b/dev/reference/mlr_callback_set.html index b1745015..15382478 100644 --- a/dev/reference/mlr_callback_set.html +++ b/dev/reference/mlr_callback_set.html @@ -17,7 +17,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -231,7 +231,7 @@ Arguments - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_callback_set.progress.html b/dev/reference/mlr_callback_set.progress.html index 42f700e8..947c10d4 100644 --- a/dev/reference/mlr_callback_set.progress.html +++ b/dev/reference/mlr_callback_set.progress.html @@ -7,7 +7,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -157,7 +157,7 @@ Arguments - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_context_torch.html b/dev/reference/mlr_context_torch.html index ad3d1778..6e79780c 100644 --- a/dev/reference/mlr_context_torch.html +++ b/dev/reference/mlr_context_torch.html @@ -11,7 +11,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -301,7 +301,7 @@ Arguments - Site built with pkgdown 2.1.0. + Site built with pkgdown 2.1.1. diff --git a/dev/reference/mlr_learners.mlp.html b/dev/reference/mlr_learners.mlp.html index eb1b5edd..97ba16ce 100644 --- a/dev/reference/mlr_learners.mlp.html +++ b/dev/reference/mlr_learners.mlp.html @@ -9,7 +9,7 @@ mlr3torch - 0.1.0-9000 + 0.1.1-9000 @@ -211,7 +211,7 @@ Examples# Score the predictions predictions$score() #> classif.ce -#> 0.6666667 +#> 0.68