A shortcut to create a feed-forward block (MLP block)
nn_mlp(..., activation = nnf_relu)(nn_module, function integer, character)
An arbitrary number of arguments, than can be:
* nn_module - e.g. torch::nn_relu()
* function - e.g. torch::nnf_relu
* character - e.g. selu, which is converted to nnf_selu
* integer -
Used if only integers are specified. By default: nnf_relu
nn_mlp(10, 1)
#> An `nn_module` containing 11 parameters.
#>
#> ── Modules ─────────────────────────────────────────────────────────────────────
#> • layer_1: <nn_linear> #11 parameters
nn_mlp(30, 10, 1)
#> An `nn_module` containing 321 parameters.
#>
#> ── Modules ─────────────────────────────────────────────────────────────────────
#> • layer_1: <nn_linear> #310 parameters
#> • layer_2: <nn_linear> #11 parameters
# Simple forward pass
net <- nn_mlp(4, 2, 1)
x <- as_tensor(iris[, 1:4])
net(x)
#> torch_tensor
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> 0
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{150,1} ][ grad_fn = <ReluBackward0> ]
# Simple forward pass with identity function
net <- nn_mlp(4, 2, 1, activation = function (x) x)
x <- as_tensor(iris[, 1:4])
net(x)
#> torch_tensor
#> 1.1206
#> 1.0234
#> 1.0382
#> 1.0559
#> 1.1376
#> 1.2832
#> 1.1056
#> 1.1192
#> 0.9958
#> 1.0487
#> 1.1825
#> 1.1348
#> 1.0091
#> 0.9430
#> 1.1884
#> 1.3440
#> 1.2068
#> 1.1331
#> 1.2572
#> 1.2084
#> 1.1645
#> 1.2022
#> 1.0541
#> 1.1780
#> 1.1921
#> 1.0634
#> 1.1634
#> 1.1415
#> 1.1036
#> 1.0955
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{150,1} ][ grad_fn = <AddmmBackward> ]