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Flattens a contiguous range of dims into a tensor.

Source: R/nn-flatten.R
nn_flatten.Rd

For use with nn_sequential.

Usage

nn_flatten(start_dim = 2, end_dim = -1)

Arguments

start_dim

first dim to flatten (default = 2).

end_dim

last dim to flatten (default = -1).

Shape

  • Input: (*, S_start,..., S_i, ..., S_end, *), where S_i is the size at dimension i and * means any number of dimensions including none.

  • Output: (*, S_start*...*S_i*...S_end, *).

See also

nn_unflatten

Examples

if (torch_is_installed()) {
input <- torch_randn(32, 1, 5, 5)
m <- nn_flatten()
m(input)
}
#> torch_tensor
#> Columns 1 to 10 0.3744  0.0925 -0.7475  1.3269 -0.5882 -0.9101 -0.6279  0.4557  1.3038 -0.1351
#>  0.0440 -0.2138 -0.0278  1.0037  0.0318  0.0934 -0.5032 -0.3149  1.0115 -0.1735
#>  0.7518  0.2354  1.4131  0.8548 -0.1538  1.4043  1.0196  1.1634  2.9539  0.4478
#>  1.0757  1.7619 -0.5264 -0.3285 -0.5814 -0.0089 -0.9595  0.5578 -0.5278 -1.7170
#>  0.5469 -0.1644  1.3332  0.3362 -0.2417 -0.9801  0.4898 -0.8932  0.3508  0.0165
#> -0.5230  0.8418 -0.7395 -0.9457 -0.9293  0.4263 -1.2827 -0.2857 -0.1399 -1.2839
#>  0.5236 -0.3141 -0.3451  1.5628  1.3143  1.3873  0.0493 -0.9291  1.6766  0.2576
#>  0.3648 -0.6445  0.3805  0.3740  0.0244  0.4638 -0.3483  0.5682  1.1927 -1.3094
#>  0.4158  0.9726  0.4795  1.0425 -1.3095 -0.8837  0.5346  2.2874 -0.0492  0.3662
#> -1.0092  0.3766  1.3942 -0.0820 -0.6053 -0.0836  1.4400 -1.0055  0.5876 -0.1000
#>  1.6075 -0.5100 -0.5964  1.0486 -1.0101  0.0618  0.0472 -0.1109  0.3942  0.8128
#>  1.6979 -1.4036 -1.6428  0.6355  0.0120  0.8881 -0.4305 -1.8287 -0.0456  1.0808
#>  0.3322 -0.2066 -0.5081 -0.3836  1.1373  0.5725  0.4196 -1.9220  0.6122 -0.1045
#>  0.8598 -1.8472  0.6940 -0.8287  1.3070  1.2191  0.2685  1.5619  0.0417  0.4880
#> -0.7136 -0.3278  0.0781  0.9123 -1.5790  1.4979  0.7009 -0.0148 -0.3330 -1.0480
#> -0.5638  1.5685 -1.5352 -0.7965  0.5877 -0.3810  0.9705  0.8452  1.7031 -0.3664
#> -0.6798 -1.2743 -0.5399 -0.3281  1.3117  2.1113  1.3778  0.7570 -0.9750 -1.6793
#> -1.5096 -1.1139  2.1695 -1.2531  0.5720 -0.2353  0.4416 -0.4713 -0.6930  0.2072
#>  1.1462 -0.6429 -1.7308 -1.0293  0.4296 -1.0975 -1.0955  0.2649 -0.0580 -0.6128
#>  0.2922 -0.1939 -2.6399 -0.9203  0.3420  0.3659  0.1217  0.0736  0.0993 -1.6958
#> -0.3153 -0.1923 -0.4287  0.6218  0.2573  0.4796  1.4541  0.3598  1.6094 -0.2614
#> -0.2670 -1.2747  1.2125  0.7833 -0.8828  0.7027  0.9628 -0.0778  1.1711 -0.2686
#> -0.5945 -0.0006 -0.7443 -1.9199 -0.0499 -0.4802 -0.6031 -0.0402  0.2481  0.4819
#>  0.4739 -1.6936 -1.8370  0.0686  1.7653  0.0798  0.7916 -1.8765 -0.0145 -0.7405
#>  1.2357 -1.5596 -1.3789  0.2428 -1.8605 -0.5010  0.3907 -1.0441 -0.6823  0.2349
#>  0.0456  0.6355 -0.1976 -0.5344 -0.3145  0.4697  0.7779 -1.3616 -0.2708 -0.8309
#> -0.4043 -0.2614  1.4862 -0.4530 -0.3937  0.8272 -1.6846  0.0224 -0.7566  0.5623
#>  0.8132  1.2623  0.1362  1.1594  0.5356  0.8297 -0.4972  0.0952  0.5013 -0.5851
#>  0.9413  0.3696  0.0327 -1.5787  1.2504  0.8851  1.4210  2.1640  0.2996  0.5636
#> -0.3994  1.0721 -2.6220  0.2867 -0.8387  0.1687 -0.3834  0.3643 -0.7581 -1.3066
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{32,25} ]