• using R Under development (unstable) (2025-11-21 r89046)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-15 (Debian 15.2.0-7) 15.2.0     GNU Fortran (Debian 15.2.0-7) 15.2.0
• running under: Debian GNU/Linux forky/sid
• using session charset: UTF-8
• checking for file ‘deepregression/DESCRIPTION’ ... OK
• this is package ‘deepregression’ version ‘2.3.2’
• package encoding: UTF-8
• checking CRAN incoming feasibility ... [1s/1s] OK
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for executable files ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking whether package ‘deepregression’ can be installed ... OK See the install log for details.
• checking package directory ... OK
• checking for future file timestamps ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... [3s/4s] OK
• checking whether the package can be loaded with stated dependencies ... [3s/4s] OK
• checking whether the package can be unloaded cleanly ... [3s/3s] OK
• checking whether the namespace can be loaded with stated dependencies ... [3s/4s] OK
• checking whether the namespace can be unloaded cleanly ... [3s/4s] OK
• checking loading without being on the library search path ... [3s/4s] OK
• checking whether startup messages can be suppressed ... [3s/4s] OK
• checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [21s/26s] OK
• checking Rd files ... [1s/1s] OK
• checking Rd metadata ... OK
• checking Rd line widths ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking examples ... [3s/3s] OK
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [359s/532s] ERROR   Running ‘testthat.R’ [359s/532s] Running the tests in ‘tests/testthat.R’ failed. Complete output:   > library(testthat)   > library(deepregression)   Loading required package: tensorflow   Loading required package: tfprobability   Loading required package: keras   The keras package is deprecated. Use the keras3 package instead.   >   > if (reticulate::py_module_available("tensorflow") &   + reticulate::py_module_available("keras") &   + .Platform$OS.type != "windows"){   + test_check("deepregression")   + }   Downloading pygments (1.2MiB)   Downloading tf-keras (1.6MiB)   Downloading tensorboard (5.2MiB)   Downloading keras (1.4MiB)   Downloading h5py (4.9MiB)   Downloading grpcio (6.3MiB)   Downloading tensorflow (615.1MiB)   Downloading ml-dtypes (4.8MiB)   Downloading numpy (17.1MiB)   Downloading tensorflow-probability (6.7MiB)    Downloaded pygments    Downloaded ml-dtypes    Downloaded h5py    Downloaded grpcio    Downloaded keras    Downloaded tf-keras    Downloaded tensorboard    Downloaded numpy    Downloaded tensorflow-probability    Downloaded tensorflow   Installed 43 packages in 371ms   2025-11-22 15:35:45.912348: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.   2025-11-22 15:35:45.913885: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.   2025-11-22 15:35:45.919636: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.   2025-11-22 15:35:45.932965: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered   WARNING: All log messages before absl::InitializeLog() is called are written to STDERR   E0000 00:00:1763822145.953673 1037078 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered   E0000 00:00:1763822145.958787 1037078 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered   W0000 00:00:1763822145.975507 1037078 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   W0000 00:00:1763822145.975702 1037078 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   W0000 00:00:1763822145.975707 1037078 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   W0000 00:00:1763822145.975712 1037078 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   2025-11-22 15:35:45.980265: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.   To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.   Saving _problems/test_customtraining_torch-6.R   Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz          8192/11490434 [..............................] - ETA: 0s      16384/11490434 [..............................] - ETA: 45s      49152/11490434 [..............................] - ETA: 40s      81920/11490434 [..............................] - ETA: 40s     147456/11490434 [..............................] - ETA: 28s     196608/11490434 [..............................] - ETA: 23s     278528/11490434 [..............................] - ETA: 18s     393216/11490434 [>.............................] - ETA: 14s     532480/11490434 [>.............................] - ETA: 11s     770048/11490434 [=>............................] - ETA: 8s    1048576/11490434 [=>............................] - ETA: 6s    1523712/11490434 [==>...........................] - ETA: 4s    2269184/11490434 [====>.........................] - ETA: 3s    3211264/11490434 [=======>......................] - ETA: 2s    4358144/11490434 [==========>...................] - ETA: 1s    6594560/11490434 [================>.............] - ETA: 0s    8699904/11490434 [=====================>........] - ETA: 0s   10362880/11490434 [==========================>...] - ETA: 0s   11490434/11490434 [==============================] - 1s 0us/step   Saving _problems/test_data_handler_torch-78.R   2025-11-22 15:36:04.437852: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)   Epoch 1/2       1/15 [=>............................] - ETA: 24s - loss: 2.2580    9/15 [=================>............] - ETA: 0s - loss: 2.2232   15/15 [==============================] - 2s 34ms/step - loss: 2.1942 - val_loss: 2.1377   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.1032   13/15 [=========================>....] - ETA: 0s - loss: 2.0592   15/15 [==============================] - 0s 15ms/step - loss: 2.0505 - val_loss: 1.9994   Epoch 1/2       1/15 [=>............................] - ETA: 14s - loss: 2.6629   10/15 [===================>..........] - ETA: 0s - loss: 2.6533   15/15 [==============================] - 2s 35ms/step - loss: 2.6533 - val_loss: 2.6393   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.6358   11/15 [=====================>........] - ETA: 0s - loss: 2.6352   15/15 [==============================] - 0s 12ms/step - loss: 2.6365 - val_loss: 2.6227   Epoch 1/2       1/15 [=>............................] - ETA: 20s - loss: 2.9965   12/15 [=======================>......] - ETA: 0s - loss: 2.9363   15/15 [==============================] - 2s 30ms/step - loss: 2.8998 - val_loss: 1.6201   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.1770   10/15 [===================>..........] - ETA: 0s - loss: 2.7329   15/15 [==============================] - 0s 12ms/step - loss: 2.6616 - val_loss: 1.5446   Epoch 1/2       1/15 [=>............................] - ETA: 18s - loss: 4.1701   11/15 [=====================>........] - ETA: 0s - loss: 4.1792   15/15 [==============================] - 2s 30ms/step - loss: 4.0062 - val_loss: 3.4240   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 4.3026   10/15 [===================>..........] - ETA: 0s - loss: 3.9434   15/15 [==============================] - 0s 11ms/step - loss: 3.9103 - val_loss: 3.3606   Epoch 1/3      1/3 [=========>....................] - ETA: 1s - loss: 7.9864   3/3 [==============================] - 1s 143ms/step - loss: 8.7055 - val_loss: 7.2396   Epoch 2/3      1/3 [=========>....................] - ETA: 0s - loss: 9.5053   3/3 [==============================] - 0s 44ms/step - loss: 8.6326 - val_loss: 7.1833   Epoch 3/3      1/3 [=========>....................] - ETA: 0s - loss: 8.5150   3/3 [==============================] - 0s 38ms/step - loss: 8.5566 - val_loss: 7.1282   Epoch 1/2       1/15 [=>............................] - ETA: 18s - loss: 2.6300   11/15 [=====================>........] - ETA: 0s - loss: 2.6286   15/15 [==============================] - 2s 39ms/step - loss: 2.6282 - val_loss: 2.6186   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.6212   14/15 [===========================>..] - ETA: 0s - loss: 2.6122   15/15 [==============================] - 0s 10ms/step - loss: 2.6124 - val_loss: 2.6029   Epoch 1/2       1/15 [=>............................] - ETA: 16s - loss: 653.6502    9/15 [=================>............] - ETA: 0s - loss: 1222.3798   15/15 [==============================] - 2s 39ms/step - loss: 1248.0040 - val_loss: 1370.1050   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 755.2901    7/15 [=============>................] - ETA: 0s - loss: 1083.8412   15/15 [==============================] - 0s 14ms/step - loss: 960.7450 - val_loss: 1055.7158   Epoch 1/2       1/15 [=>............................] - ETA: 20s - loss: 927.8141   12/15 [=======================>......] - ETA: 0s - loss: 2380.3286   15/15 [==============================] - 2s 31ms/step - loss: 3026.8003 - val_loss: 321.5525   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 991.2161   13/15 [=========================>....] - ETA: 0s - loss: 2603.2161   15/15 [==============================] - 0s 14ms/step - loss: 2679.4148 - val_loss: 292.7626   Epoch 1/2       1/15 [=>............................] - ETA: 25s - loss: 2.3149   10/15 [===================>..........] - ETA: 0s - loss: 2.2399   15/15 [==============================] - 2s 43ms/step - loss: 2.2516 - val_loss: 2.2750   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.1948   11/15 [=====================>........] - ETA: 0s - loss: 2.2114   15/15 [==============================] - 0s 14ms/step - loss: 2.2132 - val_loss: 2.2328   Saving _problems/test_deepregression_torch-10.R   Saving _problems/test_deepregression_torch-117.R   Saving _problems/test_deepregression_torch-158.R   Saving _problems/test_deepregression_torch-190.R   Saving _problems/test_deepregression_torch-229.R   Fitting member 1 ...Epoch 1/10       1/32 [..............................] - ETA: 25s - loss: 2.3293   10/32 [========>.....................] - ETA: 0s - loss: 2.3532   19/32 [================>.............] - ETA: 0s - loss: 2.3442   28/32 [=========================>....] - ETA: 0s - loss: 2.3344   32/32 [==============================] - 1s 6ms/step - loss: 2.3303   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 2.3269    9/32 [=======>......................] - ETA: 0s - loss: 2.3223   19/32 [================>.............] - ETA: 0s - loss: 2.3079   29/32 [==========================>...] - ETA: 0s - loss: 2.3010   32/32 [==============================] - 0s 6ms/step - loss: 2.2977   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 2.3270   12/32 [==========>...................] - ETA: 0s - loss: 2.2760   21/32 [==================>...........] - ETA: 0s - loss: 2.2748   31/32 [============================>.] - ETA: 0s - loss: 2.2615   32/32 [==============================] - 0s 6ms/step - loss: 2.2650   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 2.2270   11/32 [=========>....................] - ETA: 0s - loss: 2.2375   21/32 [==================>...........] - ETA: 0s - loss: 2.2214   32/32 [==============================] - 0s 5ms/step - loss: 2.2325   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 2.2185    9/32 [=======>......................] - ETA: 0s - loss: 2.2298   22/32 [===================>..........] - ETA: 0s - loss: 2.2113   31/32 [============================>.] - ETA: 0s - loss: 2.2011   32/32 [==============================] - 0s 6ms/step - loss: 2.2002   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 2.1431   10/32 [========>.....................] - ETA: 0s - loss: 2.1695   19/32 [================>.............] - ETA: 0s - loss: 2.1769   30/32 [===========================>..] - ETA: 0s - loss: 2.1702   32/32 [==============================] - 0s 6ms/step - loss: 2.1681   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 2.2840   11/32 [=========>....................] - ETA: 0s - loss: 2.1667   21/32 [==================>...........] - ETA: 0s - loss: 2.1390   31/32 [============================>.] - ETA: 0s - loss: 2.1356   32/32 [==============================] - 0s 5ms/step - loss: 2.1359   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 2.0769   11/32 [=========>....................] - ETA: 0s - loss: 2.1122   23/32 [====================>.........] - ETA: 0s - loss: 2.1145   32/32 [==============================] - 0s 5ms/step - loss: 2.1036   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 2.0602   11/32 [=========>....................] - ETA: 0s - loss: 2.1029   23/32 [====================>.........] - ETA: 0s - loss: 2.0765   31/32 [============================>.] - ETA: 0s - loss: 2.0715   32/32 [==============================] - 0s 5ms/step - loss: 2.0717   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 2.0980   17/32 [==============>...............] - ETA: 0s - loss: 2.0371   28/32 [=========================>....] - ETA: 0s - loss: 2.0400   32/32 [==============================] - 0s 4ms/step - loss: 2.0400      Done in 2.809544 secs   Fitting member 2 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 2.1814   12/32 [==========>...................] - ETA: 0s - loss: 2.3720   22/32 [===================>..........] - ETA: 0s - loss: 2.3458   31/32 [============================>.] - ETA: 0s - loss: 2.3332   32/32 [==============================] - 0s 6ms/step - loss: 2.3312   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 2.4545   10/32 [========>.....................] - ETA: 0s - loss: 2.3328   18/32 [===============>..............] - ETA: 0s - loss: 2.3090   27/32 [========================>.....] - ETA: 0s - loss: 2.2986   32/32 [==============================] - 0s 7ms/step - loss: 2.2785   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 2.5406   11/32 [=========>....................] - ETA: 0s - loss: 2.2785   20/32 [=================>............] - ETA: 0s - loss: 2.2709   30/32 [===========================>..] - ETA: 0s - loss: 2.2240   32/32 [==============================] - 0s 6ms/step - loss: 2.2334   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 2.0854   13/32 [===========>..................] - ETA: 0s - loss: 2.1742   22/32 [===================>..........] - ETA: 0s - loss: 2.1684   29/32 [==========================>...] - ETA: 0s - loss: 2.1824   32/32 [==============================] - 0s 6ms/step - loss: 2.1937   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 2.2393    8/32 [======>.......................] - ETA: 0s - loss: 2.2023   15/32 [=============>................] - ETA: 0s - loss: 2.1972   21/32 [==================>...........] - ETA: 0s - loss: 2.1809   29/32 [==========================>...] - ETA: 0s - loss: 2.1651   32/32 [==============================] - 0s 8ms/step - loss: 2.1597   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 2.0225   11/32 [=========>....................] - ETA: 0s - loss: 2.1111   21/32 [==================>...........] - ETA: 0s - loss: 2.1341   28/32 [=========================>....] - ETA: 0s - loss: 2.1318   32/32 [==============================] - 0s 6ms/step - loss: 2.1276   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 2.4568   10/32 [========>.....................] - ETA: 0s - loss: 2.1524   15/32 [=============>................] - ETA: 0s - loss: 2.1130   24/32 [=====================>........] - ETA: 0s - loss: 2.0974   32/32 [==============================] - 0s 7ms/step - loss: 2.0961   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 1.9680   13/32 [===========>..................] - ETA: 0s - loss: 2.0686   20/32 [=================>............] - ETA: 0s - loss: 2.0726   28/32 [=========================>....] - ETA: 0s - loss: 2.0678   32/32 [==============================] - 0s 6ms/step - loss: 2.0644   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 2.0730   12/32 [==========>...................] - ETA: 0s - loss: 2.0829   24/32 [=====================>........] - ETA: 0s - loss: 2.0498   32/32 [==============================] - 0s 5ms/step - loss: 2.0336   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 2.1117    9/32 [=======>......................] - ETA: 0s - loss: 2.0130   17/32 [==============>...............] - ETA: 0s - loss: 1.9820   24/32 [=====================>........] - ETA: 0s - loss: 1.9973   32/32 [==============================] - ETA: 0s - loss: 2.0026   32/32 [==============================] - 0s 7ms/step - loss: 2.0026      Done in 2.270218 secs   Fitting member 3 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 41.9180   13/32 [===========>..................] - ETA: 0s - loss: 46.0774   21/32 [==================>...........] - ETA: 0s - loss: 41.5402   28/32 [=========================>....] - ETA: 0s - loss: 39.4926   32/32 [==============================] - 0s 6ms/step - loss: 39.2828   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 34.1315    8/32 [======>.......................] - ETA: 0s - loss: 30.4575   19/32 [================>.............] - ETA: 0s - loss: 28.2665   29/32 [==========================>...] - ETA: 0s - loss: 27.0886   32/32 [==============================] - 0s 6ms/step - loss: 27.2884   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 27.6960   13/32 [===========>..................] - ETA: 0s - loss: 25.7246   21/32 [==================>...........] - ETA: 0s - loss: 23.2212   30/32 [===========================>..] - ETA: 0s - loss: 21.5371   32/32 [==============================] - 0s 6ms/step - loss: 21.7021   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 13.7574    7/32 [=====>........................] - ETA: 0s - loss: 17.2677   17/32 [==============>...............] - ETA: 0s - loss: 18.7698   26/32 [=======================>......] - ETA: 0s - loss: 18.4618   32/32 [==============================] - 0s 6ms/step - loss: 18.2036   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 15.2942   12/32 [==========>...................] - ETA: 0s - loss: 15.9608   21/32 [==================>...........] - ETA: 0s - loss: 16.1793   29/32 [==========================>...] - ETA: 0s - loss: 15.9619   32/32 [==============================] - 0s 6ms/step - loss: 15.8282   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 9.8128   13/32 [===========>..................] - ETA: 0s - loss: 13.8890   22/32 [===================>..........] - ETA: 0s - loss: 13.7030   32/32 [==============================] - 0s 5ms/step - loss: 14.0666   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 14.0067    9/32 [=======>......................] - ETA: 0s - loss: 14.2036   20/32 [=================>............] - ETA: 0s - loss: 12.8080   32/32 [==============================] - ETA: 0s - loss: 12.6950   32/32 [==============================] - 0s 5ms/step - loss: 12.6950   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 12.2746    9/32 [=======>......................] - ETA: 0s - loss: 11.6077   22/32 [===================>..........] - ETA: 0s - loss: 11.5587   32/32 [==============================] - 0s 5ms/step - loss: 11.5684   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 13.3544   13/32 [===========>..................] - ETA: 0s - loss: 10.4875   24/32 [=====================>........] - ETA: 0s - loss: 10.7448   32/32 [==============================] - 0s 5ms/step - loss: 10.6440   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 8.5922   13/32 [===========>..................] - ETA: 0s - loss: 9.2266   21/32 [==================>...........] - ETA: 0s - loss: 9.3876   32/32 [==============================] - ETA: 0s - loss: 9.8495   32/32 [==============================] - 0s 5ms/step - loss: 9.8495      Done in 2.001302 secs   Fitting member 4 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 2.8816    8/32 [======>.......................] - ETA: 0s - loss: 3.2879   19/32 [================>.............] - ETA: 0s - loss: 3.0365   26/32 [=======================>......] - ETA: 0s - loss: 2.9227   32/32 [==============================] - 0s 6ms/step - loss: 2.9588   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 2.3825   15/32 [=============>................] - ETA: 0s - loss: 2.9621   29/32 [==========================>...] - ETA: 0s - loss: 2.8595   32/32 [==============================] - 0s 4ms/step - loss: 2.9011   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 3.9315   17/32 [==============>...............] - ETA: 0s - loss: 2.9411   31/32 [============================>.] - ETA: 0s - loss: 2.8401   32/32 [==============================] - 0s 4ms/step - loss: 2.8534   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 2.3602   15/32 [=============>................] - ETA: 0s - loss: 2.6057   30/32 [===========================>..] - ETA: 0s - loss: 2.7315   32/32 [==============================] - 0s 4ms/step - loss: 2.8062   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 2.7419   17/32 [==============>...............] - ETA: 0s - loss: 2.7865   32/32 [==============================] - 0s 3ms/step - loss: 2.7623   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 1.7251   19/32 [================>.............] - ETA: 0s - loss: 2.6876   32/32 [==============================] - 0s 3ms/step - loss: 2.7193   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 2.8260   15/32 [=============>................] - ETA: 0s - loss: 2.6259   25/32 [======================>.......] - ETA: 0s - loss: 2.6624   32/32 [==============================] - 0s 4ms/step - loss: 2.6774   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 2.2710   18/32 [===============>..............] - ETA: 0s - loss: 2.7490   32/32 [==============================] - 0s 3ms/step - loss: 2.6383   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 3.0022   17/32 [==============>...............] - ETA: 0s - loss: 2.6928   29/32 [==========================>...] - ETA: 0s - loss: 2.6502   32/32 [==============================] - 0s 4ms/step - loss: 2.6007   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 2.6980   14/32 [============>.................] - ETA: 0s - loss: 2.4191   32/32 [==============================] - ETA: 0s - loss: 2.5630   32/32 [==============================] - 0s 4ms/step - loss: 2.5630      Done in 1.409164 secs   Fitting member 5 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 112.3703   10/32 [========>.....................] - ETA: 0s - loss: 155.6538   19/32 [================>.............] - ETA: 0s - loss: 150.9393   30/32 [===========================>..] - ETA: 0s - loss: 139.5462   32/32 [==============================] - 0s 5ms/step - loss: 139.0890   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 141.4559   15/32 [=============>................] - ETA: 0s - loss: 104.8303   21/32 [==================>...........] - ETA: 0s - loss: 99.5094   32/32 [==============================] - 0s 5ms/step - loss: 95.6168   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 97.2787   14/32 [============>.................] - ETA: 0s - loss: 88.5640   29/32 [==========================>...] - ETA: 0s - loss: 74.4262   32/32 [==============================] - 0s 4ms/step - loss: 74.6476   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 42.6719    9/32 [=======>......................] - ETA: 0s - loss: 65.1981   22/32 [===================>..........] - ETA: 0s - loss: 64.0068   32/32 [==============================] - 0s 4ms/step - loss: 61.8040   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 51.5790   21/32 [==================>...........] - ETA: 0s - loss: 53.9275   32/32 [==============================] - 0s 3ms/step - loss: 53.1899   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 35.5863   17/32 [==============>...............] - ETA: 0s - loss: 46.4085   27/32 [========================>.....] - ETA: 0s - loss: 47.2335   32/32 [==============================] - 0s 4ms/step - loss: 46.9254   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 43.7696   20/32 [=================>............] - ETA: 0s - loss: 42.1246   32/32 [==============================] - ETA: 0s - loss: 42.0744   32/32 [==============================] - 0s 3ms/step - loss: 42.0744   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 43.0723   12/32 [==========>...................] - ETA: 0s - loss: 39.9295   24/32 [=====================>........] - ETA: 0s - loss: 37.5887   32/32 [==============================] - 0s 5ms/step - loss: 38.1152   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 45.3285   12/32 [==========>...................] - ETA: 0s - loss: 35.6146   24/32 [=====================>........] - ETA: 0s - loss: 35.3619   32/32 [==============================] - 0s 5ms/step - loss: 34.8808   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 29.3974   22/32 [===================>..........] - ETA: 0s - loss: 30.7542   32/32 [==============================] - 0s 3ms/step - loss: 32.1088      Done in 2.763182 secs   Epoch 1/2      1/3 [=========>....................] - ETA: 2s - loss: 2.3341   3/3 [==============================] - 2s 284ms/step - loss: 2.3038 - val_loss: 2.2154   Epoch 2/2      1/3 [=========>....................] - ETA: 0s - loss: 2.3662   3/3 [==============================] - 0s 66ms/step - loss: 2.3004 - val_loss: 2.2128   Epoch 1/2      1/3 [=========>....................] - ETA: 0s - loss: 52.8941   3/3 [==============================] - 0s 105ms/step - loss: 47.0024 - val_loss: 27.1291   Epoch 2/2      1/3 [=========>....................] - ETA: 0s - loss: 49.6579   3/3 [==============================] - 0s 35ms/step - loss: 46.6172 - val_loss: 26.8568   Saving _problems/test_ensemble_torch-17.R   Saving _problems/test_ensemble_torch-63.R   Fitting normal   Fitting bernoulli   Fitting bernoulli_prob   WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7f12b51defc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.   Fitting beta   WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x7f12b4faede0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.   Fitting betar   Fitting chi2   Fitting chi   Fitting exponential   Fitting gamma   Fitting gammar   Fitting gumbel   Fitting half_normal   Fitting horseshoe   Fitting inverse_gaussian   Fitting laplace   Fitting log_normal   Fitting logistic   Fitting negbinom   Fitting negbinom   Fitting pareto_ls   Fitting poisson   Fitting poisson_lograte   Saving _problems/test_families_torch-82.R   Saving _problems/test_layers_torch-6.R   Saving _problems/test_methods_torch-23.R   Epoch 1/2       1/29 [>.............................] - ETA: 27s - loss: 11.2004   13/29 [============>.................] - ETA: 0s - loss: 10.5970   23/29 [======================>.......] - ETA: 0s - loss: 10.4031   29/29 [==============================] - 2s 20ms/step - loss: 10.6607 - val_loss: 7.6350   Epoch 2/2       1/29 [>.............................] - ETA: 0s - loss: 7.1379   12/29 [===========>..................] - ETA: 0s - loss: 9.7647   23/29 [======================>.......] - ETA: 0s - loss: 9.7949   29/29 [==============================] - 0s 8ms/step - loss: 9.5296 - val_loss: 6.8647   Epoch 1/10       1/29 [>.............................] - ETA: 1:19 - loss: 6.4103    4/29 [===>..........................] - ETA: 0s - loss: 7.1513    7/29 [======>.......................] - ETA: 0s - loss: 8.3079   10/29 [=========>....................] - ETA: 0s - loss: 8.4085   13/29 [============>.................] - ETA: 0s - loss: 8.6694   17/29 [================>.............] - ETA: 0s - loss: 8.3282   21/29 [====================>.........] - ETA: 0s - loss: 8.3033   25/29 [========================>.....] - ETA: 0s - loss: 8.1120   29/29 [==============================] - ETA: 0s - loss: 7.8712   29/29 [==============================] - 4s 38ms/step - loss: 7.8712 - val_loss: 8.8751   Epoch 2/10       1/29 [>.............................] - ETA: 0s - loss: 6.9897    4/29 [===>..........................] - ETA: 0s - loss: 7.7692    7/29 [======>.......................] - ETA: 0s - loss: 7.0320   10/29 [=========>....................] - ETA: 0s - loss: 7.5030   14/29 [=============>................] - ETA: 0s - loss: 7.4321   17/29 [================>.............] - ETA: 0s - loss: 7.6919   21/29 [====================>.........] - ETA: 0s - loss: 7.8491   25/29 [========================>.....] - ETA: 0s - loss: 7.4780   29/29 [==============================] - ETA: 0s - loss: 7.4694   29/29 [==============================] - 1s 21ms/step - loss: 7.4694 - val_loss: 8.4332   Epoch 3/10       1/29 [>.............................] - ETA: 0s - loss: 14.7798    4/29 [===>..........................] - ETA: 0s - loss: 10.5156    8/29 [=======>......................] - ETA: 0s - loss: 9.1643   12/29 [===========>..................] - ETA: 0s - loss: 8.4318   16/29 [===============>..............] - ETA: 0s - loss: 7.8376   19/29 [==================>...........] - ETA: 0s - loss: 7.4744   21/29 [====================>.........] - ETA: 0s - loss: 7.4352   25/29 [========================>.....] - ETA: 0s - loss: 7.2180   29/29 [==============================] - 1s 19ms/step - loss: 7.1170 - val_loss: 8.0236   Epoch 4/10       1/29 [>.............................] - ETA: 0s - loss: 9.3139    4/29 [===>..........................] - ETA: 0s - loss: 7.8766    6/29 [=====>........................] - ETA: 0s - loss: 7.3959   10/29 [=========>....................] - ETA: 0s - loss: 6.8699   14/29 [=============>................] - ETA: 0s - loss: 6.6848   18/29 [=================>............] - ETA: 0s - loss: 6.7634   22/29 [=====================>........] - ETA: 0s - loss: 6.8245   25/29 [========================>.....] - ETA: 0s - loss: 6.8528   28/29 [===========================>..] - ETA: 0s - loss: 6.8088   29/29 [==============================] - 1s 22ms/step - loss: 6.7865 - val_loss: 7.6687   Epoch 5/10       1/29 [>.............................] - ETA: 0s - loss: 3.8834    5/29 [====>.........................] - ETA: 0s - loss: 5.7258    8/29 [=======>......................] - ETA: 0s - loss: 7.1880   12/29 [===========>..................] - ETA: 0s - loss: 7.0123   16/29 [===============>..............] - ETA: 0s - loss: 6.7281   19/29 [==================>...........] - ETA: 0s - loss: 6.5392   23/29 [======================>.......] - ETA: 0s - loss: 6.6037   26/29 [=========================>....] - ETA: 0s - loss: 6.3531   29/29 [==============================] - ETA: 0s - loss: 6.4899   29/29 [==============================] - 1s 20ms/step - loss: 6.4899 - val_loss: 7.3231   Epoch 6/10       1/29 [>.............................] - ETA: 0s - loss: 5.2272    5/29 [====>.........................] - ETA: 0s - loss: 7.8718    8/29 [=======>......................] - ETA: 0s - loss: 7.1353   12/29 [===========>..................] - ETA: 0s - loss: 6.6544   15/29 [==============>...............] - ETA: 0s - loss: 6.4547   20/29 [===================>..........] - ETA: 0s - loss: 6.6261   24/29 [=======================>......] - ETA: 0s - loss: 6.2656   28/29 [===========================>..] - ETA: 0s - loss: 6.1921   29/29 [==============================] - 1s 18ms/step - loss: 6.2000 - val_loss: 6.9819   Epoch 7/10       1/29 [>.............................] - ETA: 0s - loss: 6.7016    5/29 [====>.........................] - ETA: 0s - loss: 6.6629    9/29 [========>.....................] - ETA: 0s - loss: 6.3465   12/29 [===========>..................] - ETA: 0s - loss: 6.2371   16/29 [===============>..............] - ETA: 0s - loss: 6.1589   19/29 [==================>...........] - ETA: 0s - loss: 6.1555   23/29 [======================>.......] - ETA: 0s - loss: 5.9828   27/29 [==========================>...] - ETA: 0s - loss: 5.8750   29/29 [==============================] - 1s 20ms/step - loss: 5.9123 - val_loss: 6.6514   Epoch 8/10       1/29 [>.............................] - ETA: 0s - loss: 5.7023    5/29 [====>.........................] - ETA: 0s - loss: 6.1573    8/29 [=======>......................] - ETA: 0s - loss: 5.6464   11/29 [==========>...................] - ETA: 0s - loss: 5.6565   15/29 [==============>...............] - ETA: 0s - loss: 5.7464   19/29 [==================>...........] - ETA: 0s - loss: 5.6294   23/29 [======================>.......] - ETA: 0s - loss: 5.6690   28/29 [===========================>..] - ETA: 0s - loss: 5.6376   29/29 [==============================] - 1s 20ms/step - loss: 5.6402 - val_loss: 6.3442   Epoch 9/10       1/29 [>.............................] - ETA: 0s - loss: 4.7939    6/29 [=====>........................] - ETA: 0s - loss: 5.2871   11/29 [==========>...................] - ETA: 0s - loss: 5.3717   14/29 [=============>................] - ETA: 0s - loss: 5.5927   17/29 [================>.............] - ETA: 0s - loss: 5.3213   21/29 [====================>.........] - ETA: 0s - loss: 5.4728   24/29 [=======================>......] - ETA: 0s - loss: 5.5022   28/29 [===========================>..] - ETA: 0s - loss: 5.3859   29/29 [==============================] - 1s 18ms/step - loss: 5.3794 - val_loss: 6.0578   Epoch 10/10       1/29 [>.............................] - ETA: 0s - loss: 4.1903    5/29 [====>.........................] - ETA: 0s - loss: 5.5030    8/29 [=======>......................] - ETA: 0s - loss: 5.4467   12/29 [===========>..................] - ETA: 0s - loss: 5.0002   15/29 [==============>...............] - ETA: 0s - loss: 5.0702   18/29 [=================>............] - ETA: 0s - loss: 5.2039   22/29 [=====================>........] - ETA: 0s - loss: 5.1664   25/29 [========================>.....] - ETA: 0s - loss: 5.1996   29/29 [==============================] - 1s 20ms/step - loss: 5.1438 - val_loss: 5.7953   Epoch 1/10       1/29 [>.............................] - ETA: 1:13 - loss: 1.4933    4/29 [===>..........................] - ETA: 0s - loss: 1.4923    8/29 [=======>......................] - ETA: 0s - loss: 1.4910   12/29 [===========>..................] - ETA: 0s - loss: 1.4898   15/29 [==============>...............] - ETA: 0s - loss: 1.4888   18/29 [=================>............] - ETA: 0s - loss: 1.4879   21/29 [====================>.........] - ETA: 0s - loss: 1.4870   26/29 [=========================>....] - ETA: 0s - loss: 1.4854   29/29 [==============================] - 4s 42ms/step - loss: 1.4848 - val_loss: 1.4755   Epoch 2/10       1/29 [>.............................] - ETA: 0s - loss: 1.4755    4/29 [===>..........................] - ETA: 0s - loss: 1.4746    7/29 [======>.......................] - ETA: 0s - loss: 1.4738   10/29 [=========>....................] - ETA: 0s - loss: 1.4729   14/29 [=============>................] - ETA: 0s - loss: 1.4718   18/29 [=================>............] - ETA: 0s - loss: 1.4707   21/29 [====================>.........] - ETA: 0s - loss: 1.4698   24/29 [=======================>......] - ETA: 0s - loss: 1.4690   29/29 [==============================] - ETA: 0s - loss: 1.4679   29/29 [==============================] - 1s 20ms/step - loss: 1.4679 - val_loss: 1.4596   Epoch 3/10       1/29 [>.............................] - ETA: 0s - loss: 1.4597    5/29 [====>.........................] - ETA: 0s - loss: 1.4587    8/29 [=======>......................] - ETA: 0s - loss: 1.4579   11/29 [==========>...................] - ETA: 0s - loss: 1.4572   14/29 [=============>................] - ETA: 0s - loss: 1.4564   17/29 [================>.............] - ETA: 0s - loss: 1.4557   20/29 [===================>..........] - ETA: 0s - loss: 1.4550   23/29 [======================>.......] - ETA: 0s - loss: 1.4543   26/29 [=========================>....] - ETA: 0s - loss: 1.4536   29/29 [==============================] - ETA: 0s - loss: 1.4531   29/29 [==============================] - 1s 26ms/step - loss: 1.4531 - val_loss: 1.4458   Epoch 4/10       1/29 [>.............................] - ETA: 0s - loss: 1.4459    4/29 [===>..........................] - ETA: 0s - loss: 1.4452    7/29 [======>.......................] - ETA: 0s - loss: 1.4445   10/29 [=========>....................] - ETA: 0s - loss: 1.4439   14/29 [=============>................] - ETA: 0s - loss: 1.4430   16/29 [===============>..............] - ETA: 0s - loss: 1.4426   20/29 [===================>..........] - ETA: 0s - loss: 1.4418   23/29 [======================>.......] - ETA: 0s - loss: 1.4411   26/29 [=========================>....] - ETA: 0s - loss: 1.4405   29/29 [==============================] - 1s 23ms/step - loss: 1.4401 - val_loss: 1.4336   Epoch 5/10       1/29 [>.............................] - ETA: 0s - loss: 1.4338    5/29 [====>.........................] - ETA: 0s - loss: 1.4330    8/29 [=======>......................] - ETA: 0s - loss: 1.4324   12/29 [===========>..................] - ETA: 0s - loss: 1.4316   15/29 [==============>...............] - ETA: 0s - loss: 1.4311   17/29 [================>.............] - ETA: 0s - loss: 1.4307   19/29 [==================>...........] - ETA: 0s - loss: 1.4303   22/29 [=====================>........] - ETA: 0s - loss: 1.4298   24/29 [=======================>......] - ETA: 0s - loss: 1.4294   28/29 [===========================>..] - ETA: 0s - loss: 1.4287   29/29 [==============================] - 1s 23ms/step - loss: 1.4287 - val_loss: 1.4230   Epoch 6/10       1/29 [>.............................] - ETA: 0s - loss: 1.4231    5/29 [====>.........................] - ETA: 0s - loss: 1.4225    9/29 [========>.....................] - ETA: 0s - loss: 1.4218   12/29 [===========>..................] - ETA: 0s - loss: 1.4213   15/29 [==============>...............] - ETA: 0s - loss: 1.4208   18/29 [=================>............] - ETA: 0s - loss: 1.4203   21/29 [====================>.........] - ETA: 0s - loss: 1.4198   25/29 [========================>.....] - ETA: 0s - loss: 1.4192   29/29 [==============================] - ETA: 0s - loss: 1.4187   29/29 [==============================] - 1s 22ms/step - loss: 1.4187 - val_loss: 1.4137   Epoch 7/10       1/29 [>.............................] - ETA: 0s - loss: 1.4140    5/29 [====>.........................] - ETA: 0s - loss: 1.4133    8/29 [=======>......................] - ETA: 0s - loss: 1.4129   12/29 [===========>..................] - ETA: 0s - loss: 1.4123   16/29 [===============>..............] - ETA: 0s - loss: 1.4118   21/29 [====================>.........] - ETA: 0s - loss: 1.4111   24/29 [=======================>......] - ETA: 0s - loss: 1.4107   28/29 [===========================>..] - ETA: 0s - loss: 1.4101   29/29 [==============================] - 1s 19ms/step - loss: 1.4101 - val_loss: 1.4059   Epoch 8/10       1/29 [>.............................] - ETA: 0s - loss: 1.4061    4/29 [===>..........................] - ETA: 0s - loss: 1.4056    8/29 [=======>......................] - ETA: 0s - loss: 1.4052   12/29 [===========>..................] - ETA: 0s - loss: 1.4048   15/29 [==============>...............] - ETA: 0s - loss: 1.4044   18/29 [=================>............] - ETA: 0s - loss: 1.4041   21/29 [====================>.........] - ETA: 0s - loss: 1.4037   23/29 [======================>.......] - ETA: 0s - loss: 1.4035   27/29 [==========================>...] - ETA: 0s - loss: 1.4031   29/29 [==============================] - 1s 20ms/step - loss: 1.4030 - val_loss: 1.3995   Epoch 9/10       1/29 [>.............................] - ETA: 0s - loss: 1.3999    5/29 [====>.........................] - ETA: 0s - loss: 1.3994   10/29 [=========>....................] - ETA: 0s - loss: 1.3989   14/29 [=============>................] - ETA: 0s - loss: 1.3985   17/29 [================>.............] - ETA: 0s - loss: 1.3983   20/29 [===================>..........] - ETA: 0s - loss: 1.3980   25/29 [========================>.....] - ETA: 0s - loss: 1.3976   28/29 [===========================>..] - ETA: 0s - loss: 1.3973   29/29 [==============================] - 1s 20ms/step - loss: 1.3973 - val_loss: 1.3948   Epoch 10/10       1/29 [>.............................] - ETA: 0s - loss: 1.3947    5/29 [====>.........................] - ETA: 0s - loss: 1.3946    9/29 [========>.....................] - ETA: 0s - loss: 1.3944   14/29 [=============>................] - ETA: 0s - loss: 1.3941   18/29 [=================>............] - ETA: 0s - loss: 1.3938   21/29 [====================>.........] - ETA: 0s - loss: 1.3936   26/29 [=========================>....] - ETA: 0s - loss: 1.3934   29/29 [==============================] - 0s 17ms/step - loss: 1.3933 - val_loss: 1.3916   Epoch 1/10       1/29 [>.............................] - ETA: 1:05 - loss: 1.2453    4/29 [===>..........................] - ETA: 0s - loss: 1.1612    7/29 [======>.......................] - ETA: 0s - loss: 1.1467   10/29 [=========>....................] - ETA: 0s - loss: 1.1705   13/29 [============>.................] - ETA: 0s - loss: 1.1763   16/29 [===============>..............] - ETA: 0s - loss: 1.1849   20/29 [===================>..........] - ETA: 0s - loss: 1.1881   24/29 [=======================>......] - ETA: 0s - loss: 1.1818   27/29 [==========================>...] - ETA: 0s - loss: 1.1817   29/29 [==============================] - 4s 49ms/step - loss: 1.1842 - val_loss: 2.1275   Epoch 2/10       1/29 [>.............................] - ETA: 0s - loss: 0.9681    6/29 [=====>........................] - ETA: 0s - loss: 1.1706   10/29 [=========>....................] - ETA: 0s - loss: 1.2102   14/29 [=============>................] - ETA: 0s - loss: 1.1598   18/29 [=================>............] - ETA: 0s - loss: 1.1774   21/29 [====================>.........] - ETA: 0s - loss: 1.1765   24/29 [=======================>......] - ETA: 0s - loss: 1.1779   28/29 [===========================>..] - ETA: 0s - loss: 1.1617   29/29 [==============================] - 1s 20ms/step - loss: 1.1645 - val_loss: 2.0574   Epoch 3/10       1/29 [>.............................] - ETA: 0s - loss: 1.0562    6/29 [=====>........................] - ETA: 0s - loss: 1.1321   10/29 [=========>....................] - ETA: 0s - loss: 1.1602   13/29 [============>.................] - ETA: 0s - loss: 1.1387   16/29 [===============>..............] - ETA: 0s - loss: 1.1374   19/29 [==================>...........] - ETA: 0s - loss: 1.1366   23/29 [======================>.......] - ETA: 0s - loss: 1.1351   28/29 [===========================>..] - ETA: 0s - loss: 1.1432   29/29 [==============================] - 1s 18ms/step - loss: 1.1441 - val_loss: 1.9952   Epoch 4/10       1/29 [>.............................] - ETA: 0s - loss: 1.0159    4/29 [===>..........................] - ETA: 0s - loss: 1.0840    7/29 [======>.......................] - ETA: 0s - loss: 1.1153   11/29 [==========>...................] - ETA: 0s - loss: 1.1059   14/29 [=============>................] - ETA: 0s - loss: 1.0867   18/29 [=================>............] - ETA: 0s - loss: 1.1009   22/29 [=====================>........] - ETA: 0s - loss: 1.1012   27/29 [==========================>...] - ETA: 0s - loss: 1.1185   29/29 [==============================] - 1s 19ms/step - loss: 1.1216 - val_loss: 1.9393   Epoch 5/10       1/29 [>.............................] - ETA: 0s - loss: 1.0572    6/29 [=====>........................] - ETA: 0s - loss: 1.0819   11/29 [==========>...................] - ETA: 0s - loss: 1.1126   15/29 [==============>...............] - ETA: 0s - loss: 1.1055   18/29 [=================>............] - ETA: 0s - loss: 1.1042   22/29 [=====================>........] - ETA: 0s - loss: 1.1126   26/29 [=========================>....] - ETA: 0s - loss: 1.1004   29/29 [==============================] - 0s 17ms/step - loss: 1.0955 - val_loss: 1.8846   Epoch 6/10       1/29 [>.............................] - ETA: 0s - loss: 0.9186    6/29 [=====>........................] - ETA: 0s - loss: 1.0189   10/29 [=========>....................] - ETA: 0s - loss: 1.0511   14/29 [=============>................] - ETA: 0s - loss: 1.0626   19/29 [==================>...........] - ETA: 0s - loss: 1.0632   24/29 [=======================>......] - ETA: 0s - loss: 1.0662   28/29 [===========================>..] - ETA: 0s - loss: 1.0668   29/29 [==============================] - 1s 18ms/step - loss: 1.0666 - val_loss: 1.8390   Epoch 7/10       1/29 [>.............................] - ETA: 0s - loss: 1.0423    5/29 [====>.........................] - ETA: 0s - loss: 1.0287   10/29 [=========>....................] - ETA: 0s - loss: 1.0637   14/29 [=============>................] - ETA: 0s - loss: 1.0709   20/29 [===================>..........] - ETA: 0s - loss: 1.0477   23/29 [======================>.......] - ETA: 0s - loss: 1.0429   26/29 [=========================>....] - ETA: 0s - loss: 1.0424   29/29 [==============================] - ETA: 0s - loss: 1.0385   29/29 [==============================] - 1s 19ms/step - loss: 1.0385 - val_loss: 1.7931   Epoch 8/10       1/29 [>.............................] - ETA: 0s - loss: 1.0082    6/29 [=====>........................] - ETA: 0s - loss: 1.0126   10/29 [=========>....................] - ETA: 0s - loss: 1.0007   14/29 [=============>................] - ETA: 0s - loss: 1.0074   18/29 [=================>............] - ETA: 0s - loss: 1.0070   22/29 [=====================>........] - ETA: 0s - loss: 1.0098   25/29 [========================>.....] - ETA: 0s - loss: 1.0129   29/29 [==============================] - 1s 19ms/step - loss: 1.0098 - val_loss: 1.7559   Epoch 9/10       1/29 [>.............................] - ETA: 0s - loss: 0.9752    5/29 [====>.........................] - ETA: 0s - loss: 1.0275   10/29 [=========>....................] - ETA: 0s - loss: 1.0247   15/29 [==============>...............] - ETA: 0s - loss: 1.0008   19/29 [==================>...........] - ETA: 0s - loss: 0.9963   22/29 [=====================>........] - ETA: 0s - loss: 0.9905   25/29 [========================>.....] - ETA: 0s - loss: 0.9855   29/29 [==============================] - ETA: 0s - loss: 0.9810   29/29 [==============================] - 1s 18ms/step - loss: 0.9810 - val_loss: 1.7130   Epoch 10/10       1/29 [>.............................] - ETA: 0s - loss: 0.8496    5/29 [====>.........................] - ETA: 0s - loss: 0.9156    9/29 [========>.....................] - ETA: 0s - loss: 0.9342   13/29 [============>.................] - ETA: 0s - loss: 0.9356   16/29 [===============>..............] - ETA: 0s - loss: 0.9381   20/29 [===================>..........] - ETA: 0s - loss: 0.9430   25/29 [========================>.....] - ETA: 0s - loss: 0.9500   28/29 [===========================>..] - ETA: 0s - loss: 0.9515   29/29 [==============================] - 1s 18ms/step - loss: 0.9513 - val_loss: 1.6782   2025-11-22 15:41:08.725657: E tensorflow/core/util/util.cc:131] oneDNN supports DT_INT64 only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.   Model: "model_43"   ________________________________________________________________________________    Layer (type) Output Shape Para Connected to Trainable                                            m #   ================================================================================    input_node_x1_x2_ [(None, 2)] 0 [] Y    n_trees_2_n_layer    s_3_tree_depth_5_    _1 (InputLayer)    input__Intercept_ [(None, 1)] 0 [] Y    _1 (InputLayer)    node_2 (NODE) (None, 3) 1754 ['input_node_x1_x2 Y                                                   _n_trees_2_n_layer                                                   s_3_tree_depth_5__                                                   1[0][0]']    1_1 (Dense) (None, 3) 3 ['input__Intercept Y                                                   __1[0][0]']    add_77 (Add) (None, 3) 0 ['node_2[0][0]', Y                                                    '1_1[0][0]']    distribution_lamb ((None, 3), 0 ['add_77[0][0]'] Y    da_43 (Distributi (None, 3))    onLambda)   ================================================================================   Total params: 1757 (6.86 KB)   Trainable params: 793 (3.10 KB)   Non-trainable params: 964 (3.77 KB)   ________________________________________________________________________________   Model formulas:   ---------------   loc :   ~node(x1, x2, n_trees = 2, n_layers = 3, tree_depth = 5)   <environment: 0x564b65b97cc0>   Fitting model with 1 orthogonalization(s) ... Fitting model with 2 orthogonalization(s) ... Fitting model with 3 orthogonalization(s) ... Fitting model with 4 orthogonalization(s) ... Fitting model with 5 orthogonalization(s) ... Saving _problems/test_reproducibility_torch-30.R   Saving _problems/test_subnetwork_init_torch-20.R   Fitting Fold 1 ...   Done in 2.045503 secs   Fitting Fold 2 ...   Done in 0.6135597 secs   Epoch 1/2      1/2 [==============>...............] - ETA: 0s - loss: 22.0463   2/2 [==============================] - 0s 11ms/step - loss: 22.4672   Epoch 2/2      1/2 [==============>...............] - ETA: 0s - loss: 21.8272   2/2 [==============================] - 0s 22ms/step - loss: 20.5671   Fitting Fold 1 ...   Done in 2.37188 secs   Fitting Fold 2 ...   Done in 0.4814572 secs   Epoch 1/2      1/2 [==============>...............] - ETA: 0s - loss: 22.0463   2/2 [==============================] - 0s 27ms/step - loss: 22.4672   Epoch 2/2      1/2 [==============>...............] - ETA: 0s - loss: 21.8272   2/2 [==============================] - 0s 13ms/step - loss: 20.5671   [ FAIL 14 | WARN 0 | SKIP 1 | PASS 680 ]      ══ Skipped tests (1) ═══════════════════════════════════════════════════════════   • empty test (1):      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test_customtraining_torch.R:6:3'): Use multiple optimizers torch ────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(50, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─torch::nn_linear(1, 50) at test_customtraining_torch.R:6:3     2. └─Module$new(...)     3. └─torch (local) initialize(...)     4. ├─torch::nn_parameter(torch_empty(out_features, in_features))     5. │ └─torch:::is_torch_tensor(x)     6. └─torch::torch_empty(out_features, in_features)     7. ├─base::do.call(.torch_empty, args)     8. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)     9. └─torch:::call_c_function(...)    10. └─torch:::do_call(f, args)    11. ├─base::do.call(fun, args)    12. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)   ── Error ('test_data_handler_torch.R:75:3'): properties of dataset torch ───────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(1L, 1L), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_data_handler_torch.R:75:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch (local) layer_module(...)    11. └─Module$new(...)    12. └─deepregression (local) initialize(...)    13. ├─torch::nn_parameter(torch_empty(out_features, in_features))    14. │ └─torch:::is_torch_tensor(x)    15. └─torch::torch_empty(out_features, in_features)    16. ├─base::do.call(.torch_empty, args)    17. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)    18. └─torch:::call_c_function(...)    19. └─torch:::do_call(f, args)    20. ├─base::do.call(fun, args)    21. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)   ── Error ('test_deepregression_torch.R:6:5'): Simple additive model ────────────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(2, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_deepregression_torch.R:21:5     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─torch::nn_sequential(...) at test_deepregression_torch.R:6:5     9. │ └─Module$new(...)    10. │ └─torch (local) initialize(...)    11. │ └─rlang::list2(...)    12. └─torch::nn_linear(in_features = i, out_features = 2, bias = F)    13. └─Module$new(...)    14. └─torch (local) initialize(...)    15. ├─torch::nn_parameter(torch_empty(out_features, in_features))    16. │ └─torch:::is_torch_tensor(x)    17. └─torch::torch_empty(out_features, in_features)    18. ├─base::do.call(.torch_empty, args)    19. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)    20. └─torch:::call_c_function(...)    21. └─torch:::do_call(f, args)    22. ├─base::do.call(fun, args)    23. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)   ── Error ('test_deepregression_torch.R:110:3'): Generalized additive model ─────   <std::runtime_error/C++Error/error/condition>   Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_deepregression_torch.R:110:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch::torch_tensor(P)    11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory)    12. └─methods$initialize(NULL, NULL, ...)    13. └─torch:::torch_tensor_cpp(...)   ── Error ('test_deepregression_torch.R:151:3'): Deep generalized additive model with LSS ──   <std::runtime_error/C++Error/error/condition>   Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_deepregression_torch.R:151:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch::torch_tensor(P)    11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory)    12. └─methods$initialize(NULL, NULL, ...)    13. └─torch:::torch_tensor_cpp(...)   ── Error ('test_deepregression_torch.R:181:3'): GAMs with shared weights ───────   <std::runtime_error/C++Error/error/condition>   Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_deepregression_torch.R:181:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. ├─base::do.call(...)     6. └─deepregression (local) `<fn>`(...)     7. └─torch::torch_tensor(P)     8. └─Tensor$new(data, dtype, device, requires_grad, pin_memory)     9. └─methods$initialize(NULL, NULL, ...)    10. └─torch:::torch_tensor_cpp(...)   ── Error ('test_deepregression_torch.R:220:3'): GAMs with fixed weights ────────   <std::runtime_error/C++Error/error/condition>   Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_deepregression_torch.R:220:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch::torch_tensor(P)    11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory)    12. └─methods$initialize(NULL, NULL, ...)    13. └─torch:::torch_tensor_cpp(...)   ── Error ('test_ensemble_torch.R:13:3'): deep ensemble ─────────────────────────   <std::runtime_error/C++Error/error/condition>   Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_ensemble_torch.R:13:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch::torch_tensor(P)    11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory)    12. └─methods$initialize(NULL, NULL, ...)    13. └─torch:::torch_tensor_cpp(...)   ── Error ('test_ensemble_torch.R:55:3'): reinitializing weights ────────────────   <std::runtime_error/C++Error/error/condition>   Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_ensemble_torch.R:55:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch::torch_tensor(P)    11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory)    12. └─methods$initialize(NULL, NULL, ...)    13. └─torch:::torch_tensor_cpp(...)   ── Error ('test_families_torch.R:76:7'): torch families can be fitted ──────────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(1L, 1L), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_families_torch.R:76:7     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch (local) layer_module(...)    11. └─Module$new(...)    12. └─deepregression (local) initialize(...)    13. ├─torch::nn_parameter(torch_empty(out_features, in_features))    14. │ └─torch:::is_torch_tensor(x)    15. └─torch::torch_empty(out_features, in_features)    16. ├─base::do.call(.torch_empty, args)    17. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)    18. └─torch:::call_c_function(...)    19. └─torch:::do_call(f, args)    20. ├─base::do.call(fun, args)    21. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)   ── Error ('test_layers_torch.R:6:3'): lasso layers ─────────────────────────────   <std::runtime_error/C++Error/error/condition>   Error in `cpp_torch_manual_seed(as.character(seed))`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:       ▆    1. └─torch::torch_manual_seed(42) at test_layers_torch.R:6:3    2. └─torch:::cpp_torch_manual_seed(as.character(seed))   ── Error ('test_methods_torch.R:18:3'): all methods ────────────────────────────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(1L, 1L), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_methods_torch.R:18:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─torch (local) layer_module(...)    11. └─Module$new(...)    12. └─deepregression (local) initialize(...)    13. ├─torch::nn_parameter(torch_empty(out_features, in_features))    14. │ └─torch:::is_torch_tensor(x)    15. └─torch::torch_empty(out_features, in_features)    16. ├─base::do.call(.torch_empty, args)    17. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)    18. └─torch:::call_c_function(...)    19. └─torch:::do_call(f, args)    20. ├─base::do.call(fun, args)    21. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)   ── Error ('test_reproducibility_torch.R:21:17'): reproducibility ───────────────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(64, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_reproducibility_torch.R:33:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(...)     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[i]]$layer()     8. ├─torch::nn_sequential(...) at test_reproducibility_torch.R:21:17     9. │ └─Module$new(...)    10. │ └─torch (local) initialize(...)    11. │ └─rlang::list2(...)    12. └─torch::nn_linear(in_features = 1, out_features = 64, bias = F)    13. └─Module$new(...)    14. └─torch (local) initialize(...)    15. ├─torch::nn_parameter(torch_empty(out_features, in_features))    16. │ └─torch:::is_torch_tensor(x)    17. └─torch::torch_empty(out_features, in_features)    18. ├─base::do.call(.torch_empty, args)    19. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)    20. └─torch:::call_c_function(...)    21. └─torch:::do_call(f, args)    22. ├─base::do.call(fun, args)    23. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)   ── Error ('test_subnetwork_init_torch.R:15:33'): subnetwork_init ───────────────   <std::runtime_error/C++Error/error/condition>   Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(5, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.   Backtrace:        ▆     1. └─deepregression::subnetwork_init_torch(list(pp)) at test_subnetwork_init_torch.R:38:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─pp_lay[[i]]$layer()     5. ├─torch::nn_sequential(...) at test_subnetwork_init_torch.R:15:33     6. │ └─Module$new(...)     7. │ └─torch (local) initialize(...)     8. │ └─rlang::list2(...)     9. └─torch::nn_linear(in_features = 1, out_features = 5)    10. └─Module$new(...)    11. └─torch (local) initialize(...)    12. ├─torch::nn_parameter(torch_empty(out_features, in_features))    13. │ └─torch:::is_torch_tensor(x)    14. └─torch::torch_empty(out_features, in_features)    15. ├─base::do.call(.torch_empty, args)    16. └─torch (local) `<fn>`(options = `<named list>`, size = `<list>`)    17. └─torch:::call_c_function(...)    18. └─torch:::do_call(f, args)    19. ├─base::do.call(fun, args)    20. └─torch (local) `<fn>`(size = `<list>`, options = `<named list>`, memory_format = NULL)      [ FAIL 14 | WARN 0 | SKIP 1 | PASS 680 ]   Error:   ! Test failures.   Execution halted
• checking PDF version of manual ... [7s/9s] OK
• checking HTML version of manual ... [4s/6s] OK
• checking for non-standard things in the check directory ... OK
• DONE

Status: 1 ERROR