• using R Under development (unstable) (2025-08-23 r88688)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-15 (Debian 15.2.0-1) 15.2.0     GNU Fortran (Debian 15.2.0-1) 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 ‘2.2.0’
• 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 ... [4s/5s] OK
• checking whether the package can be loaded with stated dependencies ... [4s/6s] OK
• checking whether the package can be unloaded cleanly ... [4s/6s] OK
• checking whether the namespace can be loaded with stated dependencies ... [4s/5s] OK
• checking whether the namespace can be unloaded cleanly ... [4s/6s] OK
• checking loading without being on the library search path ... [4s/6s] OK
• checking whether startup messages can be suppressed ... [4s/5s] 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 ... [28s/33s] 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 ... [4s/4s] OK
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [4m/16m] ERROR   Running ‘testthat.R’ [4m/16m] 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 tensorboard (5.2MiB)   Downloading ml-dtypes (4.7MiB)   Downloading keras (1.3MiB)   Downloading tf-keras (1.6MiB)   Downloading tensorflow-probability (6.7MiB)   Downloading tensorflow (615.0MiB)    Downloading ml-dtypes    Downloading pygments    Downloading tensorboard    Downloading keras    Downloading tf-keras    Downloading tensorflow-probability    Downloading tensorflow   Installed 44 packages in 456ms   2025-08-24 15:36:32.120023: 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`.   2025-08-24 15:36:32.121971: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.   2025-08-24 15:36:32.127376: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.   2025-08-24 15:36:32.141508: 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 00:00:1756042592.165312 3405165 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered   E0000 00:00:1756042592.172790 3405165 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered   W0000 00:00:1756042592.192439 3405165 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   W0000 00:00:1756042592.192493 3405165 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   W0000 00:00:1756042592.192498 3405165 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   W0000 00:00:1756042592.192503 3405165 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.   2025-08-24 15:36:32.197854: 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.   Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz          8192/11490434 [..............................] - ETA: 0s      16384/11490434 [..............................] - ETA: 52s      49152/11490434 [..............................] - ETA: 41s      81920/11490434 [..............................] - ETA: 39s     131072/11490434 [..............................] - ETA: 28s     180224/11490434 [..............................] - ETA: 24s     245760/11490434 [..............................] - ETA: 20s     368640/11490434 [..............................] - ETA: 14s     491520/11490434 [>.............................] - ETA: 12s     737280/11490434 [>.............................] - ETA: 8s    1048576/11490434 [=>............................] - ETA: 6s    1523712/11490434 [==>...........................] - ETA: 4s    2220032/11490434 [====>.........................] - ETA: 3s    3448832/11490434 [========>.....................] - ETA: 1s    5046272/11490434 [============>.................] - ETA: 1s    7438336/11490434 [==================>...........] - ETA: 0s    9764864/11490434 [========================>.....] - ETA: 0s   11490434/11490434 [==============================] - 1s 0us/step   2025-08-24 15:36:52.176762: 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: 26s - loss: 2.3051    9/15 [=================>............] - ETA: 0s - loss: 2.2279   15/15 [==============================] - 2s 32ms/step - loss: 2.2047 - val_loss: 2.1376   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.1137    7/15 [=============>................] - ETA: 0s - loss: 2.0777   15/15 [==============================] - 0s 18ms/step - loss: 2.0593 - val_loss: 1.9954   Epoch 1/2       1/15 [=>............................] - ETA: 14s - loss: 2.6629   10/15 [===================>..........] - ETA: 0s - loss: 2.6533   15/15 [==============================] - 1s 30ms/step - loss: 2.6533 - val_loss: 2.6393   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.6358   12/15 [=======================>......] - ETA: 0s - loss: 2.6346   15/15 [==============================] - 0s 12ms/step - loss: 2.6365 - val_loss: 2.6227   Epoch 1/2       1/15 [=>............................] - ETA: 12s - loss: 4.1701   11/15 [=====================>........] - ETA: 0s - loss: 4.1792   15/15 [==============================] - 1s 23ms/step - loss: 4.0062 - val_loss: 3.4240   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 4.3026   12/15 [=======================>......] - ETA: 0s - loss: 3.9271   15/15 [==============================] - 0s 11ms/step - loss: 3.9103 - val_loss: 3.3606   Epoch 1/3      1/3 [=========>....................] - ETA: 2s - loss: 7.9864   3/3 [==============================] - 1s 137ms/step - loss: 8.7055 - val_loss: 7.2396   Epoch 2/3      1/3 [=========>....................] - ETA: 0s - loss: 9.5053   3/3 [==============================] - 0s 48ms/step - loss: 8.6326 - val_loss: 7.1833   Epoch 3/3      1/3 [=========>....................] - ETA: 0s - loss: 8.5150   3/3 [==============================] - 0s 32ms/step - loss: 8.5566 - val_loss: 7.1282   Epoch 1/2       1/15 [=>............................] - ETA: 9s - loss: 653.6502   11/15 [=====================>........] - ETA: 0s - loss: 1183.8137   15/15 [==============================] - 1s 34ms/step - loss: 1248.0040 - val_loss: 1370.1050   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 755.2901   14/15 [===========================>..] - ETA: 0s - loss: 964.7537   15/15 [==============================] - 0s 9ms/step - loss: 960.7450 - val_loss: 1055.7158   Epoch 1/2       1/15 [=>............................] - ETA: 11s - loss: 927.8141   12/15 [=======================>......] - ETA: 0s - loss: 2380.3286   15/15 [==============================] - 1s 27ms/step - loss: 3026.8003 - val_loss: 321.5525   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 991.2161   11/15 [=====================>........] - ETA: 0s - loss: 3033.7759   15/15 [==============================] - 0s 11ms/step - loss: 2679.4148 - val_loss: 292.7626   Epoch 1/2       1/15 [=>............................] - ETA: 14s - loss: 2.3149    9/15 [=================>............] - ETA: 0s - loss: 2.2388   15/15 [==============================] - 1s 29ms/step - loss: 2.2516 - val_loss: 2.2750   Epoch 2/2       1/15 [=>............................] - ETA: 0s - loss: 2.1948   10/15 [===================>..........] - ETA: 0s - loss: 2.2188   15/15 [==============================] - 0s 13ms/step - loss: 2.2132 - val_loss: 2.2328   Fitting member 1 ...Epoch 1/10       1/32 [..............................] - ETA: 37s - loss: 2.3293   14/32 [============>.................] - ETA: 0s - loss: 2.3423   27/32 [========================>.....] - ETA: 0s - loss: 2.3338   32/32 [==============================] - 1s 4ms/step - loss: 2.3303   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 2.3269   13/32 [===========>..................] - ETA: 0s - loss: 2.3187   25/32 [======================>.......] - ETA: 0s - loss: 2.3073   32/32 [==============================] - 0s 4ms/step - loss: 2.2977   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 2.3270   11/32 [=========>....................] - ETA: 0s - loss: 2.2778   21/32 [==================>...........] - ETA: 0s - loss: 2.2748   32/32 [==============================] - ETA: 0s - loss: 2.2650   32/32 [==============================] - 0s 5ms/step - loss: 2.2650   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 2.2270   12/32 [==========>...................] - ETA: 0s - loss: 2.2328   24/32 [=====================>........] - ETA: 0s - loss: 2.2332   32/32 [==============================] - 0s 4ms/step - loss: 2.2325   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 2.2185   17/32 [==============>...............] - ETA: 0s - loss: 2.2090   31/32 [============================>.] - ETA: 0s - loss: 2.2011   32/32 [==============================] - 0s 4ms/step - loss: 2.2002   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 2.1431   12/32 [==========>...................] - ETA: 0s - loss: 2.1670   24/32 [=====================>........] - ETA: 0s - loss: 2.1740   32/32 [==============================] - 0s 5ms/step - loss: 2.1681   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 2.2840   13/32 [===========>..................] - ETA: 0s - loss: 2.1583   25/32 [======================>.......] - ETA: 0s - loss: 2.1366   32/32 [==============================] - 0s 4ms/step - loss: 2.1359   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 2.0769   15/32 [=============>................] - ETA: 0s - loss: 2.1076   26/32 [=======================>......] - ETA: 0s - loss: 2.1102   32/32 [==============================] - 0s 4ms/step - loss: 2.1036   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 2.0602   10/32 [========>.....................] - ETA: 0s - loss: 2.1068   26/32 [=======================>......] - ETA: 0s - loss: 2.0764   32/32 [==============================] - 0s 5ms/step - loss: 2.0717   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 2.0980   19/32 [================>.............] - ETA: 0s - loss: 2.0377   30/32 [===========================>..] - ETA: 0s - loss: 2.0396   32/32 [==============================] - 0s 4ms/step - loss: 2.0400      Done in 3.241244 secs   Fitting member 2 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 2.1814   12/32 [==========>...................] - ETA: 0s - loss: 2.3720   24/32 [=====================>........] - ETA: 0s - loss: 2.3417   32/32 [==============================] - 0s 4ms/step - loss: 2.3312   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 2.4545   15/32 [=============>................] - ETA: 0s - loss: 2.3411   28/32 [=========================>....] - ETA: 0s - loss: 2.2945   32/32 [==============================] - 0s 4ms/step - loss: 2.2785   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 2.5406   13/32 [===========>..................] - ETA: 0s - loss: 2.2695   30/32 [===========================>..] - ETA: 0s - loss: 2.2240   32/32 [==============================] - 0s 4ms/step - loss: 2.2334   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 2.0854   16/32 [==============>...............] - ETA: 0s - loss: 2.1746   32/32 [==============================] - 0s 3ms/step - loss: 2.1937   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 2.2393   17/32 [==============>...............] - ETA: 0s - loss: 2.1791   29/32 [==========================>...] - ETA: 0s - loss: 2.1651   32/32 [==============================] - 0s 4ms/step - loss: 2.1597   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 2.0225   18/32 [===============>..............] - ETA: 0s - loss: 2.1451   32/32 [==============================] - 0s 3ms/step - loss: 2.1276   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 2.4568   16/32 [==============>...............] - ETA: 0s - loss: 2.1088   31/32 [============================>.] - ETA: 0s - loss: 2.0940   32/32 [==============================] - 0s 4ms/step - loss: 2.0961   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 1.9680   15/32 [=============>................] - ETA: 0s - loss: 2.0623   26/32 [=======================>......] - ETA: 0s - loss: 2.0754   32/32 [==============================] - 0s 4ms/step - loss: 2.0644   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 2.0730   15/32 [=============>................] - ETA: 0s - loss: 2.0602   32/32 [==============================] - 0s 3ms/step - loss: 2.0336   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 2.1117   12/32 [==========>...................] - ETA: 0s - loss: 2.0073   26/32 [=======================>......] - ETA: 0s - loss: 1.9918   32/32 [==============================] - 0s 4ms/step - loss: 2.0026      Done in 1.385268 secs   Fitting member 3 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 41.9180   12/32 [==========>...................] - ETA: 0s - loss: 47.0636   23/32 [====================>.........] - ETA: 0s - loss: 40.6723   32/32 [==============================] - 0s 5ms/step - loss: 39.2828   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 34.1315   17/32 [==============>...............] - ETA: 0s - loss: 28.3797   26/32 [=======================>......] - ETA: 0s - loss: 27.7863   32/32 [==============================] - 0s 4ms/step - loss: 27.2884   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 27.6960   17/32 [==============>...............] - ETA: 0s - loss: 24.6021   29/32 [==========================>...] - ETA: 0s - loss: 21.6970   32/32 [==============================] - 0s 4ms/step - loss: 21.7021   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 13.7574   14/32 [============>.................] - ETA: 0s - loss: 19.0813   27/32 [========================>.....] - ETA: 0s - loss: 18.3997   32/32 [==============================] - 0s 5ms/step - loss: 18.2036   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 15.2942   15/32 [=============>................] - ETA: 0s - loss: 16.4400   27/32 [========================>.....] - ETA: 0s - loss: 16.0637   32/32 [==============================] - 0s 4ms/step - loss: 15.8282   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 9.8128   10/32 [========>.....................] - ETA: 0s - loss: 13.5695   23/32 [====================>.........] - ETA: 0s - loss: 13.7845   32/32 [==============================] - 0s 5ms/step - loss: 14.0666   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 14.0067   19/32 [================>.............] - ETA: 0s - loss: 12.7763   32/32 [==============================] - ETA: 0s - loss: 12.6950   32/32 [==============================] - 0s 3ms/step - loss: 12.6950   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 12.2746   11/32 [=========>....................] - ETA: 0s - loss: 11.9713   26/32 [=======================>......] - ETA: 0s - loss: 11.4475   32/32 [==============================] - 0s 5ms/step - loss: 11.5684   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 13.3544   16/32 [==============>...............] - ETA: 0s - loss: 10.6689   30/32 [===========================>..] - ETA: 0s - loss: 10.7419   32/32 [==============================] - 0s 3ms/step - loss: 10.6440   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 8.5922   17/32 [==============>...............] - ETA: 0s - loss: 9.3869   32/32 [==============================] - ETA: 0s - loss: 9.8495   32/32 [==============================] - 0s 3ms/step - loss: 9.8495      Done in 1.493408 secs   Fitting member 4 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 2.8816   12/32 [==========>...................] - ETA: 0s - loss: 3.2172   27/32 [========================>.....] - ETA: 0s - loss: 2.9532   32/32 [==============================] - 0s 4ms/step - loss: 2.9588   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 2.3825   19/32 [================>.............] - ETA: 0s - loss: 2.8742   32/32 [==============================] - 0s 3ms/step - loss: 2.9011   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 3.9315   14/32 [============>.................] - ETA: 0s - loss: 2.9395   23/32 [====================>.........] - ETA: 0s - loss: 2.9088   32/32 [==============================] - 0s 5ms/step - loss: 2.8534   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 2.3602   12/32 [==========>...................] - ETA: 0s - loss: 2.6534   26/32 [=======================>......] - ETA: 0s - loss: 2.8024   32/32 [==============================] - 0s 4ms/step - loss: 2.8062   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 2.7419   13/32 [===========>..................] - ETA: 0s - loss: 2.7061   23/32 [====================>.........] - ETA: 0s - loss: 2.7823   32/32 [==============================] - 0s 5ms/step - loss: 2.7623   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 1.7251   14/32 [============>.................] - ETA: 0s - loss: 2.5452   32/32 [==============================] - ETA: 0s - loss: 2.7193   32/32 [==============================] - 0s 4ms/step - loss: 2.7193   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 2.8260   12/32 [==========>...................] - ETA: 0s - loss: 2.7719   24/32 [=====================>........] - ETA: 0s - loss: 2.6041   32/32 [==============================] - 0s 4ms/step - loss: 2.6774   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 2.2710   16/32 [==============>...............] - ETA: 0s - loss: 2.6983   30/32 [===========================>..] - ETA: 0s - loss: 2.6296   32/32 [==============================] - 0s 4ms/step - loss: 2.6383   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 3.0022   14/32 [============>.................] - ETA: 0s - loss: 2.7629   24/32 [=====================>........] - ETA: 0s - loss: 2.6937   32/32 [==============================] - 0s 4ms/step - loss: 2.6007   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 2.6980   19/32 [================>.............] - ETA: 0s - loss: 2.3980   28/32 [=========================>....] - ETA: 0s - loss: 2.5031   32/32 [==============================] - 0s 4ms/step - loss: 2.5630      Done in 1.511275 secs   Fitting member 5 ...Epoch 1/10       1/32 [..............................] - ETA: 0s - loss: 112.3703   14/32 [============>.................] - ETA: 0s - loss: 160.9385   27/32 [========================>.....] - ETA: 0s - loss: 141.0971   32/32 [==============================] - 0s 4ms/step - loss: 139.0890   Epoch 2/10       1/32 [..............................] - ETA: 0s - loss: 141.4559   17/32 [==============>...............] - ETA: 0s - loss: 101.6428   30/32 [===========================>..] - ETA: 0s - loss: 96.1611   32/32 [==============================] - 0s 4ms/step - loss: 95.6168   Epoch 3/10       1/32 [..............................] - ETA: 0s - loss: 97.2787   18/32 [===============>..............] - ETA: 0s - loss: 83.6157   31/32 [============================>.] - ETA: 0s - loss: 73.9531   32/32 [==============================] - 0s 3ms/step - loss: 74.6476   Epoch 4/10       1/32 [..............................] - ETA: 0s - loss: 42.6719   14/32 [============>.................] - ETA: 0s - loss: 64.5960   24/32 [=====================>........] - ETA: 0s - loss: 63.6280   32/32 [==============================] - 0s 5ms/step - loss: 61.8040   Epoch 5/10       1/32 [..............................] - ETA: 0s - loss: 51.5790   15/32 [=============>................] - ETA: 0s - loss: 54.4421   30/32 [===========================>..] - ETA: 0s - loss: 53.6450   32/32 [==============================] - 0s 4ms/step - loss: 53.1899   Epoch 6/10       1/32 [..............................] - ETA: 0s - loss: 35.5863   17/32 [==============>...............] - ETA: 0s - loss: 46.4085   32/32 [==============================] - 0s 3ms/step - loss: 46.9254   Epoch 7/10       1/32 [..............................] - ETA: 0s - loss: 43.7696   12/32 [==========>...................] - ETA: 0s - loss: 44.6775   25/32 [======================>.......] - ETA: 0s - loss: 43.9927   32/32 [==============================] - 0s 4ms/step - loss: 42.0744   Epoch 8/10       1/32 [..............................] - ETA: 0s - loss: 43.0723   12/32 [==========>...................] - ETA: 0s - loss: 39.9295   29/32 [==========================>...] - ETA: 0s - loss: 37.9504   32/32 [==============================] - 0s 4ms/step - loss: 38.1152   Epoch 9/10       1/32 [..............................] - ETA: 0s - loss: 45.3285   13/32 [===========>..................] - ETA: 0s - loss: 34.6790   31/32 [============================>.] - ETA: 0s - loss: 34.9863   32/32 [==============================] - 0s 4ms/step - loss: 34.8808   Epoch 10/10       1/32 [..............................] - ETA: 0s - loss: 29.3974   12/32 [==========>...................] - ETA: 0s - loss: 30.0236   22/32 [===================>..........] - ETA: 0s - loss: 30.7542   31/32 [============================>.] - ETA: 0s - loss: 32.0094   32/32 [==============================] - 0s 5ms/step - loss: 32.1088      Done in 1.471473 secs   Epoch 1/2      1/3 [=========>....................] - ETA: 1s - loss: 2.3341   3/3 [==============================] - 1s 172ms/step - loss: 2.3038 - val_loss: 2.2154   Epoch 2/2      1/3 [=========>....................] - ETA: 0s - loss: 2.3662   3/3 [==============================] - 0s 68ms/step - loss: 2.3004 - val_loss: 2.2128   Epoch 1/2      1/3 [=========>....................] - ETA: 0s - loss: 52.8941   3/3 [==============================] - 0s 104ms/step - loss: 47.0024 - val_loss: 27.1291   Epoch 2/2      1/3 [=========>....................] - ETA: 0s - loss: 49.6579   3/3 [==============================] - 0s 50ms/step - loss: 46.6172 - val_loss: 26.8568   Fitting member 1 ...   Done in 5.897056 secs   Fitting member 2 ...   Done in 5.863565 secs   Fitting member 3 ...   Done in 6.058224 secs   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 0x7f5df1dc1940> 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 0x7f5df19796c0> 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   Fitting normal   Fitting bernoulli   Fitting bernoulli_prob   Fitting gamma   Fitting poisson   Epoch 1/2       1/29 [>.............................] - ETA: 29s - loss: 11.2004   11/29 [==========>...................] - ETA: 0s - loss: 10.4834   22/29 [=====================>........] - ETA: 0s - loss: 10.6107   29/29 [==============================] - 1s 16ms/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   22/29 [=====================>........] - ETA: 0s - loss: 9.5857   29/29 [==============================] - 0s 8ms/step - loss: 9.5296 - val_loss: 6.8647   Fitting model with 1 orthogonalization(s) ... Fitting Fold 1 ...   Done in 2.553033 secs   Fitting Fold 2 ...   Done in 0.493022 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 22ms/step - loss: 20.5671   Fitting Fold 1 ...   Done in 1.820647 secs   Fitting Fold 2 ...   Done in 0.5037026 secs   Epoch 1/2      1/2 [==============>...............] - ETA: 0s - loss: 22.0463   2/2 [==============================] - 0s 9ms/step - loss: 22.4672   Epoch 2/2      1/2 [==============>...............] - ETA: 0s - loss: 21.8272   2/2 [==============================] - 0s 16ms/step - loss: 20.5671   [ FAIL 13 | WARN 0 | SKIP 0 | PASS 681 ]      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test_deepregression.R:109:3'): Generalized additive model ───────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─deepregression::deepregression(...) at test_deepregression.R:109:3     2. │ └─base::lapply(...)     3. │ └─deepregression (local) FUN(X[[i]], ...)     4. │ └─subnetwork_builder[[i]](...)     5. │ ├─deep_splitted[[2]](orthog_fun(deep, ox))     6. │ │ └─x %>% layer_dense(units = 1L, activation = "linear")     7. │ └─deepregression (local) orthog_fun(deep, ox)     8. │ └─deepregression:::orthog_tf(Y, X, deactivate_oz_at_test)     9. │ └─reticulate::import_from_path("layers", path = python_path)    10. │ └─reticulate:::import_from_path_immediate(module, path, convert)    11. │ └─reticulate::import(module, convert = convert)    12. │ └─reticulate:::py_module_import(module, convert = convert)    13. └─keras::layer_dense(., units = 1L, activation = "linear")    14. └─keras::create_layer(...)   ── Error ('test_deepregression.R:193:3'): Generalized additive model with RWT in formula ──   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─deepregression::deepregression(...) at test_deepregression.R:193:3     2. │ └─base::lapply(...)     3. │ └─deepregression (local) FUN(X[[i]], ...)     4. │ └─subnetwork_builder[[i]](...)     5. │ ├─deep_splitted[[2]](orthog_fun(deep, ox))     6. │ │ └─x %>% layer_dense(units = 1L, activation = "linear")     7. │ └─deepregression (local) orthog_fun(deep, ox)     8. │ └─deepregression:::orthog_tf(Y, X, deactivate_oz_at_test)     9. │ └─reticulate::import_from_path("layers", path = python_path)    10. │ └─reticulate:::import_from_path_immediate(module, path, convert)    11. │ └─reticulate::import(module, convert = convert)    12. │ └─reticulate:::py_module_import(module, convert = convert)    13. └─keras::layer_dense(., units = 1L, activation = "linear")    14. └─keras::create_layer(...)   ── Error ('test_layers.R:14:3'): custom layers ─────────────────────────────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:       ▆    1. └─deepregression::layer_hadamard_diff(...) at test_layers.R:14:3    2. └─reticulate::import_from_path("layers", path = python_path)    3. └─reticulate:::import_from_path_immediate(module, path, convert)    4. └─reticulate::import(module, convert = convert)    5. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_layers.R:48:3'): lasso layers ──────────────────────────────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:       ▆    1. └─deepregression::tib_layer(units = 1L, la = 1) at test_layers.R:48:3    2. └─reticulate::import_from_path("layers", path = python_path)    3. └─reticulate:::import_from_path_immediate(module, path, convert)    4. └─reticulate::import(module, convert = convert)    5. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_node.R:26:3'): node regression ─────────────────────────────────   <python.builtin.ModuleNotFoundError/python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ModuleNotFoundError: No module named 'keras.src.engine'   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_node.R:26:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(1:length(pp_in), function(i) pp_lay[[layer_matching[i]]]$layer(inputs[[i]]))     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[layer_matching[i]]]$layer(inputs[[i]])     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─reticulate::import_from_path("node", path = python_path)    11. └─reticulate:::import_from_path_immediate(module, path, convert)    12. └─reticulate::import(module, convert = convert)    13. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_node.R:104:3'): node bernoulli ─────────────────────────────────   <python.builtin.ModuleNotFoundError/python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ModuleNotFoundError: No module named 'keras.src.engine'   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_node.R:104:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(1:length(pp_in), function(i) pp_lay[[layer_matching[i]]]$layer(inputs[[i]]))     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[layer_matching[i]]]$layer(inputs[[i]])     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─reticulate::import_from_path("node", path = python_path)    11. └─reticulate:::import_from_path_immediate(module, path, convert)    12. └─reticulate::import(module, convert = convert)    13. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_node.R:190:3'): node multinoulli ───────────────────────────────   <python.builtin.ModuleNotFoundError/python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ModuleNotFoundError: No module named 'keras.src.engine'   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. └─deepregression::deepregression(...) at test_node.R:190:3     2. └─base::lapply(...)     3. └─deepregression (local) FUN(X[[i]], ...)     4. └─subnetwork_builder[[i]](...)     5. └─base::lapply(1:length(pp_in), function(i) pp_lay[[layer_matching[i]]]$layer(inputs[[i]]))     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[layer_matching[i]]]$layer(inputs[[i]])     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(...)    10. └─reticulate::import_from_path("node", path = python_path)    11. └─reticulate:::import_from_path_immediate(module, path, convert)    12. └─reticulate::import(module, convert = convert)    13. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_node.R:276:3'): node overlap ───────────────────────────────────   <python.builtin.ModuleNotFoundError/python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ModuleNotFoundError: No module named 'keras.src.engine'   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─testthat::expect_warning(...) at test_node.R:276:3     2. │ └─testthat:::quasi_capture(...)     3. │ ├─testthat (local) .capture(...)     4. │ │ └─base::withCallingHandlers(...)     5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))     6. └─deepregression::deepregression(...)     7. └─base::lapply(...)     8. └─deepregression (local) FUN(X[[i]], ...)     9. └─subnetwork_builder[[i]](...)    10. └─base::lapply(1:length(pp_in), function(i) pp_lay[[layer_matching[i]]]$layer(inputs[[i]]))    11. └─deepregression (local) FUN(X[[i]], ...)    12. └─pp_lay[[layer_matching[i]]]$layer(inputs[[i]])    13. ├─base::do.call(layer_class, layer_args)    14. └─deepregression (local) `<fn>`(...)    15. └─reticulate::import_from_path("node", path = python_path)    16. └─reticulate:::import_from_path_immediate(module, path, convert)    17. └─reticulate::import(module, convert = convert)    18. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_orthogonalization.R:44:5'): orthogonalization ──────────────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─base::suppressWarnings(...) at test_orthogonalization.R:44:5     2. │ └─base::withCallingHandlers(...)     3. ├─deepregression::deepregression(...)     4. │ └─base::lapply(...)     5. │ └─deepregression (local) FUN(X[[i]], ...)     6. │ └─subnetwork_builder[[i]](...)     7. │ ├─deep_splitted[[2]](orthog_fun(deep, ox))     8. │ │ └─x %>% layer_dense(units = 1, activation = "linear")     9. │ └─deepregression (local) orthog_fun(deep, ox)    10. │ └─deepregression:::orthog_tf(Y, X, deactivate_oz_at_test)    11. │ └─reticulate::import_from_path("layers", path = python_path)    12. │ └─reticulate:::import_from_path_immediate(module, path, convert)    13. │ └─reticulate::import(module, convert = convert)    14. │ └─reticulate:::py_module_import(module, convert = convert)    15. └─keras::layer_dense(., units = 1, activation = "linear")    16. └─keras::create_layer(...)   ── Error ('test_orthogonalization.R:106:5'): custom orthogonalization ──────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─base::suppressWarnings(...) at test_orthogonalization.R:106:5     2. │ └─base::withCallingHandlers(...)     3. ├─deepregression::deepregression(...)     4. │ └─base::lapply(...)     5. │ └─deepregression (local) FUN(X[[i]], ...)     6. │ └─subnetwork_builder[[i]](...)     7. │ ├─deep_splitted[[2]](orthog_fun(deep, ox))     8. │ │ └─x %>% layer_dense(units = 1, activation = "linear")     9. │ └─deepregression (local) orthog_fun(deep, ox)    10. │ └─deepregression:::orthog_tf(Y, X, deactivate_oz_at_test)    11. │ └─reticulate::import_from_path("layers", path = python_path)    12. │ └─reticulate:::import_from_path_immediate(module, path, convert)    13. │ └─reticulate::import(module, convert = convert)    14. │ └─reticulate:::py_module_import(module, convert = convert)    15. └─keras::layer_dense(., units = 1, activation = "linear")    16. └─keras::create_layer(...)   ── Error ('test_orthogonalization.R:163:3'): orthogonalization at test time ────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─deepregression::deepregression(...) at test_orthogonalization.R:163:3     2. │ └─base::lapply(...)     3. │ └─deepregression (local) FUN(X[[i]], ...)     4. │ └─subnetwork_builder[[i]](...)     5. │ ├─deep_splitted[[2]](orthog_fun(deep, ox))     6. │ │ └─x %>% layer_dense(units = 1, activation = "linear")     7. │ └─deepregression (local) orthog_fun(deep, ox)     8. │ └─deepregression:::orthog_tf(Y, X, deactivate_oz_at_test)     9. │ └─reticulate::import_from_path("layers", path = python_path)    10. │ └─reticulate:::import_from_path_immediate(module, path, convert)    11. │ └─reticulate::import(module, convert = convert)    12. │ └─reticulate:::py_module_import(module, convert = convert)    13. └─keras::layer_dense(., units = 1, activation = "linear")    14. └─keras::create_layer(...)   ── Error ('test_subnetwork_init.R:35:3'): subnetwork_init ──────────────────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─base::suppressWarnings(subnetwork_init(list(pp), gaminputs = gaminputs)) at test_subnetwork_init.R:35:3     2. │ └─base::withCallingHandlers(...)     3. └─deepregression::subnetwork_init(list(pp), gaminputs = gaminputs)     4. ├─deepregression::layer_add_identity(...)     5. └─base::lapply((1:length(pp_in))[outputs_wo_oz], function(i) pp_lay[[layer_matching[i]]]$layer(inputs[[i]]))     6. └─deepregression (local) FUN(X[[i]], ...)     7. └─pp_lay[[layer_matching[i]]]$layer(inputs[[i]])     8. ├─base::do.call(layer_class, layer_args)     9. └─deepregression (local) `<fn>`(name = "lasso_z__1", units = 1L, la = 0.001)    10. └─reticulate::import_from_path("layers", path = python_path)    11. └─reticulate:::import_from_path_immediate(module, path, convert)    12. └─reticulate::import(module, convert = convert)    13. └─reticulate:::py_module_import(module, convert = convert)   ── Error ('test_subnetwork_init.R:77:3'): shared layer within formula ──────────   <python.builtin.ImportError/python.builtin.Exception/python.builtin.BaseException/python.builtin.object/error/condition>   Error in `py_module_import(module, convert = convert)`: ImportError: cannot import name 'Conv' from 'keras.src.layers.convolutional.base_conv' (/tmp/check-CRAN-regular-hornik/cache/R/reticulate/uv/cache/archive-v0/t8T8CEPGnFbZY0y47sPm_/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py)   Run `reticulate::py_last_error()` for details.   Backtrace:        ▆     1. ├─base::suppressWarnings(subnetwork_init(list(pp), gaminputs = gaminputs)) at test_subnetwork_init.R:77:3     2. │ └─base::withCallingHandlers(...)     3. └─deepregression::subnetwork_init(list(pp), gaminputs = gaminputs)     4. └─base::lapply(1:length(pp_in), function(i) pp_lay[[layer_matching[i]]]$layer(inputs[[i]]))     5. └─deepregression (local) FUN(X[[i]], ...)     6. └─pp_lay[[layer_matching[i]]]$layer(inputs[[i]])     7. ├─base::do.call(layer_class, layer_args)     8. └─deepregression (local) `<fn>`(name = "lasso_z__1", units = 1L, la = 0.001)     9. └─reticulate::import_from_path("layers", path = python_path)    10. └─reticulate:::import_from_path_immediate(module, path, convert)    11. └─reticulate::import(module, convert = convert)    12. └─reticulate:::py_module_import(module, convert = convert)      [ FAIL 13 | WARN 0 | SKIP 0 | PASS 681 ]   Error: Test failures   Execution halted
• checking PDF version of manual ... [7s/9s] OK
• checking HTML version of manual ... [2s/3s] OK
• checking for non-standard things in the check directory ... OK
• DONE

Status: 1 ERROR