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### Running command:
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###   /home/biocbuild/bbs-3.14-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data VAExprs
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* checking for file ‘VAExprs/DESCRIPTION’ ... OK
* preparing ‘VAExprs’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘VAExprs.Rmd’ using rmarkdown
2022-04-12 06:11:09.446169: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/biocbuild/bbs-3.14-bioc/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/java-11-openjdk-amd64/lib/server:/home/biocbuild/bbs-3.14-bioc/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/java-11-openjdk-amd64/lib/server:/home/biocbuild/bbs-3.14-bioc/R/lib:/usr/local/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/java-11-openjdk-amd64/lib/server
2022-04-12 06:11:09.446213: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.
2022-04-12 06:11:21.643491: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-04-12 06:11:21.644882: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2022-04-12 06:11:21.703932: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2022-04-12 06:11:21.703982: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (nebbiolo2): /proc/driver/nvidia/version does not exist
2022-04-12 06:11:21.704523: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-04-12 06:11:21.707923: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-04-12 06:11:21.738084: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2022-04-12 06:11:21.744073: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2200000000 Hz
Train on 300 samples, validate on 300 samples
Epoch 1/100

 32/300 [==>...........................] - ETA: 2s - loss: 695.0123
300/300 [==============================] - ETA: 0s - loss: 660.6599
300/300 [==============================] - 0s 2ms/sample - loss: 660.6599 - val_loss: 615.1421
Epoch 2/100

 32/300 [==>...........................] - ETA: 0s - loss: 617.0280
300/300 [==============================] - ETA: 0s - loss: 608.5843
300/300 [==============================] - 0s 376us/sample - loss: 608.5843 - val_loss: 602.7748
Epoch 3/100

 32/300 [==>...........................] - ETA: 0s - loss: 599.4803
300/300 [==============================] - 0s 261us/sample - loss: 601.5733 - val_loss: 601.1372
Epoch 4/100

 32/300 [==>...........................] - ETA: 0s - loss: 602.6429
300/300 [==============================] - 0s 245us/sample - loss: 601.2843 - val_loss: 603.2950
Epoch 5/100

 32/300 [==>...........................] - ETA: 0s - loss: 601.3651
300/300 [==============================] - 0s 261us/sample - loss: 599.8374 - val_loss: 599.5879
Epoch 6/100

 32/300 [==>...........................] - ETA: 0s - loss: 599.7968
300/300 [==============================] - 0s 277us/sample - loss: 599.2142 - val_loss: 598.6806
Epoch 7/100

 32/300 [==>...........................] - ETA: 0s - loss: 595.5739
300/300 [==============================] - ETA: 0s - loss: 597.7195
300/300 [==============================] - 0s 308us/sample - loss: 597.7195 - val_loss: 596.7556
Epoch 8/100

 32/300 [==>...........................] - ETA: 0s - loss: 597.3861
300/300 [==============================] - ETA: 0s - loss: 597.3125
300/300 [==============================] - 0s 308us/sample - loss: 597.3125 - val_loss: 594.6665
Epoch 9/100

 32/300 [==>...........................] - ETA: 0s - loss: 593.7170
288/300 [===========================>..] - ETA: 0s - loss: 594.0556
300/300 [==============================] - 0s 378us/sample - loss: 594.0888 - val_loss: 591.0117
Epoch 10/100

 32/300 [==>...........................] - ETA: 0s - loss: 591.2639
300/300 [==============================] - ETA: 0s - loss: 590.5604
300/300 [==============================] - 0s 304us/sample - loss: 590.5604 - val_loss: 589.1824
Epoch 11/100

 32/300 [==>...........................] - ETA: 0s - loss: 590.3290
288/300 [===========================>..] - ETA: 0s - loss: 589.0400
300/300 [==============================] - 0s 338us/sample - loss: 589.1357 - val_loss: 588.1579
Epoch 12/100

 32/300 [==>...........................] - ETA: 0s - loss: 591.6268
300/300 [==============================] - 0s 296us/sample - loss: 587.6177 - val_loss: 586.6503
Epoch 13/100

 32/300 [==>...........................] - ETA: 0s - loss: 584.3870
288/300 [===========================>..] - ETA: 0s - loss: 585.9706
300/300 [==============================] - 0s 337us/sample - loss: 585.9434 - val_loss: 584.6153
Epoch 14/100

 32/300 [==>...........................] - ETA: 0s - loss: 586.4080
300/300 [==============================] - ETA: 0s - loss: 584.0884
300/300 [==============================] - 0s 317us/sample - loss: 584.0884 - val_loss: 582.7133
Epoch 15/100

 32/300 [==>...........................] - ETA: 0s - loss: 584.8075
300/300 [==============================] - ETA: 0s - loss: 583.3200
300/300 [==============================] - 0s 297us/sample - loss: 583.3200 - val_loss: 581.4463
Epoch 16/100

 32/300 [==>...........................] - ETA: 0s - loss: 581.0640
300/300 [==============================] - 0s 311us/sample - loss: 581.7065 - val_loss: 580.4421
Epoch 17/100

 32/300 [==>...........................] - ETA: 0s - loss: 580.0742
256/300 [========================>.....] - ETA: 0s - loss: 580.7148
300/300 [==============================] - 0s 361us/sample - loss: 580.7329 - val_loss: 579.9194
Epoch 18/100

 32/300 [==>...........................] - ETA: 0s - loss: 581.2576
300/300 [==============================] - ETA: 0s - loss: 580.1616
300/300 [==============================] - 0s 328us/sample - loss: 580.1616 - val_loss: 579.3833
Epoch 19/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.3789
288/300 [===========================>..] - ETA: 0s - loss: 579.7006
300/300 [==============================] - 0s 321us/sample - loss: 579.7140 - val_loss: 579.2953
Epoch 20/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.4491
300/300 [==============================] - 0s 291us/sample - loss: 579.6094 - val_loss: 579.2088
Epoch 21/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.9298
288/300 [===========================>..] - ETA: 0s - loss: 579.7697
300/300 [==============================] - 0s 325us/sample - loss: 579.6036 - val_loss: 579.3953
Epoch 22/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.8207
300/300 [==============================] - ETA: 0s - loss: 579.9964
300/300 [==============================] - 0s 301us/sample - loss: 579.9964 - val_loss: 579.5417
Epoch 23/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.4367
300/300 [==============================] - ETA: 0s - loss: 579.5066
300/300 [==============================] - 0s 332us/sample - loss: 579.5066 - val_loss: 579.4829
Epoch 24/100

 32/300 [==>...........................] - ETA: 0s - loss: 575.9515
288/300 [===========================>..] - ETA: 0s - loss: 579.5531
300/300 [==============================] - 0s 366us/sample - loss: 579.5465 - val_loss: 579.8302
Epoch 25/100

 32/300 [==>...........................] - ETA: 0s - loss: 580.0435
300/300 [==============================] - ETA: 0s - loss: 580.9922
300/300 [==============================] - 0s 300us/sample - loss: 580.9922 - val_loss: 580.1515
Epoch 26/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.0277
288/300 [===========================>..] - ETA: 0s - loss: 579.7832
300/300 [==============================] - 0s 323us/sample - loss: 579.9080 - val_loss: 579.2345
Epoch 27/100

 32/300 [==>...........................] - ETA: 0s - loss: 580.1425
288/300 [===========================>..] - ETA: 0s - loss: 579.4595
300/300 [==============================] - 0s 351us/sample - loss: 579.4142 - val_loss: 578.8008
Epoch 28/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.0876
288/300 [===========================>..] - ETA: 0s - loss: 579.3245
300/300 [==============================] - 0s 340us/sample - loss: 579.1497 - val_loss: 578.8971
Epoch 29/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.7809
300/300 [==============================] - ETA: 0s - loss: 579.3563
300/300 [==============================] - 0s 307us/sample - loss: 579.3563 - val_loss: 578.7053
Epoch 30/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.3322
300/300 [==============================] - ETA: 0s - loss: 578.8109
300/300 [==============================] - 0s 290us/sample - loss: 578.8109 - val_loss: 579.3028
Epoch 31/100

 32/300 [==>...........................] - ETA: 0s - loss: 575.9546
300/300 [==============================] - ETA: 0s - loss: 579.2258
300/300 [==============================] - 0s 296us/sample - loss: 579.2258 - val_loss: 581.1687
Epoch 32/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.2332
300/300 [==============================] - 0s 283us/sample - loss: 580.7144 - val_loss: 579.8112
Epoch 33/100

 32/300 [==>...........................] - ETA: 0s - loss: 581.6672
300/300 [==============================] - ETA: 0s - loss: 579.2022
300/300 [==============================] - 0s 322us/sample - loss: 579.2022 - val_loss: 578.7181
Epoch 34/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.6627
300/300 [==============================] - 0s 291us/sample - loss: 578.7545 - val_loss: 579.8445
Epoch 35/100

 32/300 [==>...........................] - ETA: 0s - loss: 580.0080
300/300 [==============================] - ETA: 0s - loss: 579.1962
300/300 [==============================] - 0s 323us/sample - loss: 579.1962 - val_loss: 578.3012
Epoch 36/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.3524
300/300 [==============================] - 0s 279us/sample - loss: 578.8937 - val_loss: 578.3208
Epoch 37/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.2126
300/300 [==============================] - ETA: 0s - loss: 578.7006
300/300 [==============================] - 0s 344us/sample - loss: 578.7006 - val_loss: 578.2462
Epoch 38/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.1388
300/300 [==============================] - 0s 300us/sample - loss: 578.7199 - val_loss: 578.2157
Epoch 39/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.6893
288/300 [===========================>..] - ETA: 0s - loss: 578.7408
300/300 [==============================] - 0s 316us/sample - loss: 578.7550 - val_loss: 578.5634
Epoch 40/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.2725
300/300 [==============================] - 0s 290us/sample - loss: 578.9247 - val_loss: 578.4557
Epoch 41/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.4936
300/300 [==============================] - 0s 287us/sample - loss: 578.7099 - val_loss: 578.5476
Epoch 42/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.2753
300/300 [==============================] - ETA: 0s - loss: 578.4906
300/300 [==============================] - 0s 300us/sample - loss: 578.4906 - val_loss: 578.4037
Epoch 43/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.4788
288/300 [===========================>..] - ETA: 0s - loss: 578.6267
300/300 [==============================] - 0s 311us/sample - loss: 578.7353 - val_loss: 578.2518
Epoch 44/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.8581
300/300 [==============================] - ETA: 0s - loss: 578.7802
300/300 [==============================] - 0s 301us/sample - loss: 578.7802 - val_loss: 578.5417
Epoch 45/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.7577
300/300 [==============================] - ETA: 0s - loss: 578.5289
300/300 [==============================] - 0s 312us/sample - loss: 578.5289 - val_loss: 577.9622
Epoch 46/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.1993
300/300 [==============================] - 0s 274us/sample - loss: 578.6074 - val_loss: 578.5393
Epoch 47/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.2775
300/300 [==============================] - 0s 281us/sample - loss: 578.2937 - val_loss: 578.0631
Epoch 48/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.9498
288/300 [===========================>..] - ETA: 0s - loss: 578.6202
300/300 [==============================] - 1s 3ms/sample - loss: 578.5509 - val_loss: 578.2229
Epoch 49/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.5522
256/300 [========================>.....] - ETA: 0s - loss: 578.3264
300/300 [==============================] - 0s 359us/sample - loss: 578.5429 - val_loss: 577.9526
Epoch 50/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.5665
300/300 [==============================] - ETA: 0s - loss: 578.2349
300/300 [==============================] - 0s 334us/sample - loss: 578.2349 - val_loss: 577.9265
Epoch 51/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.4781
288/300 [===========================>..] - ETA: 0s - loss: 578.2982
300/300 [==============================] - 0s 356us/sample - loss: 578.2195 - val_loss: 578.0465
Epoch 52/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.3842
300/300 [==============================] - ETA: 0s - loss: 578.6264
300/300 [==============================] - 0s 308us/sample - loss: 578.6264 - val_loss: 578.6533
Epoch 53/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.4321
300/300 [==============================] - ETA: 0s - loss: 578.4895
300/300 [==============================] - 0s 306us/sample - loss: 578.4895 - val_loss: 578.3741
Epoch 54/100

 32/300 [==>...........................] - ETA: 0s - loss: 580.5452
300/300 [==============================] - ETA: 0s - loss: 578.7046
300/300 [==============================] - 0s 299us/sample - loss: 578.7046 - val_loss: 578.7139
Epoch 55/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.3436
300/300 [==============================] - ETA: 0s - loss: 578.3036
300/300 [==============================] - 0s 323us/sample - loss: 578.3036 - val_loss: 577.6968
Epoch 56/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.8230
300/300 [==============================] - ETA: 0s - loss: 578.1954
300/300 [==============================] - 0s 321us/sample - loss: 578.1954 - val_loss: 577.9015
Epoch 57/100

 32/300 [==>...........................] - ETA: 0s - loss: 581.5431
300/300 [==============================] - ETA: 0s - loss: 578.1802
300/300 [==============================] - 0s 297us/sample - loss: 578.1802 - val_loss: 577.9089
Epoch 58/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.2073
300/300 [==============================] - 0s 286us/sample - loss: 577.9259 - val_loss: 577.7045
Epoch 59/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.4598
288/300 [===========================>..] - ETA: 0s - loss: 577.8953
300/300 [==============================] - 0s 319us/sample - loss: 577.9342 - val_loss: 577.4765
Epoch 60/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.1696
300/300 [==============================] - ETA: 0s - loss: 578.1554
300/300 [==============================] - 0s 303us/sample - loss: 578.1554 - val_loss: 577.6761
Epoch 61/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.1683
300/300 [==============================] - 0s 272us/sample - loss: 577.9310 - val_loss: 577.5173
Epoch 62/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.1322
300/300 [==============================] - ETA: 0s - loss: 577.7010
300/300 [==============================] - 0s 285us/sample - loss: 577.7010 - val_loss: 578.0153
Epoch 63/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.0369
300/300 [==============================] - 0s 273us/sample - loss: 577.8886 - val_loss: 577.9256
Epoch 64/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.1699
300/300 [==============================] - ETA: 0s - loss: 578.2474
300/300 [==============================] - 0s 292us/sample - loss: 578.2474 - val_loss: 577.9450
Epoch 65/100

 32/300 [==>...........................] - ETA: 0s - loss: 581.2871
300/300 [==============================] - 0s 262us/sample - loss: 578.0415 - val_loss: 577.4459
Epoch 66/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.7867
288/300 [===========================>..] - ETA: 0s - loss: 577.5219
300/300 [==============================] - 0s 320us/sample - loss: 577.5787 - val_loss: 577.5474
Epoch 67/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.6685
300/300 [==============================] - ETA: 0s - loss: 577.8698
300/300 [==============================] - 0s 305us/sample - loss: 577.8698 - val_loss: 577.7022
Epoch 68/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.5612
300/300 [==============================] - 0s 291us/sample - loss: 577.5845 - val_loss: 577.4463
Epoch 69/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.6013
288/300 [===========================>..] - ETA: 0s - loss: 578.0499
300/300 [==============================] - 0s 309us/sample - loss: 577.8672 - val_loss: 577.4521
Epoch 70/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.2272
300/300 [==============================] - 0s 311us/sample - loss: 577.6783 - val_loss: 577.2854
Epoch 71/100

 32/300 [==>...........................] - ETA: 0s - loss: 575.8434
300/300 [==============================] - ETA: 0s - loss: 577.5166
300/300 [==============================] - 0s 294us/sample - loss: 577.5166 - val_loss: 577.0156
Epoch 72/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.3799
300/300 [==============================] - 0s 293us/sample - loss: 577.9002 - val_loss: 577.2207
Epoch 73/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.0160
288/300 [===========================>..] - ETA: 0s - loss: 577.8246
300/300 [==============================] - 0s 312us/sample - loss: 577.8347 - val_loss: 578.0924
Epoch 74/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.3498
288/300 [===========================>..] - ETA: 0s - loss: 577.6512
300/300 [==============================] - 0s 314us/sample - loss: 577.7352 - val_loss: 577.5734
Epoch 75/100

 32/300 [==>...........................] - ETA: 0s - loss: 579.6338
300/300 [==============================] - 0s 289us/sample - loss: 578.0444 - val_loss: 577.2284
Epoch 76/100

 32/300 [==>...........................] - ETA: 0s - loss: 575.1534
300/300 [==============================] - ETA: 0s - loss: 577.5870
300/300 [==============================] - 0s 283us/sample - loss: 577.5870 - val_loss: 577.2396
Epoch 77/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.3832
300/300 [==============================] - ETA: 0s - loss: 577.5007
300/300 [==============================] - 0s 323us/sample - loss: 577.5007 - val_loss: 577.1855
Epoch 78/100

 32/300 [==>...........................] - ETA: 0s - loss: 578.0580
300/300 [==============================] - ETA: 0s - loss: 577.4889
300/300 [==============================] - 0s 294us/sample - loss: 577.4889 - val_loss: 577.1653
Epoch 79/100

 32/300 [==>...........................] - ETA: 0s - loss: 575.6101
300/300 [==============================] - 0s 344us/sample - loss: 577.5352 - val_loss: 577.2803
Epoch 80/100

 32/300 [==>...........................] - ETA: 0s - loss: 577.7758
300/300 [==============================] - ETA: 0s - loss: 577.9124
300/300 [==============================] - 0s 306us/sample - loss: 577.9124 - val_loss: 577.6696
Epoch 81/100

 32/300 [==>...........................] - ETA: 0s - loss: 576.2922
300/300 [==============================] - ETA: 0s - loss: 577.5339
300/300 [==============================] - 0s 452us/sample - loss: 577.5339 - val_loss: 577.7261
Epoch 1/100

1/3 [=========>....................] - ETA: 1s - batch: 0.0000e+00 - size: 32.0000 - loss: 13588.3965
2/3 [===================>..........] - ETA: 0s - batch: 0.5000 - size: 32.0000 - loss: 13470.9775    
3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: 2294625293141852028928.0000
3/3 [==============================] - 1s 90ms/step - batch: 1.0000 - size: 32.0000 - loss: 2294625293141852028928.0000
Epoch 2/100

1/3 [=========>....................] - ETA: 0s - batch: 0.0000e+00 - size: 32.0000 - loss: nan
2/3 [===================>..........] - ETA: 0s - batch: 0.5000 - size: 32.0000 - loss: nan    
3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 112ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 3/100

1/3 [=========>....................] - ETA: 0s - batch: 0.0000e+00 - size: 32.0000 - loss: nan
2/3 [===================>..........] - ETA: 0s - batch: 0.5000 - size: 32.0000 - loss: nan    /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  warnings.warn('`Model.state_updates` will be removed in a future version. '
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  warnings.warn('`Model.state_updates` will be removed in a future version. '
WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0333s vs `on_train_batch_begin` time: 0.0509s). Check your callbacks.

3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 1s 478ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 4/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 63ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 5/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 67ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 6/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 65ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 7/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 86ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 8/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 79ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 9/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 72ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 10/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 74ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 11/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 96ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 12/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 69ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 13/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 78ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 14/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 68ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 15/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 87ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 16/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 74ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 17/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 63ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 18/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 69ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 19/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 1s 413ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 20/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 68ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Epoch 21/100

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3/3 [==============================] - ETA: 0s - batch: 1.0000 - size: 32.0000 - loss: nan
3/3 [==============================] - 0s 124ms/step - batch: 1.0000 - size: 32.0000 - loss: nan
Quitting from lines 205-214 (VAExprs.Rmd) 
Error: processing vignette 'VAExprs.Rmd' failed with diagnostics:
NA/NaN/Inf in foreign function call (arg 1)
--- failed re-building ‘VAExprs.Rmd’

SUMMARY: processing the following file failed:
  ‘VAExprs.Rmd’

Error: Vignette re-building failed.
Execution halted