Console log: 'Microsoft.ML.TensorFlow.Tests' from job f691e486-7d5b-4885-9e4a-1ea3c59477ef workitem 0997e603-be95-4461-b0f3-6db2225e6787 (windows.10.amd64.open) executed on machine a00EH82 running Windows-2016Server-10.0.14393-SP0 C:\h\w\A9B70968\w\A10408C3\e>set ML_TEST_DATADIR=C:\h\w\A9B70968\p C:\h\w\A9B70968\w\A10408C3\e>set MICROSOFTML_RESOURCE_PATH=C:\h\w\A9B70968\w\A10408C3\e C:\h\w\A9B70968\w\A10408C3\e>set PATH=C:\h\w\A9B70968\p\dotnet-cli;C:\python3\Scripts\;C:\python3\;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Debuggers\x64;C:\Program Files\Microsoft SQL Server\160\Tools\Binn\;C:\Users\runner\AppData\Local\Microsoft\WindowsApps C:\h\w\A9B70968\w\A10408C3\e>call .\runTests.cmd ----- start Fri 04/24/2026 23:39:08.83 =============== To repro directly: ===================================================== pushd C:\h\w\A9B70968\w\A10408C3\e\ C:\h\w\A9B70968\p/xunit-runner/tools/net48/xunit.console.exe Microsoft.ML.TensorFlow.Tests.dll -notrait Category=SkipInCI -xml testResults.xml popd =========================================================================================================== C:\h\w\A9B70968\w\A10408C3\e>C:\h\w\A9B70968\p/xunit-runner/tools/net48/xunit.console.exe Microsoft.ML.TensorFlow.Tests.dll -notrait Category=SkipInCI -xml testResults.xml xUnit.net Console Runner v2.9.3+9712244020 (64-bit .NET Framework 4.8, runtime: 4.0.30319.42000) Discovering: Microsoft.ML.TensorFlow.Tests 2026-04-24 23:39:15.628739: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:15.688448: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:15.766735: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:15.829804: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:15.888621: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:15.945849: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.005987: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.065913: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.125080: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.183824: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.244141: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.301155: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.358053: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.415738: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.474406: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.531638: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.588768: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.648048: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.705873: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.765193: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.823215: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.882554: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.941271: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:16.999333: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.056840: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.121585: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.180138: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.237588: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.294713: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.351273: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.408355: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.464927: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.521107: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:17.577534: I tensorflow/core/util/port.cc:113] 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`. Discovered: Microsoft.ML.TensorFlow.Tests Starting: Microsoft.ML.TensorFlow.Tests Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputShapeTest 2026-04-24 23:39:19.798539: I tensorflow/core/util/port.cc:113] 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`. 2026-04-24 23:39:19.819391: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_shape_test 2026-04-24 23:39:19.820067: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:39:19.820233: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_shape_test 2026-04-24 23:39:19.821090: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2026-04-24 23:39:19.822705: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled 2026-04-24 23:39:19.824628: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 4459 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputShapeTest with memory usage 618,057,728.00 and max memory usage 618,885,120.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationDefault Saver not created because there are no variables in the graph to restore Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\njynpu5s.qnq\custom_retrained_model_based_on_resnet_v2_50_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationDefault with memory usage 1,015,648,256.00 and max memory usage 2,635,259,904.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationWithExponentialLRScheduling Saver not created because there are no variables in the graph to restore Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 10 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 11 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 12 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 13 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 14 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 15 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 16 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 17 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 18 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 19 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 20 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 21 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 22 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 23 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 24 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 25 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 26 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 27 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 28 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 29 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 30 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 31 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 32 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 33 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 34 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 35 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 36 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 37 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 38 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 39 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 40 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 41 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 0, Accuracy: 0.52, Cross-Entropy: 1.328545, Learning Rate: 0.01 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.7777778, Cross-Entropy: 0.5619881 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 1, Accuracy: 0.82, Cross-Entropy: 0.6804146, Learning Rate: 0.0094 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.3485622 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.3435691, Learning Rate: 0.0094 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.2692264 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2489292, Learning Rate: 0.008836 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2302867 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.1940039, Learning Rate: 0.008836 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.2056145 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1608252, Learning Rate: 0.008305839 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1892275 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.137071, Learning Rate: 0.008305839 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1767262 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.120511, Learning Rate: 0.007807489 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1673212 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1073857, Learning Rate: 0.007807489 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1595277 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.0975167, Learning Rate: 0.00733904 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1532939 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.08922131, Learning Rate: 0.00733904 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1478932 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.08268945, Learning Rate: 0.006898697 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1434115 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.07698561, Learning Rate: 0.006898697 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1394204 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.07235328, Learning Rate: 0.006484775 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1360252 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.06819858, Learning Rate: 0.006484775 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1329446 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.06474911, Learning Rate: 0.006095689 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.130276 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.06159366, Learning Rate: 0.006095689 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1278217 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.05893014, Learning Rate: 0.005729948 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1256661 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.05645646, Learning Rate: 0.005729948 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1236632 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.05434157, Learning Rate: 0.005386151 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1218848 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.05235375, Learning Rate: 0.005386151 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1202191 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.05063691, Learning Rate: 0.005062982 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1187271 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.04900739, Learning Rate: 0.005062982 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.1173205 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.04758839, Learning Rate: 0.004759203 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.1160512 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.04623078, Learning Rate: 0.004759203 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.1148485 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.0450404, Learning Rate: 0.004473651 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.1137565 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 26, Accuracy: 1, Cross-Entropy: 0.04389384, Learning Rate: 0.004473651 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 26, Accuracy: 1, Cross-Entropy: 0.1127172 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 27, Accuracy: 1, Cross-Entropy: 0.04288285, Learning Rate: 0.004205232 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 27, Accuracy: 1, Cross-Entropy: 0.1117687 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 28, Accuracy: 1, Cross-Entropy: 0.0419034, Learning Rate: 0.004205232 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 28, Accuracy: 1, Cross-Entropy: 0.1108626 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 29, Accuracy: 1, Cross-Entropy: 0.04103556, Learning Rate: 0.003952918 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 29, Accuracy: 1, Cross-Entropy: 0.1100321 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 30, Accuracy: 1, Cross-Entropy: 0.04019072, Learning Rate: 0.003952918 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 30, Accuracy: 1, Cross-Entropy: 0.1092362 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 31, Accuracy: 1, Cross-Entropy: 0.039439, Learning Rate: 0.003715743 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 31, Accuracy: 1, Cross-Entropy: 0.1085037 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 32, Accuracy: 1, Cross-Entropy: 0.03870412, Learning Rate: 0.003715743 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 32, Accuracy: 1, Cross-Entropy: 0.1077999 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 33, Accuracy: 1, Cross-Entropy: 0.03804785, Learning Rate: 0.003492798 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 33, Accuracy: 1, Cross-Entropy: 0.10715 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 34, Accuracy: 1, Cross-Entropy: 0.0374039, Learning Rate: 0.003492798 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 34, Accuracy: 1, Cross-Entropy: 0.1065241 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 35, Accuracy: 1, Cross-Entropy: 0.03682704, Learning Rate: 0.00328323 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 35, Accuracy: 1, Cross-Entropy: 0.1059444 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 36, Accuracy: 1, Cross-Entropy: 0.03625919, Learning Rate: 0.00328323 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 36, Accuracy: 1, Cross-Entropy: 0.1053849 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 37, Accuracy: 1, Cross-Entropy: 0.03574905, Learning Rate: 0.003086236 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 37, Accuracy: 1, Cross-Entropy: 0.1048655 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 38, Accuracy: 1, Cross-Entropy: 0.03524545, Learning Rate: 0.003086236 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 38, Accuracy: 1, Cross-Entropy: 0.1043632 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 39, Accuracy: 1, Cross-Entropy: 0.03479192, Learning Rate: 0.002901062 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 39, Accuracy: 1, Cross-Entropy: 0.1038958 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 40, Accuracy: 1, Cross-Entropy: 0.03434303, Learning Rate: 0.002901062 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 40, Accuracy: 1, Cross-Entropy: 0.1034431 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 41, Accuracy: 1, Cross-Entropy: 0.03393792, Learning Rate: 0.002726999 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 41, Accuracy: 1, Cross-Entropy: 0.1030209 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 42, Accuracy: 1, Cross-Entropy: 0.03353605, Learning Rate: 0.002726999 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 42, Accuracy: 1, Cross-Entropy: 0.1026115 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 43, Accuracy: 1, Cross-Entropy: 0.03317268, Learning Rate: 0.002563379 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 43, Accuracy: 1, Cross-Entropy: 0.1022289 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 44, Accuracy: 1, Cross-Entropy: 0.03281148, Learning Rate: 0.002563379 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 44, Accuracy: 1, Cross-Entropy: 0.1018574 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 45, Accuracy: 1, Cross-Entropy: 0.03248427, Learning Rate: 0.002409576 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 45, Accuracy: 1, Cross-Entropy: 0.1015097 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 46, Accuracy: 1, Cross-Entropy: 0.03215843, Learning Rate: 0.002409576 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 46, Accuracy: 1, Cross-Entropy: 0.1011716 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 47, Accuracy: 1, Cross-Entropy: 0.0318628, Learning Rate: 0.002265001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 47, Accuracy: 1, Cross-Entropy: 0.1008548 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 48, Accuracy: 1, Cross-Entropy: 0.0315679, Learning Rate: 0.002265001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 48, Accuracy: 1, Cross-Entropy: 0.1005464 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 49, Accuracy: 1, Cross-Entropy: 0.03129997, Learning Rate: 0.002129101 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 49, Accuracy: 1, Cross-Entropy: 0.1002569 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_0\custom_retrained_model_based_on_resnet_v2_101_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationWithExponentialLRScheduling with memory usage 1,533,157,376.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifar Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifar with memory usage 1,546,121,216.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransforCifarEndToEndTest2 Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransforCifarEndToEndTest2 with memory usage 1,725,833,216.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationBadImages Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformObjectDetectionTest [SKIP] Model files are not available yet Saver not created because there are no variables in the graph to restore 2026-04-24 23:42:58.115893: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INVALID_ARGUMENT: Trying to decode BMP format using a wrong op. Use `decode_bmp` or `decode_image` instead. Op used: DecodeJpeg [[{{node DecodeJpeg}}]] Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\rw3ua15z.qaw\custom_retrained_model_based_on_resnet_v2_101_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationBadImages with memory usage 1,531,056,128.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTest with memory usage 1,552,134,144.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorflowPlaceholderShapeInferenceTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorflowPlaceholderShapeInferenceTest with memory usage 1,552,199,680.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification Saver not created because there are no variables in the graph to restore Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.7777778, Cross-Entropy: 0.5557055 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.3469596 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.2683212 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2296086 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.2050639 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1887539 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1763037 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1669374 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1591714 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1529613 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1475785 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1431135 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1391355 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1357529 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1326823 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.1300239 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1275778 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1254306 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1234346 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1216633 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1200034 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1185175 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.117116 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_0\custom_retrained_model_based_on_resnet_v2_101_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification with memory usage 1,586,053,120.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification Saver not created because there are no variables in the graph to restore Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 10 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 11 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 12 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 13 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 14 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 15 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 16 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 17 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 18 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 19 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 20 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 21 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 22 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 23 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 24 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 25 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 26 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 27 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 28 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 29 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 30 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 31 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 32 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 33 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 34 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 35 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 36 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 37 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 38 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 39 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 40 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 41 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.7777778, Cross-Entropy: 0.5445184 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 1, Cross-Entropy: 0.3860295 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.3264747 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2900043 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.2656592 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.2475205 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.2338268 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.2227189 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.2138002 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.2062041 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1998716 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1943045 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1895451 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1852671 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1815435 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.1781418 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.175141 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1723653 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1698915 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.167581 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1655048 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1635505 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_2\custom_retrained_model_based_on_mobilenet_v2.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification with memory usage 1,708,724,224.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification Saver not created because there are no variables in the graph to restore Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 10 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 11 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 12 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 13 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 14 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 15 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 16 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 17 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 18 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 19 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 20 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 21 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 22 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 23 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 24 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 25 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 26 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 27 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 28 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 29 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 30 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 31 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 32 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 33 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 34 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 35 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 36 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 37 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 38 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 39 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 40 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 41 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.7777778, Cross-Entropy: 0.6536338 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.4061742 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 0.8888889, Cross-Entropy: 0.3163792 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 0.8888889, Cross-Entropy: 0.2756419 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 0.8888889, Cross-Entropy: 0.2496679 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.2326372 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.2194329 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.2094605 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.2010989 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.194352 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1884797 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1835512 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1791679 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1753888 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1719798 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.1689817 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1662499 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1638104 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1615705 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1595457 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1576758 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1559685 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.1543842 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.1529257 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.1515672 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.1503077 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_3\custom_retrained_model_based_on_resnet_v2_50_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification with memory usage 1,911,787,520.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification Saver not created because there are no variables in the graph to restore Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Validation, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 1 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 2 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 3 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 4 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 5 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 6 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 7 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 8 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 9 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 10 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 11 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 12 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 13 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 14 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 15 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 16 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 17 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 18 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 19 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 20 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 21 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 22 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 23 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 24 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 25 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 26 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 27 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 28 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 29 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 30 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 31 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 32 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 33 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 34 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 35 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 36 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 37 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 38 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 39 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 40 Phase: Bottleneck Computation, Dataset used: Train, Image Index: 41 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.7777778, Cross-Entropy: 0.6804911 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.5300652 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 0.8888889, Cross-Entropy: 0.4539562 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.4100402 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.3807624 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.3598836 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.3438494 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.3312071 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.3208122 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.3121643 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.3047682 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.2984073 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.2928266 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.2879178 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.2835336 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.2796143 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.2760677 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.2728581 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.2699245 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.2672442 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.264775 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.2625017 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.2603942 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.2584418 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_1\custom_retrained_model_based_on_inception_v3.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassification with memory usage 1,660,006,400.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowInputsOutputsSchemaTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowInputsOutputsSchemaTest with memory usage 1,686,347,776.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMatrixMultiplicationTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMatrixMultiplicationTest with memory usage 1,686,777,856.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTLRTrainingTest 2026-04-24 23:48:06.009461: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_lr_model 2026-04-24 23:48:06.010569: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:48:06.010772: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_lr_model 2026-04-24 23:48:06.013460: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:48:06.021688: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 12227 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTLRTrainingTest with memory usage 1,695,281,152.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTrainingTest 2026-04-24 23:48:06.422687: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_conv_model 2026-04-24 23:48:06.424081: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:48:06.424276: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_conv_model 2026-04-24 23:48:06.428922: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:48:06.453900: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 31210 microseconds. 2026-04-24 23:48:06.962682: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_conv_model 2026-04-24 23:48:06.964328: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:48:06.964600: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_conv_model 2026-04-24 23:48:06.970279: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:48:07.002731: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 40038 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTrainingTest with memory usage 1,698,377,728.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSentimentClassificationTest Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInceptionTest [SKIP] Model files are not available yet 2026-04-24 23:48:07.659768: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: sentiment_model 2026-04-24 23:48:07.662668: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:48:07.662868: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: sentiment_model 2026-04-24 23:48:07.677963: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:48:07.725487: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 65708 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSentimentClassificationTest with memory usage 1,699,991,552.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarSavedModel 2026-04-24 23:48:07.832144: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:48:07.833516: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:48:07.833712: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:48:07.838568: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:48:07.859263: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 27116 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarSavedModel with memory usage 1,702,506,496.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputOutputTypesTest 2026-04-24 23:48:07.911067: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_types_test 2026-04-24 23:48:07.911715: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:48:07.911909: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_types_test 2026-04-24 23:48:07.912625: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 1559 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputOutputTypesTest with memory usage 1,702,977,536.00 and max memory usage 5,154,443,264.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationEarlyStopping Saver not created because there are no variables in the graph to restore Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 0, Accuracy: 0.9, Cross-Entropy: 0.3019042 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 1, Accuracy: 1, Cross-Entropy: 0.1979824 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.1640134 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.1462716 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.1343397 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1264946 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1199871 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1151581 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1109204 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1075947 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1045897 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1021442 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.09989235 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.09801164 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.09625667 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.09476227 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.09335382 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.09213634 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.09098007 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.08996865 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.08900223 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.08814865 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_0\custom_retrained_model_based_on_resnet_v2_101_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationEarlyStopping with memory usage 1,677,819,904.00 and max memory usage 5,264,039,936.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationEarlyStopping Saver not created because there are no variables in the graph to restore Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 0, Accuracy: 0.9, Cross-Entropy: 0.2923562 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 1, Accuracy: 1, Cross-Entropy: 0.1926697 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.1593665 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.1419328 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.1302845 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1226606 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1163488 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1116739 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1075747 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1043617 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1014595 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.09909986 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.09692753 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.09511462 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.09342301 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.09198357 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.09062682 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.08945487 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.08834171 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.08736856 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.08643857 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.0856177 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.08482921 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.08412778 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.08345128 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.08284535 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 26, Accuracy: 1, Cross-Entropy: 0.08225892 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 27, Accuracy: 1, Cross-Entropy: 0.08173074 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 28, Accuracy: 1, Cross-Entropy: 0.08121806 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 29, Accuracy: 1, Cross-Entropy: 0.08075413 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 30, Accuracy: 1, Cross-Entropy: 0.08030267 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 31, Accuracy: 1, Cross-Entropy: 0.0798924 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 32, Accuracy: 1, Cross-Entropy: 0.07949223 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 33, Accuracy: 1, Cross-Entropy: 0.07912739 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 34, Accuracy: 1, Cross-Entropy: 0.07877076 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 35, Accuracy: 1, Cross-Entropy: 0.07844454 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 36, Accuracy: 1, Cross-Entropy: 0.07812519 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 37, Accuracy: 1, Cross-Entropy: 0.07783229 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 38, Accuracy: 1, Cross-Entropy: 0.07754514 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 39, Accuracy: 1, Cross-Entropy: 0.07728102 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 40, Accuracy: 1, Cross-Entropy: 0.07702178 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 41, Accuracy: 1, Cross-Entropy: 0.0767829 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 42, Accuracy: 1, Cross-Entropy: 0.07654813 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 43, Accuracy: 1, Cross-Entropy: 0.0763313 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 44, Accuracy: 1, Cross-Entropy: 0.07611791 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 45, Accuracy: 1, Cross-Entropy: 0.0759206 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_0\custom_retrained_model_based_on_resnet_v2_101_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationEarlyStopping with memory usage 1,795,473,408.00 and max memory usage 5,264,039,936.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowStringTest 2026-04-24 23:51:03.596287: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_string_test 2026-04-24 23:51:03.596886: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:03.597089: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_string_test 2026-04-24 23:51:03.597742: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 1457 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowStringTest with memory usage 1,795,571,712.00 and max memory usage 5,264,039,936.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSaveAndLoadSavedModel 2026-04-24 23:51:03.633794: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:03.635621: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:03.635902: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:03.642443: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:03.668975: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 35176 microseconds. 2026-04-24 23:51:03.922728: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\h\w\A9B70968\t\TensorFlowTransformer_7ee91c6b-dff8-4cec-93f5-76bda23d5244 2026-04-24 23:51:03.924165: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:03.924360: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: C:\h\w\A9B70968\t\TensorFlowTransformer_7ee91c6b-dff8-4cec-93f5-76bda23d5244 2026-04-24 23:51:03.929339: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:03.949206: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 26476 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSaveAndLoadSavedModel with memory usage 1,801,797,632.00 and max memory usage 5,264,039,936.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowGettingSchemaMultipleTimes 2026-04-24 23:51:04.026017: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.027875: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.028075: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.033458: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.053729: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 27707 microseconds. 2026-04-24 23:51:04.066338: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.067607: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.067802: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.072759: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.092477: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 26133 microseconds. 2026-04-24 23:51:04.107818: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.109409: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.109607: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.114705: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.134222: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 26400 microseconds. 2026-04-24 23:51:04.146019: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.147352: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.147549: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.152519: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.173115: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 27085 microseconds. 2026-04-24 23:51:04.191161: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.193092: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.193400: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.200268: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.228916: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 37750 microseconds. 2026-04-24 23:51:04.241248: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.242680: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.242878: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.247888: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.268405: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 27150 microseconds. 2026-04-24 23:51:04.282709: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.283995: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.284192: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.288997: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.309113: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 26399 microseconds. 2026-04-24 23:51:04.320898: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.322144: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.322339: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.326999: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.347431: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 26527 microseconds. 2026-04-24 23:51:04.359489: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.360879: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.361105: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.365990: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.391162: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 31666 microseconds. 2026-04-24 23:51:04.410148: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:51:04.411503: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:51:04.411700: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:51:04.416509: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:51:04.436085: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 25930 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowGettingSchemaMultipleTimes with memory usage 1,802,100,736.00 and max memory usage 5,264,039,936.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationWithPolynomialLRScheduling Saver not created because there are no variables in the graph to restore Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 0, Accuracy: 0.56, Cross-Entropy: 1.345866, Learning Rate: 0.00505 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.8888889, Cross-Entropy: 0.5441733 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 1, Accuracy: 0.8, Cross-Entropy: 0.7040228, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.4783581 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 2, Accuracy: 0.9399999, Cross-Entropy: 0.5701614, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 0.8888889, Cross-Entropy: 0.4769138 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 3, Accuracy: 0.9399999, Cross-Entropy: 0.5671547, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 0.8888889, Cross-Entropy: 0.4754697 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 4, Accuracy: 0.9399999, Cross-Entropy: 0.5641825, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 0.8888889, Cross-Entropy: 0.4740259 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 5, Accuracy: 0.9399999, Cross-Entropy: 0.5612438, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 0.8888889, Cross-Entropy: 0.4725828 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 6, Accuracy: 0.9399999, Cross-Entropy: 0.5583381, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 0.8888889, Cross-Entropy: 0.4711407 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 7, Accuracy: 0.9399999, Cross-Entropy: 0.5554642, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 0.8888889, Cross-Entropy: 0.4696998 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 8, Accuracy: 0.9399999, Cross-Entropy: 0.5526216, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 0.8888889, Cross-Entropy: 0.4682606 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 9, Accuracy: 0.9399999, Cross-Entropy: 0.5498095, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 0.8888889, Cross-Entropy: 0.4668232 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 10, Accuracy: 0.9399999, Cross-Entropy: 0.5470269, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 0.8888889, Cross-Entropy: 0.4653878 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 11, Accuracy: 0.9399999, Cross-Entropy: 0.5442737, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 0.8888889, Cross-Entropy: 0.4639548 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 12, Accuracy: 0.9399999, Cross-Entropy: 0.5415488, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 0.8888889, Cross-Entropy: 0.4625245 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 13, Accuracy: 0.96, Cross-Entropy: 0.5388519, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 0.8888889, Cross-Entropy: 0.4610969 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 14, Accuracy: 0.96, Cross-Entropy: 0.5361822, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 0.8888889, Cross-Entropy: 0.4596725 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 15, Accuracy: 0.96, Cross-Entropy: 0.5335391, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 0.8888889, Cross-Entropy: 0.4582513 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 16, Accuracy: 0.96, Cross-Entropy: 0.5309222, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 0.8888889, Cross-Entropy: 0.4568337 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 17, Accuracy: 0.96, Cross-Entropy: 0.5283309, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 0.8888889, Cross-Entropy: 0.4554198 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 18, Accuracy: 0.96, Cross-Entropy: 0.5257648, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 0.8888889, Cross-Entropy: 0.4540099 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 19, Accuracy: 0.96, Cross-Entropy: 0.5232235, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 0.8888889, Cross-Entropy: 0.4526038 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 20, Accuracy: 0.96, Cross-Entropy: 0.5207063, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 0.8888889, Cross-Entropy: 0.4512022 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 21, Accuracy: 0.96, Cross-Entropy: 0.5182127, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 0.8888889, Cross-Entropy: 0.4498051 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 22, Accuracy: 0.96, Cross-Entropy: 0.5157426, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 0.8888889, Cross-Entropy: 0.4484124 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 23, Accuracy: 0.96, Cross-Entropy: 0.5132955, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 0.8888889, Cross-Entropy: 0.4470247 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 24, Accuracy: 0.96, Cross-Entropy: 0.5108708, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 24, Accuracy: 0.8888889, Cross-Entropy: 0.4456417 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 25, Accuracy: 0.96, Cross-Entropy: 0.5084682, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 25, Accuracy: 0.8888889, Cross-Entropy: 0.4442638 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 26, Accuracy: 0.96, Cross-Entropy: 0.5060874, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 26, Accuracy: 0.8888889, Cross-Entropy: 0.4428911 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 27, Accuracy: 0.96, Cross-Entropy: 0.5037281, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 27, Accuracy: 0.8888889, Cross-Entropy: 0.4415237 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 28, Accuracy: 0.96, Cross-Entropy: 0.5013898, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 28, Accuracy: 0.8888889, Cross-Entropy: 0.4401617 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 29, Accuracy: 0.96, Cross-Entropy: 0.4990723, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 29, Accuracy: 0.8888889, Cross-Entropy: 0.4388052 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 30, Accuracy: 0.96, Cross-Entropy: 0.4967751, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 30, Accuracy: 0.8888889, Cross-Entropy: 0.4374544 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 31, Accuracy: 0.96, Cross-Entropy: 0.4944979, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 31, Accuracy: 0.8888889, Cross-Entropy: 0.4361093 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 32, Accuracy: 0.96, Cross-Entropy: 0.4922407, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 32, Accuracy: 0.8888889, Cross-Entropy: 0.43477 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 33, Accuracy: 0.96, Cross-Entropy: 0.4900028, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 33, Accuracy: 0.8888889, Cross-Entropy: 0.4334366 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 34, Accuracy: 0.96, Cross-Entropy: 0.4877841, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 34, Accuracy: 0.8888889, Cross-Entropy: 0.4321092 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 35, Accuracy: 0.96, Cross-Entropy: 0.4855843, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 35, Accuracy: 0.8888889, Cross-Entropy: 0.4307878 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 36, Accuracy: 0.96, Cross-Entropy: 0.4834031, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 36, Accuracy: 0.8888889, Cross-Entropy: 0.4294726 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 37, Accuracy: 0.96, Cross-Entropy: 0.4812402, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 37, Accuracy: 0.8888889, Cross-Entropy: 0.4281636 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 38, Accuracy: 0.96, Cross-Entropy: 0.4790956, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 38, Accuracy: 0.8888889, Cross-Entropy: 0.4268607 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 39, Accuracy: 0.96, Cross-Entropy: 0.4769687, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 39, Accuracy: 0.8888889, Cross-Entropy: 0.4255641 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 40, Accuracy: 0.96, Cross-Entropy: 0.4748594, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 40, Accuracy: 0.8888889, Cross-Entropy: 0.424274 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 41, Accuracy: 0.96, Cross-Entropy: 0.4727675, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 41, Accuracy: 0.8888889, Cross-Entropy: 0.4229902 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 42, Accuracy: 0.96, Cross-Entropy: 0.4706927, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 42, Accuracy: 0.8888889, Cross-Entropy: 0.4217129 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 43, Accuracy: 0.96, Cross-Entropy: 0.4686347, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 43, Accuracy: 0.8888889, Cross-Entropy: 0.4204419 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 44, Accuracy: 0.96, Cross-Entropy: 0.4665934, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 44, Accuracy: 0.8888889, Cross-Entropy: 0.4191776 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 45, Accuracy: 0.96, Cross-Entropy: 0.4645686, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 45, Accuracy: 0.8888889, Cross-Entropy: 0.4179197 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 46, Accuracy: 0.96, Cross-Entropy: 0.4625599, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 46, Accuracy: 0.8888889, Cross-Entropy: 0.4166684 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 47, Accuracy: 0.96, Cross-Entropy: 0.4605673, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 47, Accuracy: 0.8888889, Cross-Entropy: 0.4154236 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 48, Accuracy: 0.96, Cross-Entropy: 0.4585906, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 48, Accuracy: 0.8888889, Cross-Entropy: 0.4141854 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 49, Accuracy: 0.96, Cross-Entropy: 0.4566293, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 49, Accuracy: 0.8888889, Cross-Entropy: 0.4129539 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A9B70968\t\jfe0xcex.jvc\assets\cached\FPTSUT_0\custom_retrained_model_based_on_resnet_v2_101_299.meta Froze 2 variables. Converted 2 variables to const ops. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowImageClassificationWithPolynomialLRScheduling with memory usage 3,288,543,232.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowPrimitiveInputTest 2026-04-24 23:52:35.410523: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_primitive_input_test 2026-04-24 23:52:35.411408: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:52:35.411706: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_primitive_input_test 2026-04-24 23:52:35.412834: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 2311 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowPrimitiveInputTest with memory usage 3,288,600,576.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvSavedModelTest 2026-04-24 23:52:35.427583: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_model 2026-04-24 23:52:35.429058: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:52:35.429368: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_model 2026-04-24 23:52:35.433890: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:52:35.470898: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: mnist_model 2026-04-24 23:52:35.476236: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 48649 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvSavedModelTest with memory usage 3,291,791,360.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarInvalidShape Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarInvalidShape with memory usage 3,291,815,936.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarCrossValidationWithInMemoryImages 2026-04-24 23:52:36.487945: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-04-24 23:52:36.489811: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-04-24 23:52:36.490112: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-04-24 23:52:36.497090: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-04-24 23:52:36.525289: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 37339 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarCrossValidationWithInMemoryImages with memory usage 3,297,865,728.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlow Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlow with memory usage 3,296,833,536.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlowWithSchema Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlowWithSchema with memory usage 3,299,291,136.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestCommandLine 3 columns: a: Vector b: Vector c: Vector Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestCommandLine with memory usage 3,305,123,840.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestLoadMultipleModel Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestLoadMultipleModel with memory usage 3,305,123,840.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestOldSavingAndLoading Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestOldSavingAndLoading with memory usage 3,300,442,112.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestSimpleCase Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestSimpleCase with memory usage 3,301,638,144.00 and max memory usage 6,037,286,912.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TreatOutputAsBatched Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TreatOutputAsBatched with memory usage 3,303,948,288.00 and max memory usage 6,037,286,912.00 Finished: Microsoft.ML.TensorFlow.Tests Unhandled exception: System.ObjectDisposedException: Cannot access a disposed object. Object name: 'The ThreadLocal object has been disposed.'. at System.Threading.ThreadLocal`1.GetValueSlow() at Tensorflow.BaseSession.DisposeUnmanagedResources(IntPtr handle) at Tensorflow.DisposableObject.Dispose(Boolean disposing) at Tensorflow.DisposableObject.Finalize() System.ObjectDisposedException: Cannot access a disposed object. Object name: 'The ThreadLocal object has been disposed.'. at System.Threading.ThreadLocal`1.GetValueSlow() at Tensorflow.BaseSession.DisposeUnmanagedResources(IntPtr handle) at Tensorflow.DisposableObject.Dispose(Boolean disposing) at Tensorflow.DisposableObject.Finalize() ----- end Fri 04/24/2026 23:52:39.67 ----- exit code 1 ---------------------------------------------------------- Compress-Archive : The path 'C:\h\w\A9B70968\w\A10408C3\e\\TestOutput' either does not exist or is not a valid file system path. At line:1 char:1 + Compress-Archive C:\h\w\A9B70968\w\A10408C3\e\\TestOutput C:\h\w\A9B7 ... + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + CategoryInfo : InvalidArgument: (C:\h\w\A9B70968\w\A10408C3\e\\TestOutput:String) [Compress-Archive], I nvalidOperationException + FullyQualifiedErrorId : ArchiveCmdletPathNotFound,Compress-Archive ( was unexpected at this time. ['Microsoft.ML.TensorFlow.Tests' END OF WORK ITEM LOG: Command exited with 255]