Console log: 'Microsoft.ML.TensorFlow.Tests' from job 568f67c1-0f12-462f-8f68-d5799dabea0e workitem 4236d1b4-ad42-4007-b32a-77ba85fb7d29 (windows.10.amd64.open) executed on machine a00FAJD running Windows-2016Server-10.0.14393-SP0 C:\h\w\A56F098A\w\A4E50939\e>set ML_TEST_DATADIR=C:\h\w\A56F098A\p C:\h\w\A56F098A\w\A4E50939\e>set MICROSOFTML_RESOURCE_PATH=C:\h\w\A56F098A\w\A4E50939\e C:\h\w\A56F098A\w\A4E50939\e>set PATH=C:\h\w\A56F098A\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\A56F098A\w\A4E50939\e>call .\runTests.cmd ----- start Tue 09/22/2026 9:06:45.47 =============== To repro directly: ===================================================== pushd C:\h\w\A56F098A\w\A4E50939\e\ C:\h\w\A56F098A\p/xunit-runner/tools/net48/xunit.console.exe Microsoft.ML.TensorFlow.Tests.dll -notrait Category=SkipInCI -xml testResults.xml popd =========================================================================================================== C:\h\w\A56F098A\w\A4E50939\e>C:\h\w\A56F098A\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 2026-09-22 09:06:51.063773: 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`. 2026-09-22 09:06:51.113809: 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`. 2026-09-22 09:06:51.180544: 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`. 2026-09-22 09:06:51.230688: 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`. 2026-09-22 09:06:51.278363: 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`. 2026-09-22 09:06:51.325888: 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`. 2026-09-22 09:06:51.372907: 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`. 2026-09-22 09:06:51.420243: 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`. 2026-09-22 09:06:51.467563: 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`. 2026-09-22 09:06:51.515508: 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`. 2026-09-22 09:06:51.563216: 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`. 2026-09-22 09:06:51.610980: 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`. 2026-09-22 09:06:51.658538: 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`. 2026-09-22 09:06:51.706691: 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`. 2026-09-22 09:06:51.754771: 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`. 2026-09-22 09:06:51.802805: 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`. 2026-09-22 09:06:51.850492: 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`. 2026-09-22 09:06:51.897339: 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`. 2026-09-22 09:06:51.944218: 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`. 2026-09-22 09:06:51.992421: 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`. 2026-09-22 09:06:52.039067: 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`. 2026-09-22 09:06:52.086966: 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`. 2026-09-22 09:06:52.134865: 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`. 2026-09-22 09:06:52.182697: 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`. 2026-09-22 09:06:52.229816: 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`. 2026-09-22 09:06:52.277808: 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`. 2026-09-22 09:06:52.325570: 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`. 2026-09-22 09:06:52.375086: 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`. 2026-09-22 09:06:52.421726: 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`. 2026-09-22 09:06:52.468265: 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`. 2026-09-22 09:06:52.517251: 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`. 2026-09-22 09:06:52.569528: 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`. 2026-09-22 09:06:52.620051: 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`. 2026-09-22 09:06:52.668359: 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 2026-09-22 09:06:55.542824: 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`. 2026-09-22 09:06:55.568117: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_shape_test 2026-09-22 09:06:55.568796: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:06:55.568954: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_shape_test 2026-09-22 09:06:55.569799: 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. 2026-09-22 09:06:55.571392: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled 2026-09-22 09:06:55.572407: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 4047 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputShapeTest with memory usage 615,809,024.00 and max memory usage 616,648,704.00 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\A56F098A\t\apt0i2ki.fzd\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 1,727,377,408.00 and max memory usage 3,046,846,464.00 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: 1.318215, Learning Rate: 0.01 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.7777778, Cross-Entropy: 0.5552479 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 1, Accuracy: 0.8, Cross-Entropy: 0.6776899, Learning Rate: 0.0094 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.3472485 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.3432009, Learning Rate: 0.0094 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.2681946 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2487057, Learning Rate: 0.008836 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2292895 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.1938415, Learning Rate: 0.008836 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.2046144 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1606892, Learning Rate: 0.008305839 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1882083 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1369586, Learning Rate: 0.008305839 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1756848 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1204119, Learning Rate: 0.007807489 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1662615 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1073004, Learning Rate: 0.007807489 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1584502 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.09743974, Learning Rate: 0.00733904 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.152203 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.08915347, Learning Rate: 0.00733904 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1467892 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.08262721, Learning Rate: 0.006898697 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1422978 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.07692968, Learning Rate: 0.006898697 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1382968 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.07230134, Learning Rate: 0.006484775 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.134894 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.06815131, Learning Rate: 0.006484775 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1318055 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.06470474, Learning Rate: 0.006095689 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.129131 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.06155276, Learning Rate: 0.006095689 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1266704 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.05889145, Learning Rate: 0.005729948 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.12451 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.05642049, Learning Rate: 0.005729948 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1225017 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.05430733, Learning Rate: 0.005386151 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1207193 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.05232161, Learning Rate: 0.005386151 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.119049 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.05060612, Learning Rate: 0.005062982 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1175535 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.04897836, Learning Rate: 0.005062982 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.1161429 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.04756044, Learning Rate: 0.004759203 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.1148707 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.04620419, Learning Rate: 0.004759203 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.1136645 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.04501475, Learning Rate: 0.004473651 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.11257 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 26, Accuracy: 1, Cross-Entropy: 0.04386941, Learning Rate: 0.004473651 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 26, Accuracy: 1, Cross-Entropy: 0.1115277 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 27, Accuracy: 1, Cross-Entropy: 0.04285907, Learning Rate: 0.004205232 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 27, Accuracy: 1, Cross-Entropy: 0.1105769 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 28, Accuracy: 1, Cross-Entropy: 0.04188069, Learning Rate: 0.004205232 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 28, Accuracy: 1, Cross-Entropy: 0.1096682 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 29, Accuracy: 1, Cross-Entropy: 0.04101345, Learning Rate: 0.003952918 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 29, Accuracy: 1, Cross-Entropy: 0.1088356 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 30, Accuracy: 1, Cross-Entropy: 0.04016942, Learning Rate: 0.003952918 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 30, Accuracy: 1, Cross-Entropy: 0.1080373 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 31, Accuracy: 1, Cross-Entropy: 0.03941824, Learning Rate: 0.003715743 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 31, Accuracy: 1, Cross-Entropy: 0.107303 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 32, Accuracy: 1, Cross-Entropy: 0.0386841, Learning Rate: 0.003715743 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 32, Accuracy: 1, Cross-Entropy: 0.106597 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 33, Accuracy: 1, Cross-Entropy: 0.03802826, Learning Rate: 0.003492798 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 33, Accuracy: 1, Cross-Entropy: 0.1059455 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 34, Accuracy: 1, Cross-Entropy: 0.03738496, Learning Rate: 0.003492798 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 34, Accuracy: 1, Cross-Entropy: 0.1053177 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 35, Accuracy: 1, Cross-Entropy: 0.03680847, Learning Rate: 0.00328323 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 35, Accuracy: 1, Cross-Entropy: 0.1047365 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 36, Accuracy: 1, Cross-Entropy: 0.03624115, Learning Rate: 0.00328323 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 36, Accuracy: 1, Cross-Entropy: 0.1041754 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 37, Accuracy: 1, Cross-Entropy: 0.03573137, Learning Rate: 0.003086236 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 37, Accuracy: 1, Cross-Entropy: 0.1036545 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 38, Accuracy: 1, Cross-Entropy: 0.03522823, Learning Rate: 0.003086236 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 38, Accuracy: 1, Cross-Entropy: 0.1031507 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 39, Accuracy: 1, Cross-Entropy: 0.034775, Learning Rate: 0.002901062 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 39, Accuracy: 1, Cross-Entropy: 0.1026821 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 40, Accuracy: 1, Cross-Entropy: 0.03432653, Learning Rate: 0.002901062 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 40, Accuracy: 1, Cross-Entropy: 0.1022279 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 41, Accuracy: 1, Cross-Entropy: 0.0339217, Learning Rate: 0.002726999 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 41, Accuracy: 1, Cross-Entropy: 0.1018046 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 42, Accuracy: 1, Cross-Entropy: 0.0335202, Learning Rate: 0.002726999 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 42, Accuracy: 1, Cross-Entropy: 0.1013938 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 43, Accuracy: 1, Cross-Entropy: 0.03315704, Learning Rate: 0.002563379 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 43, Accuracy: 1, Cross-Entropy: 0.1010102 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 44, Accuracy: 1, Cross-Entropy: 0.03279616, Learning Rate: 0.002563379 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 44, Accuracy: 1, Cross-Entropy: 0.1006375 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 45, Accuracy: 1, Cross-Entropy: 0.03246918, Learning Rate: 0.002409576 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 45, Accuracy: 1, Cross-Entropy: 0.100289 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 46, Accuracy: 1, Cross-Entropy: 0.03214359, Learning Rate: 0.002409576 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 46, Accuracy: 1, Cross-Entropy: 0.09994977 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 47, Accuracy: 1, Cross-Entropy: 0.03184818, Learning Rate: 0.002265001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 47, Accuracy: 1, Cross-Entropy: 0.09963208 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 48, Accuracy: 1, Cross-Entropy: 0.03155355, Learning Rate: 0.002265001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 48, Accuracy: 1, Cross-Entropy: 0.09932271 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 49, Accuracy: 1, Cross-Entropy: 0.0312858, Learning Rate: 0.002129101 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 49, Accuracy: 1, Cross-Entropy: 0.09903249 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 1,640,136,704.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifar Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifar with memory usage 1,648,226,304.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransforCifarEndToEndTest2 Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransforCifarEndToEndTest2 with memory usage 1,824,501,760.00 and max memory usage 4,568,645,632.00 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 2026-09-22 09:10:35.808197: 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\A56F098A\t\ynocyfb3.bke\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 1,648,173,056.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTest with memory usage 1,663,926,272.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorflowPlaceholderShapeInferenceTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorflowPlaceholderShapeInferenceTest with memory usage 1,663,979,520.00 and max memory usage 4,568,645,632.00 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: 0.5568673 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.3447151 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.2656599 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2268916 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.2024046 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1861718 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1737951 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1644915 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1567819 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1506187 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1452785 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1408494 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1369043 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1335497 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1305052 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.1278692 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1254442 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1233153 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1213365 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1195804 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1179349 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1164617 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.1150724 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 1,694,097,408.00 and max memory usage 4,568,645,632.00 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: 0.548546 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 1, Cross-Entropy: 0.3897775 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.32922 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.2921435 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.2674141 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.2490173 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.2351378 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.2238882 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.2148585 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.2071714 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.2007641 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1951327 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1903187 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1859927 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1822272 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.1787879 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1757539 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1729479 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1704471 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1681114 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1660127 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1640373 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 1,818,034,176.00 and max memory usage 4,568,645,632.00 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: 0.6666667, Cross-Entropy: 0.6503479 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.4031934 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 0.8888889, Cross-Entropy: 0.3145048 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 0.8888889, Cross-Entropy: 0.274222 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 0.8888889, Cross-Entropy: 0.2485492 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.2317208 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.2186801 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.2088324 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.2005801 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1939214 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1881284 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.183266 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.1789429 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.1752153 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.1718535 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.1688967 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.1662029 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.1637969 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.1615883 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.1595915 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.1577475 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.1560637 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.1545014 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.153063 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.1517233 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.1504811 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 1,718,349,824.00 and max memory usage 4,568,645,632.00 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: 0.679096 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.5322503 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 0.8888889, Cross-Entropy: 0.4565899 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.412803 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.3835444 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.3626632 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.346603 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.3339365 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.3235098 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.3148355 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.3074099 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.3010249 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.2954184 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.2904887 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.2860824 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.282145 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.2785794 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.2753542 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.2724044 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.2697106 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.2672272 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.2649422 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.2628223 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.2608595 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 2,823,843,840.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowInputsOutputsSchemaTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowInputsOutputsSchemaTest with memory usage 2,837,209,088.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMatrixMultiplicationTest Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMatrixMultiplicationTest with memory usage 2,837,663,744.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTLRTrainingTest 2026-09-22 09:14:53.735485: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_lr_model 2026-09-22 09:14:53.736472: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:14:53.736656: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_lr_model 2026-09-22 09:14:53.738593: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:14:53.746606: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 11120 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTLRTrainingTest with memory usage 2,843,394,048.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTrainingTest 2026-09-22 09:14:54.319277: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_conv_model 2026-09-22 09:14:54.320724: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:14:54.320912: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_conv_model 2026-09-22 09:14:54.326188: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:14:54.349884: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 30604 microseconds. 2026-09-22 09:14:55.031717: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_conv_model 2026-09-22 09:14:55.032930: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:14:55.033124: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_conv_model 2026-09-22 09:14:55.037721: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:14:55.068104: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 36379 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvTrainingTest with memory usage 2,848,677,888.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSentimentClassificationTest Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInceptionTest [SKIP] Model files are not available yet 2026-09-22 09:14:55.778893: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: sentiment_model 2026-09-22 09:14:55.782663: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:14:55.782941: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: sentiment_model 2026-09-22 09:14:55.803931: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:14:55.860142: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 81240 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSentimentClassificationTest with memory usage 2,851,762,176.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarSavedModel 2026-09-22 09:14:55.956368: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:14:55.958080: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:14:55.958362: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:14:55.965471: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:14:55.990408: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 34036 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarSavedModel with memory usage 2,854,461,440.00 and max memory usage 4,568,645,632.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputOutputTypesTest 2026-09-22 09:14:56.033811: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_types_test 2026-09-22 09:14:56.034584: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:14:56.034772: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_types_test 2026-09-22 09:14:56.035408: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 1598 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformInputOutputTypesTest with memory usage 2,855,112,704.00 and max memory usage 4,568,645,632.00 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: 0.2972616 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 1, Accuracy: 1, Cross-Entropy: 0.1946169 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.1611045 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.143646 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.131917 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1242188 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1178429 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.1131184 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1089752 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1057276 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1027938 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1004087 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.0982125 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.0963801 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.09466979 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.09321477 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.09184301 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.09065845 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.08953287 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.08854929 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.08760895 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.08677918 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 1,905,442,816.00 and max memory usage 6,366,171,136.00 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: 0.2958105 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 1, Accuracy: 1, Cross-Entropy: 0.1940009 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 2, Accuracy: 1, Cross-Entropy: 0.1608089 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 3, Accuracy: 1, Cross-Entropy: 0.1434745 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 4, Accuracy: 1, Cross-Entropy: 0.1318672 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 5, Accuracy: 1, Cross-Entropy: 0.1242771 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 6, Accuracy: 1, Cross-Entropy: 0.1179729 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 7, Accuracy: 1, Cross-Entropy: 0.113311 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 8, Accuracy: 1, Cross-Entropy: 0.1092114 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 9, Accuracy: 1, Cross-Entropy: 0.1060044 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 10, Accuracy: 1, Cross-Entropy: 0.1031002 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 11, Accuracy: 1, Cross-Entropy: 0.1007439 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 12, Accuracy: 1, Cross-Entropy: 0.09856935 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 13, Accuracy: 1, Cross-Entropy: 0.09675869 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 14, Accuracy: 1, Cross-Entropy: 0.09506503 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 15, Accuracy: 1, Cross-Entropy: 0.09362717 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 16, Accuracy: 1, Cross-Entropy: 0.09226878 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 17, Accuracy: 1, Cross-Entropy: 0.09109807 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 18, Accuracy: 1, Cross-Entropy: 0.08998352 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 19, Accuracy: 1, Cross-Entropy: 0.0890114 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 20, Accuracy: 1, Cross-Entropy: 0.08808023 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 21, Accuracy: 1, Cross-Entropy: 0.08726023 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 22, Accuracy: 1, Cross-Entropy: 0.08647081 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 23, Accuracy: 1, Cross-Entropy: 0.08577009 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 24, Accuracy: 1, Cross-Entropy: 0.08509277 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 25, Accuracy: 1, Cross-Entropy: 0.08448756 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 26, Accuracy: 1, Cross-Entropy: 0.08390044 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 27, Accuracy: 1, Cross-Entropy: 0.08337293 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 28, Accuracy: 1, Cross-Entropy: 0.08285969 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 29, Accuracy: 1, Cross-Entropy: 0.08239631 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 30, Accuracy: 1, Cross-Entropy: 0.08194432 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 31, Accuracy: 1, Cross-Entropy: 0.08153457 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 32, Accuracy: 1, Cross-Entropy: 0.08113404 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 33, Accuracy: 1, Cross-Entropy: 0.08076953 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 34, Accuracy: 1, Cross-Entropy: 0.08041261 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 35, Accuracy: 1, Cross-Entropy: 0.0800868 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 36, Accuracy: 1, Cross-Entropy: 0.07976719 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 37, Accuracy: 1, Cross-Entropy: 0.07947467 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 38, Accuracy: 1, Cross-Entropy: 0.07918718 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 39, Accuracy: 1, Cross-Entropy: 0.07892348 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 40, Accuracy: 1, Cross-Entropy: 0.078664 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 41, Accuracy: 1, Cross-Entropy: 0.07842545 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 42, Accuracy: 1, Cross-Entropy: 0.07819044 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 43, Accuracy: 1, Cross-Entropy: 0.07797386 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 44, Accuracy: 1, Cross-Entropy: 0.07776034 Phase: Training, Dataset used: Validation, Batch Processed Count: 2, Epoch: 45, Accuracy: 1, Cross-Entropy: 0.0775633 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 3,168,559,104.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowStringTest 2026-09-22 09:17:05.338737: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_string_test 2026-09-22 09:17:05.339520: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.339809: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_string_test 2026-09-22 09:17:05.340706: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 1971 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowStringTest with memory usage 3,168,792,576.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSaveAndLoadSavedModel 2026-09-22 09:17:05.379045: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:05.380792: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.381075: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:05.388084: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:05.414549: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 35500 microseconds. 2026-09-22 09:17:05.681845: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\h\w\A56F098A\t\TensorFlowTransformer_5198f601-a108-457b-9d16-667e256bf3af 2026-09-22 09:17:05.683180: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.683366: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: C:\h\w\A56F098A\t\TensorFlowTransformer_5198f601-a108-457b-9d16-667e256bf3af 2026-09-22 09:17:05.688188: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:05.706546: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 24697 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowSaveAndLoadSavedModel with memory usage 3,176,488,960.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowGettingSchemaMultipleTimes 2026-09-22 09:17:05.783355: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:05.785002: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.785270: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:05.791705: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:05.816663: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 33302 microseconds. 2026-09-22 09:17:05.836210: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:05.838389: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.838744: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:05.845867: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:05.875818: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 39593 microseconds. 2026-09-22 09:17:05.901804: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:05.903577: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.903858: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:05.910436: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:05.934358: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 32551 microseconds. 2026-09-22 09:17:05.945910: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:05.947382: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.947568: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:05.952249: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:05.973098: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 27179 microseconds. 2026-09-22 09:17:05.984917: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:05.986183: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:05.986369: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:05.990910: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:06.009499: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 24576 microseconds. 2026-09-22 09:17:06.029529: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:06.031302: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:06.031591: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:06.036603: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:06.054575: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 25038 microseconds. 2026-09-22 09:17:06.074994: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:06.077117: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:06.077513: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:06.084824: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:06.113882: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 38882 microseconds. 2026-09-22 09:17:06.130298: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:06.131973: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:06.132243: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:06.137433: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:06.156027: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 25725 microseconds. 2026-09-22 09:17:06.171537: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:06.172791: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:06.172976: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:06.177586: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:06.195865: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 24323 microseconds. 2026-09-22 09:17:06.207368: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:17:06.208699: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:17:06.208943: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:17:06.213714: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:17:06.232431: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 25054 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowGettingSchemaMultipleTimes with memory usage 3,165,511,680.00 and max memory usage 6,366,171,136.00 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: 0.54, Cross-Entropy: 1.345228, Learning Rate: 0.00505 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 0, Accuracy: 0.8888889, Cross-Entropy: 0.541873 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 1, Accuracy: 0.8, Cross-Entropy: 0.7046759, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 1, Accuracy: 0.8888889, Cross-Entropy: 0.4766648 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 2, Accuracy: 0.9399999, Cross-Entropy: 0.5711825, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 2, Accuracy: 0.8888889, Cross-Entropy: 0.4752045 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 3, Accuracy: 0.9399999, Cross-Entropy: 0.5681674, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 3, Accuracy: 0.8888889, Cross-Entropy: 0.4737447 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 4, Accuracy: 0.9399999, Cross-Entropy: 0.5651869, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 4, Accuracy: 0.8888889, Cross-Entropy: 0.4722855 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 5, Accuracy: 0.9399999, Cross-Entropy: 0.5622399, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 5, Accuracy: 0.8888889, Cross-Entropy: 0.4708274 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 6, Accuracy: 0.9399999, Cross-Entropy: 0.5593253, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 6, Accuracy: 0.8888889, Cross-Entropy: 0.4693706 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 7, Accuracy: 0.9399999, Cross-Entropy: 0.556443, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 7, Accuracy: 0.8888889, Cross-Entropy: 0.4679155 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 8, Accuracy: 0.9399999, Cross-Entropy: 0.5535917, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 8, Accuracy: 0.8888889, Cross-Entropy: 0.4664621 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 9, Accuracy: 0.9399999, Cross-Entropy: 0.5507709, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 9, Accuracy: 0.8888889, Cross-Entropy: 0.465011 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 10, Accuracy: 0.9399999, Cross-Entropy: 0.5479797, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 10, Accuracy: 0.8888889, Cross-Entropy: 0.4635622 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 11, Accuracy: 0.9399999, Cross-Entropy: 0.5452176, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 11, Accuracy: 0.8888889, Cross-Entropy: 0.4621161 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 12, Accuracy: 0.9399999, Cross-Entropy: 0.5424842, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 12, Accuracy: 0.8888889, Cross-Entropy: 0.460673 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 13, Accuracy: 0.9399999, Cross-Entropy: 0.5397784, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 13, Accuracy: 0.8888889, Cross-Entropy: 0.4592329 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 14, Accuracy: 0.9399999, Cross-Entropy: 0.5371, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 14, Accuracy: 0.8888889, Cross-Entropy: 0.4577963 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 15, Accuracy: 0.9399999, Cross-Entropy: 0.5344482, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 15, Accuracy: 0.8888889, Cross-Entropy: 0.4563633 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 16, Accuracy: 0.9399999, Cross-Entropy: 0.5318227, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 16, Accuracy: 0.8888889, Cross-Entropy: 0.454934 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 17, Accuracy: 0.9399999, Cross-Entropy: 0.5292228, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 17, Accuracy: 0.8888889, Cross-Entropy: 0.4535086 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 18, Accuracy: 0.9399999, Cross-Entropy: 0.5266482, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 18, Accuracy: 0.8888889, Cross-Entropy: 0.4520876 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 19, Accuracy: 0.9399999, Cross-Entropy: 0.5240983, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 19, Accuracy: 0.8888889, Cross-Entropy: 0.4506708 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 20, Accuracy: 0.9399999, Cross-Entropy: 0.5215726, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 20, Accuracy: 0.8888889, Cross-Entropy: 0.4492585 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 21, Accuracy: 0.9399999, Cross-Entropy: 0.5190707, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 21, Accuracy: 0.8888889, Cross-Entropy: 0.4478511 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 22, Accuracy: 0.96, Cross-Entropy: 0.5165922, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 22, Accuracy: 0.8888889, Cross-Entropy: 0.4464484 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 23, Accuracy: 0.96, Cross-Entropy: 0.5141368, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 23, Accuracy: 0.8888889, Cross-Entropy: 0.4450507 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 24, Accuracy: 0.96, Cross-Entropy: 0.5117039, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 24, Accuracy: 0.8888889, Cross-Entropy: 0.4436582 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 25, Accuracy: 0.96, Cross-Entropy: 0.5092932, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 25, Accuracy: 0.8888889, Cross-Entropy: 0.442271 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 26, Accuracy: 0.96, Cross-Entropy: 0.5069043, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 26, Accuracy: 0.8888889, Cross-Entropy: 0.4408891 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 27, Accuracy: 0.96, Cross-Entropy: 0.504537, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 27, Accuracy: 0.8888889, Cross-Entropy: 0.4395128 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 28, Accuracy: 0.96, Cross-Entropy: 0.5021908, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 28, Accuracy: 0.8888889, Cross-Entropy: 0.4381421 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 29, Accuracy: 0.96, Cross-Entropy: 0.4998653, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 29, Accuracy: 0.8888889, Cross-Entropy: 0.4367771 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 30, Accuracy: 0.96, Cross-Entropy: 0.4975603, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 30, Accuracy: 0.8888889, Cross-Entropy: 0.435418 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 31, Accuracy: 0.96, Cross-Entropy: 0.4952754, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 31, Accuracy: 0.8888889, Cross-Entropy: 0.4340648 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 32, Accuracy: 0.96, Cross-Entropy: 0.4930104, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 32, Accuracy: 0.8888889, Cross-Entropy: 0.4327177 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 33, Accuracy: 0.96, Cross-Entropy: 0.4907651, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 33, Accuracy: 0.8888889, Cross-Entropy: 0.4313766 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 34, Accuracy: 0.96, Cross-Entropy: 0.4885389, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 34, Accuracy: 0.8888889, Cross-Entropy: 0.4300417 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 35, Accuracy: 0.96, Cross-Entropy: 0.4863316, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 35, Accuracy: 0.8888889, Cross-Entropy: 0.428713 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 36, Accuracy: 0.96, Cross-Entropy: 0.4841433, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 36, Accuracy: 0.8888889, Cross-Entropy: 0.4273906 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 37, Accuracy: 0.96, Cross-Entropy: 0.4819732, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 37, Accuracy: 0.8888889, Cross-Entropy: 0.4260746 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 38, Accuracy: 0.96, Cross-Entropy: 0.4798212, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 38, Accuracy: 0.8888889, Cross-Entropy: 0.424765 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 39, Accuracy: 0.96, Cross-Entropy: 0.4776873, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 39, Accuracy: 0.8888889, Cross-Entropy: 0.4234619 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 40, Accuracy: 0.96, Cross-Entropy: 0.475571, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 40, Accuracy: 0.8888889, Cross-Entropy: 0.4221653 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 41, Accuracy: 0.96, Cross-Entropy: 0.4734721, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 41, Accuracy: 0.8888889, Cross-Entropy: 0.4208752 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 42, Accuracy: 0.96, Cross-Entropy: 0.4713904, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 42, Accuracy: 0.8888889, Cross-Entropy: 0.4195918 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 43, Accuracy: 0.96, Cross-Entropy: 0.4693257, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 43, Accuracy: 0.8888889, Cross-Entropy: 0.4183149 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 44, Accuracy: 0.96, Cross-Entropy: 0.4672778, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 44, Accuracy: 0.8888889, Cross-Entropy: 0.4170447 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 45, Accuracy: 0.96, Cross-Entropy: 0.4652463, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 45, Accuracy: 0.8888889, Cross-Entropy: 0.4157811 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 46, Accuracy: 0.96, Cross-Entropy: 0.4632311, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 46, Accuracy: 0.8888889, Cross-Entropy: 0.4145243 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 47, Accuracy: 0.96, Cross-Entropy: 0.461232, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 47, Accuracy: 0.8888889, Cross-Entropy: 0.4132742 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 48, Accuracy: 0.96, Cross-Entropy: 0.4592489, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 48, Accuracy: 0.8888889, Cross-Entropy: 0.4120308 Phase: Training, Dataset used: Train, Batch Processed Count: 5, Epoch: 49, Accuracy: 0.96, Cross-Entropy: 0.4572813, Learning Rate: 0.0001 Phase: Training, Dataset used: Validation, Batch Processed Count: 1, Epoch: 49, Accuracy: 0.8888889, Cross-Entropy: 0.4107942 Saver not created because there are no variables in the graph to restore Restoring parameters from C:\h\w\A56F098A\t\j1f1cvid.tbc\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 2,104,655,872.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowPrimitiveInputTest 2026-09-22 09:18:20.062009: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: model_primitive_input_test 2026-09-22 09:18:20.062892: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:18:20.063200: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: model_primitive_input_test 2026-09-22 09:18:20.064322: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 2315 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowPrimitiveInputTest with memory usage 2,104,725,504.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvSavedModelTest 2026-09-22 09:18:20.079244: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: mnist_model 2026-09-22 09:18:20.080266: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:18:20.080459: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: mnist_model 2026-09-22 09:18:20.083632: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:18:20.115016: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: mnist_model 2026-09-22 09:18:20.118574: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 39327 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformMNISTConvSavedModelTest with memory usage 2,108,571,648.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarInvalidShape Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarInvalidShape with memory usage 2,108,628,992.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarCrossValidationWithInMemoryImages 2026-09-22 09:18:21.156671: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: cifar_saved_model 2026-09-22 09:18:21.157921: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve } 2026-09-22 09:18:21.158105: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: cifar_saved_model 2026-09-22 09:18:21.162749: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle. 2026-09-22 09:18:21.185082: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 28404 microseconds. Finished test: Microsoft.ML.TensorFlow.Scenarios.TensorFlowScenariosTests.TensorFlowTransformCifarCrossValidationWithInMemoryImages with memory usage 2,113,806,336.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlow Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlow with memory usage 2,117,623,808.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlowWithSchema Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestTensorFlowWithSchema with memory usage 2,111,180,800.00 and max memory usage 6,366,171,136.00 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 2,117,689,344.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestLoadMultipleModel Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestLoadMultipleModel with memory usage 2,119,069,696.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestOldSavingAndLoading Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestOldSavingAndLoading with memory usage 2,120,888,320.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestSimpleCase Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TestSimpleCase with memory usage 2,117,660,672.00 and max memory usage 6,366,171,136.00 Starting test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TreatOutputAsBatched Finished test: Microsoft.ML.Tests.TensorFlowEstimatorTests.TreatOutputAsBatched with memory usage 2,120,392,704.00 and max memory usage 6,366,171,136.00 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 Tue 09/22/2026 9:18:23.96 ----- exit code 1 ---------------------------------------------------------- Compress-Archive : The path 'C:\h\w\A56F098A\w\A4E50939\e\\TestOutput' either does not exist or is not a valid file system path. At line:1 char:1 + Compress-Archive C:\h\w\A56F098A\w\A4E50939\e\\TestOutput C:\h\w\A56F ... + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + CategoryInfo : InvalidArgument: (C:\h\w\A56F098A\w\A4E50939\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]