analyze the following log for any calculation problems 
[2024-10-08 06:02:50.742] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer1' with 192 parameters.
[2024-10-08 06:02:50.773] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer2' with 4160 parameters.
[2024-10-08 06:02:50.773] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'OutputLayer' with 195 parameters.
[Scope: Trainer] [2024-10-08 06:02:50.807] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer1' with 192 parameters.
[Scope: Trainer] [2024-10-08 06:02:50.812] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer2' with 4160 parameters.
[Scope: Trainer] [2024-10-08 06:02:50.812] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'OutputLayer' with 195 parameters.
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.893] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0795661]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.897] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.21542077, 0.23155832, 0.55302095], Probabilities Sample: [0.29258174, 0.2973416, 0.4100767], Log Probabilities Sample: [-1.2290112, -1.2128737, -0.89141107], Target Sample: [0, 0, 1], Per-Sample Loss: [0.89141107, 1.1391363, 1.1171917, 1.1631736, 0.98942757, 1.0986123, 1.0986123, 1.040737, 1.0986123, 1.0988069, 1.030381, 1.188693], Loss Sum: [12.954794], Mean Loss: [1.0795661]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.899] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.899] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.21542077, 0.23155832, 0.55302095, Gradient Output Sample Values: [0, 0, 1], Gradient Sample Values: [0.02438181, 0.024778465, -0.049160272]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.919] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.920] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24895492, 0.76819676, -0.81324923, -1.3552126, 0.014571875], Gradients Sample Values: [-0.0021620835, 0.0027564976, -0.00034714723, 0.00042011775, 0.002605125], Scaled Gradients Sample Values: [-2.1620835E-05, 2.7564975E-05, -3.4714722E-06, 4.2011775E-06, 2.605125E-05]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.920] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.920] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.00012403233, 9.395629E-06, -1.4581877E-05, -2.273402E-05, 2.9945164E-05], Gradients Sample Values: [-0.012403233, -0.0009395629, 0.0014581877, 0.0022734022, -0.0029945164], Scaled Gradients Sample Values: [-0.00012403233, -9.395629E-06, 1.4581877E-05, 2.273402E-05, -2.9945164E-05]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13412663, 0.052999943, -0.11041397, -0.12644397, 0.13205789], Gradients Sample Values: [0.00063953246, 8.579092E-05, -0.0003204974, 0.0014699465, 0], Scaled Gradients Sample Values: [6.3953244E-06, 8.579092E-07, -3.204974E-06, 1.4699464E-05, 0]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00013997711, -3.8574435E-06, -1.335915E-05, -0.00010800499, -4.7503854E-06], Gradients Sample Values: [0.013997711, 0.00038574435, 0.0013359151, 0.0108004995, 0.00047503854], Scaled Gradients Sample Values: [0.00013997711, 3.8574435E-06, 1.335915E-05, 0.00010800499, 4.7503854E-06]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.39863965, -0.1396058, 0.18611749, 0.015442743, 0.0015371868], Gradients Sample Values: [0.008353917, -0.000991123, -0.007362794, 0.007935213, -0.0032017129], Scaled Gradients Sample Values: [8.353917E-05, -9.91123E-06, -7.362794E-05, 7.9352125E-05, -3.2017128E-05]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.921] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0003535316, 0.00010598486, 0.00024754676, 0, 0], Gradients Sample Values: [0.03535316, -0.010598486, -0.024754677, 0, 0], Scaled Gradients Sample Values: [0.0003535316, -0.00010598486, -0.00024754676]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-08 06:02:50.930] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 1/1 completed in 0.10s. Loss: 1.0795661
[Scope: Trainer, Epoch: 1, Evaluator] [2024-10-08 06:02:50.948] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 1, Evaluator] [2024-10-08 06:02:50.948] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.952] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0770913]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.952] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33313307, 0.3333765, 0.33349043], Probabilities Sample: [0.3332666, 0.3333477, 0.33338568], Log Probabilities Sample: [-1.0988126, -1.0985692, -1.0984553], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0988126, 1.0985692, 1.0338922], Loss Sum: [3.231274], Mean Loss: [1.0770913]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.952] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0770913], Total Loss: [1.0770913]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.952] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33313307, 0.3333765, 0.33349043, 0.33313307, 0.3333765]
[Scope: Trainer, Epoch: 1, Evaluator] [2024-10-08 06:02:50.952] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0770913 
[Scope: Trainer, Epoch: 1] [2024-10-08 06:02:50.952] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1/100, Training Loss: 1.0795661, Validation Loss: 1.0770913
[Scope: Trainer, Epoch: 1, Use Early Stopping] [2024-10-08 06:02:50.955] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_1.ckpt' with Validation Loss: 1.0770913
[Scope: Trainer, Epoch: 1] [2024-10-08 06:02:50.955] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_1.ckpt' with Validation Loss: 1.0770913
[Scope: Trainer, Epoch: 1, Metrics] [2024-10-08 06:02:50.976] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 2 Final Metrics, Total Batches: 1, Average Error: 1.0796, Median Error: 1.0796, Mode Error: 1.0796, Min Error: 1.0796, Max Error: 1.0796, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0796, 75th Percentile: 1.0796, Interquartile Range: 0.0000, Sum Error: 1.0796, Epoch Duration: 00:00:00.1552993.
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.981] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0793518]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.982] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33313307, 0.3333765, 0.33349043], Probabilities Sample: [0.3332666, 0.3333477, 0.33338568], Log Probabilities Sample: [-1.0988126, -1.0985692, -1.0984553], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0988126, 1.163037, 0.8902079, 1.1388905, 1.1167089, 1.0984553, 1.0985692, 1.0308414, 1.0983131, 1.1883507, 0.9902008, 1.039833], Loss Sum: [12.952221], Mean Loss: [1.0793518]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.982] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.982] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.33313307, 0.3333765, 0.33349043, Gradient Output Sample Values: [1, 0, 0], Gradient Sample Values: [-0.055561114, 0.027778976, 0.02778214]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.988] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.988] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24897651, 0.7681692, -0.8132458, -1.3552167, 0.014545787], Gradients Sample Values: [-0.0021592719, 0.002750906, -0.00034385314, 0.00041681807, 0.002608776], Scaled Gradients Sample Values: [-2.1592718E-05, 2.7509059E-05, -3.4385314E-06, 4.1681806E-06, 2.608776E-05]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.988] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.988] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.00024781525, 1.8957086E-05, -2.9108392E-05, -4.5475397E-05, 6.0068876E-05], Gradients Sample Values: [-0.012378291, -0.0009561458, 0.0014526516, 0.002274138, -0.0030123712], Scaled Gradients Sample Values: [-0.00012378291, -9.561458E-06, 1.4526516E-05, 2.2741378E-05, -3.0123712E-05]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.989] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.989] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13413301, 0.05299909, -0.11041077, -0.12645869, 0.13205789], Gradients Sample Values: [0.00063722534, 8.5320054E-05, -0.0003200021, 0.0014717907, 0], Scaled Gradients Sample Values: [6.372253E-06, 8.532005E-07, -3.2000207E-06, 1.4717907E-05, 0]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.989] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.989] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0002796721, -7.6835195E-06, -2.6777943E-05, -0.0002160457, -9.540776E-06], Gradients Sample Values: [0.013969498, 0.0003826076, 0.0013418793, 0.01080407, 0.00047903904], Scaled Gradients Sample Values: [0.00013969498, 3.826076E-06, 1.3418792E-05, 0.0001080407, 4.7903904E-06]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.989] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.989] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.3985564, -0.13959603, 0.18619093, 0.015363645, 0.0015691988], Gradients Sample Values: [0.008322438, -0.0009779753, -0.0073444634, 0.007909801, -0.0032011976], Scaled Gradients Sample Values: [8.322438E-05, -9.779753E-06, -7.344463E-05, 7.9098005E-05, -3.2011976E-05]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.990] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.990] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00070590794, 0.00021193764, 0.00049397035, 0, 0], Gradients Sample Values: [0.035237636, -0.01059528, -0.024642358, 0, 0], Scaled Gradients Sample Values: [0.00035237634, -0.00010595279, -0.00024642356]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-08 06:02:50.990] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 1/1 completed in 0.01s. Loss: 1.0793518
[Scope: Trainer, Epoch: 2, Evaluator] [2024-10-08 06:02:50.991] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 2, Evaluator] [2024-10-08 06:02:50.991] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.991] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0773829]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.991] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33293328, 0.33341965, 0.33364707], Probabilities Sample: [0.33319998, 0.33336207, 0.3334379], Log Probabilities Sample: [-1.0990125, -1.0985261, -1.0982987], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0990125, 1.0985261, 1.03461], Loss Sum: [3.2321486], Mean Loss: [1.0773829]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.991] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0773829], Total Loss: [1.0773829]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-08 06:02:50.992] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33293328, 0.33341965, 0.33364707, 0.33293328, 0.33341965]
[Scope: Trainer, Epoch: 2, Evaluator] [2024-10-08 06:02:50.992] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0773829 
[Scope: Trainer, Epoch: 2] [2024-10-08 06:02:50.992] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2/100, Training Loss: 1.0793518, Validation Loss: 1.0773829
[Scope: Trainer, Epoch: 2] [2024-10-08 06:02:50.992] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_2.ckpt' with Validation Loss: 1.0773829
[Scope: Trainer, Epoch: 2, Metrics] [2024-10-08 06:02:50.994] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 3 Final Metrics, Total Batches: 1, Average Error: 1.0794, Median Error: 1.0794, Mode Error: 1.0794, Min Error: 1.0794, Max Error: 1.0794, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0794, 75th Percentile: 1.0794, Interquartile Range: 0.0000, Sum Error: 1.0794, Epoch Duration: 00:00:00.0158458.
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:50.997] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0791382]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:50.997] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.32342222, 0.2706036, 0.4059742], Probabilities Sample: [0.32953197, 0.3125782, 0.3578899], Log Probabilities Sample: [-1.1100819, -1.1629006, -1.02753], Target Sample: [0, 1, 0], Per-Sample Loss: [1.1629006, 0.9909704, 1.0985261, 1.038934, 1.0990125, 1.0313, 1.0982987, 1.1386467, 1.0978211, 1.1162281, 1.1880087, 0.8890112], Loss Sum: [12.949658], Mean Loss: [1.0791382]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:50.997] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:50.997] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.32342222, 0.2706036, 0.4059742, Gradient Output Sample Values: [0, 1, 0], Gradient Sample Values: [0.027460998, -0.05728515, 0.029824158]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.004] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.004] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24899808, 0.76814175, -0.8132424, -1.3552209, 0.014519663], Gradients Sample Values: [-0.002156455, 0.0027453285, -0.00034056447, 0.00041349512, 0.002612419], Scaled Gradients Sample Values: [-2.156455E-05, 2.7453285E-05, -3.4056445E-06, 4.1349513E-06, 2.612419E-05]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.004] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.004] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.00037134925, 2.8683598E-05, -4.357962E-05, -6.822485E-05, 9.037072E-05], Gradients Sample Values: [-0.012353401, -0.0009726513, 0.0014471228, 0.002274945, -0.0030301842], Scaled Gradients Sample Values: [-0.000123534, -9.726513E-06, 1.4471228E-05, 2.274945E-05, -3.030184E-05]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13413936, 0.05299824, -0.110407576, -0.12647343, 0.13205789], Gradients Sample Values: [0.0006349383, 8.4852174E-05, -0.0003194985, 0.0014736323, 0], Scaled Gradients Sample Values: [6.349383E-06, 8.485217E-07, -3.1949849E-06, 1.4736323E-05, 0]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00041908646, -1.1478466E-05, -4.0256622E-05, -0.00032412205, -1.4371148E-05], Gradients Sample Values: [0.013941437, 0.00037949468, 0.0013478678, 0.010807637, 0.0004830372], Scaled Gradients Sample Values: [0.00013941436, 3.7949467E-06, 1.3478678E-05, 0.00010807637, 4.830372E-06]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.3984735, -0.13958637, 0.1862642, 0.0152848, 0.0016012064], Gradients Sample Values: [0.008291106, -0.00096491305, -0.0073261913, 0.007884456, -0.00320076], Scaled Gradients Sample Values: [8.291106E-05, -9.64913E-06, -7.326191E-05, 7.8844554E-05, -3.20076E-05]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0010571321, 0.00031786124, 0.00073927094, 0, 0], Gradients Sample Values: [0.035122424, -0.01059236, -0.024530062, 0, 0], Scaled Gradients Sample Values: [0.00035122424, -0.000105923595, -0.00024530062]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-08 06:02:51.005] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 1/1 completed in 0.01s. Loss: 1.0791382
[Scope: Trainer, Epoch: 3, Evaluator] [2024-10-08 06:02:51.006] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 3, Evaluator] [2024-10-08 06:02:51.006] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.007] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0776736]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.007] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33273393, 0.3334628, 0.3338033], Probabilities Sample: [0.33313358, 0.33337647, 0.33348998], Log Probabilities Sample: [-1.0992117, -1.0984828, -1.0981425], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0992117, 1.0984828, 1.0353261], Loss Sum: [3.2330208], Mean Loss: [1.0776736]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.007] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0776736], Total Loss: [1.0776736]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.007] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33273393, 0.3334628, 0.3338033, 0.33273393, 0.3334628]
[Scope: Trainer, Epoch: 3, Evaluator] [2024-10-08 06:02:51.007] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0776736 
[Scope: Trainer, Epoch: 3] [2024-10-08 06:02:51.007] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3/100, Training Loss: 1.0791382, Validation Loss: 1.0776736
[Scope: Trainer, Epoch: 3] [2024-10-08 06:02:51.007] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_3.ckpt' with Validation Loss: 1.0776736
[Scope: Trainer, Epoch: 3, Metrics] [2024-10-08 06:02:51.009] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 4 Final Metrics, Total Batches: 1, Average Error: 1.0791, Median Error: 1.0791, Mode Error: 1.0791, Min Error: 1.0791, Max Error: 1.0791, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0791, 75th Percentile: 1.0791, Interquartile Range: 0.0000, Sum Error: 1.0791, Epoch Duration: 00:00:00.0148152.
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.012] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0789256]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.012] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.3977525, 0.2849182, 0.31732938], Probabilities Sample: [0.35511056, 0.3172198, 0.32766968], Log Probabilities Sample: [-1.0353261, -1.1481603, -1.1157492], Target Sample: [0, 0, 1], Per-Sample Loss: [1.1157492, 1.0984828, 1.0992117, 1.0380405, 1.1627644, 1.031757, 0.99173594, 1.138405, 1.0981425, 0.8878207, 1.187667, 1.0973313], Loss Sum: [12.947107], Mean Loss: [1.0789256]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.012] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.012] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.3977525, 0.2849182, 0.31732938, Gradient Output Sample Values: [0, 0, 1], Gradient Sample Values: [0.029592546, 0.026434982, -0.056027528]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.018] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.018] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24901961, 0.7681143, -0.813239, -1.355225, 0.014493502], Gradients Sample Values: [-0.0021536318, 0.0027397648, -0.00033728097, 0.00041013956, 0.0026160562], Scaled Gradients Sample Values: [-2.1536318E-05, 2.7397648E-05, -3.3728097E-06, 4.1013955E-06, 2.6160562E-05]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.018] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.018] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0004946348, 3.8574326E-05, -5.7995618E-05, -9.098319E-05, 0.000120850375], Gradients Sample Values: [-0.012328556, -0.000989073, 0.0014415997, 0.0022758334, -0.0030479655], Scaled Gradients Sample Values: [-0.00012328556, -9.890729E-06, 1.4415997E-05, 2.2758333E-05, -3.0479654E-05]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13414569, 0.052997395, -0.11040439, -0.12648818, 0.13205789], Gradients Sample Values: [0.0006326714, 8.43873E-05, -0.00031898677, 0.0014754711, 0], Scaled Gradients Sample Values: [6.326714E-06, 8.4387295E-07, -3.1898676E-06, 1.4754711E-05, 0]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0005582218, -1.5242524E-05, -5.3795426E-05, -0.00043223402, -1.9241497E-05], Gradients Sample Values: [0.01391353, 0.0003764058, 0.0013538804, 0.010811197, 0.0004870349], Scaled Gradients Sample Values: [0.0001391353, 3.764058E-06, 1.3538804E-05, 0.00010811196, 4.870349E-06]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.3983909, -0.13957685, 0.18633728, 0.015206208, 0.0016332103], Gradients Sample Values: [0.008259914, -0.00095193763, -0.007307976, 0.007859184, -0.0032004006], Scaled Gradients Sample Values: [8.259914E-05, -9.519376E-06, -7.307976E-05, 7.8591846E-05, -3.2004005E-05]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.019] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0014072075, 0.0004237586, 0.0009834489, 0, 0], Gradients Sample Values: [0.035007536, -0.010589737, -0.024417795, 0, 0], Scaled Gradients Sample Values: [0.00035007537, -0.00010589737, -0.00024417794]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-08 06:02:51.020] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 4, Batch 1/1 completed in 0.01s. Loss: 1.0789256
[Scope: Trainer, Epoch: 4, Evaluator] [2024-10-08 06:02:51.020] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 4, Evaluator] [2024-10-08 06:02:51.020] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.021] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0779637]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.021] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.332535, 0.33350593, 0.33395904], Probabilities Sample: [0.33306727, 0.3333908, 0.3335419], Log Probabilities Sample: [-1.0994108, -1.0984399, -1.0979868], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0994108, 1.0984399, 1.0360404], Loss Sum: [3.233891], Mean Loss: [1.0779637]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.021] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0779637], Total Loss: [1.0779637]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.021] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.332535, 0.33350593, 0.33395904, 0.332535, 0.33350593]
[Scope: Trainer, Epoch: 4, Evaluator] [2024-10-08 06:02:51.021] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0779637 
[Scope: Trainer, Epoch: 4] [2024-10-08 06:02:51.021] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 4/100, Training Loss: 1.0789256, Validation Loss: 1.0779637
[Scope: Trainer, Epoch: 4] [2024-10-08 06:02:51.021] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_4.ckpt' with Validation Loss: 1.0779637
[Scope: Trainer, Epoch: 4, Metrics] [2024-10-08 06:02:51.023] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 5 Final Metrics, Total Batches: 1, Average Error: 1.0789, Median Error: 1.0789, Mode Error: 1.0789, Min Error: 1.0789, Max Error: 1.0789, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0789, 75th Percentile: 1.0789, Interquartile Range: 0.0000, Sum Error: 1.0789, Epoch Duration: 00:00:00.0144359.
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.027] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0787143]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.027] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.40134853, 0.2621009, 0.3365506], Probabilities Sample: [0.35621804, 0.30991417, 0.3338678], Log Probabilities Sample: [-1.0322123, -1.1714599, -1.0970103], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0322123, 1.187326, 1.1152723, 1.0371522, 0.9924975, 1.0968431, 1.0994108, 1.1626285, 1.1381652, 0.8866366, 1.0984399, 1.0979868], Loss Sum: [12.9445715], Mean Loss: [1.0787143]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.027] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.027] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.40134853, 0.2621009, 0.3365506, Gradient Output Sample Values: [1, 0, 0], Gradient Sample Values: [-0.053648498, 0.02582618, 0.027822316]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.042] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.042] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24904111, 0.76808697, -0.81323564, -1.355229, 0.014467305], Gradients Sample Values: [-0.002150802, 0.0027342131, -0.00033400266, 0.00040676107, 0.0026196872], Scaled Gradients Sample Values: [-2.1508018E-05, 2.734213E-05, -3.3400265E-06, 4.0676105E-06, 2.6196873E-05]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.042] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.042] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.00061767234, 4.862841E-05, -7.2356444E-05, -0.00011375111, 0.00015150748], Gradients Sample Values: [-0.012303757, -0.0010054085, 0.0014360829, 0.0022767931, -0.003065711], Scaled Gradients Sample Values: [-0.00012303756, -1.0054085E-05, 1.4360829E-05, 2.276793E-05, -3.065711E-05]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.042] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.042] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.134152, 0.052996557, -0.110401206, -0.12650295, 0.13205789], Gradients Sample Values: [0.00063042494, 8.392542E-05, -0.00031846648, 0.0014773079, 0], Scaled Gradients Sample Values: [6.3042494E-06, 8.392542E-07, -3.1846648E-06, 1.4773078E-05, 0]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0006970796, -1.8975932E-05, -6.7394605E-05, -0.0005403816, -2.415179E-05], Gradients Sample Values: [0.013885778, 0.00037334085, 0.0013599182, 0.010814754, 0.00049102935], Scaled Gradients Sample Values: [0.00013885778, 3.7334084E-06, 1.3599181E-05, 0.00010814754, 4.9102932E-06]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.3983086, -0.13956746, 0.18641017, 0.015127868, 0.0016652115], Gradients Sample Values: [0.008228861, -0.00093904417, -0.007289817, 0.0078339875, -0.003200121], Scaled Gradients Sample Values: [8.228861E-05, -9.390442E-06, -7.289817E-05, 7.833987E-05, -3.2001208E-05]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0017561372, 0.00052963285, 0.0012265046, 0, 0], Gradients Sample Values: [0.034892976, -0.010587425, -0.02430556, 0, 0], Scaled Gradients Sample Values: [0.00034892975, -0.00010587425, -0.00024305559]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-08 06:02:51.043] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5, Batch 1/1 completed in 0.02s. Loss: 1.0787143
[Scope: Trainer, Epoch: 5, Evaluator] [2024-10-08 06:02:51.043] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 5, Evaluator] [2024-10-08 06:02:51.043] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.044] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0782529]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.044] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33233654, 0.33354908, 0.3341144], Probabilities Sample: [0.33300114, 0.33340517, 0.3335937], Log Probabilities Sample: [-1.0996094, -1.0983968, -1.0978315], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0996094, 1.0983968, 1.0367527], Loss Sum: [3.2347589], Mean Loss: [1.0782529]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.044] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0782529], Total Loss: [1.0782529]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.044] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33233654, 0.33354908, 0.3341144, 0.33233654, 0.33354908]
[Scope: Trainer, Epoch: 5, Evaluator] [2024-10-08 06:02:51.044] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0782529 
[Scope: Trainer, Epoch: 5] [2024-10-08 06:02:51.044] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5/100, Training Loss: 1.0787143, Validation Loss: 1.0782529
[Scope: Trainer, Epoch: 5] [2024-10-08 06:02:51.044] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_5.ckpt' with Validation Loss: 1.0782529
[Scope: Trainer, Epoch: 5, Metrics] [2024-10-08 06:02:51.047] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 6 Final Metrics, Total Batches: 1, Average Error: 1.0787, Median Error: 1.0787, Mode Error: 1.0787, Min Error: 1.0787, Max Error: 1.0787, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0787, 75th Percentile: 1.0787, Interquartile Range: 0.0000, Sum Error: 1.0787, Epoch Duration: 00:00:00.0236636.
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.050] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.078504]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.050] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.3941628, 0.2469076, 0.3589296], Probabilities Sample: [0.35355002, 0.30513972, 0.3413102], Log Probabilities Sample: [-1.0397303, -1.1869855, -1.0749636], Target Sample: [0, 1, 0], Per-Sample Loss: [1.1869855, 1.1379277, 1.0362692, 1.0326658, 1.0983968, 0.993255, 1.0978315, 1.1147975, 1.1624928, 1.096357, 1.0996094, 0.8854587], Loss Sum: [12.942047], Mean Loss: [1.078504]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.050] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.050] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.3941628, 0.2469076, 0.3589296, Gradient Output Sample Values: [0, 1, 0], Gradient Sample Values: [0.029462501, -0.057905022, 0.028442517]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.056] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.056] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24906258, 0.7680597, -0.81323236, -1.3552331, 0.014441071], Gradients Sample Values: [-0.0021479675, 0.002728676, -0.0003307294, 0.00040335738, 0.002623313], Scaled Gradients Sample Values: [-2.1479675E-05, 2.7286758E-05, -3.307294E-06, 4.0335735E-06, 2.623313E-05]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.056] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.056] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0007404624, 5.8845035E-05, -8.666217E-05, -0.00013652936, 0.00018234173], Gradients Sample Values: [-0.012279006, -0.0010216623, 0.0014305727, 0.0022778236, -0.0030834249], Scaled Gradients Sample Values: [-0.00012279005, -1.0216622E-05, 1.4305727E-05, 2.2778237E-05, -3.083425E-05]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13415828, 0.052995723, -0.110398024, -0.12651774, 0.13205789], Gradients Sample Values: [0.0006281979, 8.346653E-05, -0.00031793848, 0.0014791418, 0], Scaled Gradients Sample Values: [6.281979E-06, 8.3466534E-07, -3.1793847E-06, 1.4791417E-05, 0]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00083566137, -2.2678927E-05, -8.1054386E-05, -0.00064856466, -2.9102011E-05], Gradients Sample Values: [0.013858179, 0.0003702996, 0.001365978, 0.0108183045, 0.00049502216], Scaled Gradients Sample Values: [0.00013858179, 3.7029959E-06, 1.365978E-05, 0.00010818304, 4.9502214E-06]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.39822662, -0.1395582, 0.18648289, 0.01504978, 0.0016972106], Gradients Sample Values: [0.008197952, -0.00092623546, -0.0072717164, 0.0078088576, -0.0031999114], Scaled Gradients Sample Values: [8.197952E-05, -9.262354E-06, -7.2717165E-05, 7.8088575E-05, -3.1999112E-05]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0021039248, 0.0006354867, 0.0014684382, 0, 0], Gradients Sample Values: [0.034778744, -0.010585385, -0.024193361, 0, 0], Scaled Gradients Sample Values: [0.00034778743, -0.000105853855, -0.00024193361]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-08 06:02:51.057] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 6, Batch 1/1 completed in 0.01s. Loss: 1.078504
[Scope: Trainer, Epoch: 6, Evaluator] [2024-10-08 06:02:51.058] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 6, Evaluator] [2024-10-08 06:02:51.058] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.059] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0785414]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.059] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.3321385, 0.3335922, 0.33426932], Probabilities Sample: [0.33293515, 0.3334195, 0.33364534], Log Probabilities Sample: [-1.0998075, -1.0983539, -1.0976768], Target Sample: [1, 0, 0], Per-Sample Loss: [1.0998075, 1.0983539, 1.037463], Loss Sum: [3.2356243], Mean Loss: [1.0785414]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.059] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0785414], Total Loss: [1.0785414]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.059] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.3321385, 0.3335922, 0.33426932, 0.3321385, 0.3335922]
[Scope: Trainer, Epoch: 6, Evaluator] [2024-10-08 06:02:51.059] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0785414 
[Scope: Trainer, Epoch: 6] [2024-10-08 06:02:51.059] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 6/100, Training Loss: 1.078504, Validation Loss: 1.0785414
[Scope: Trainer, Epoch: 6] [2024-10-08 06:02:51.059] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_6.ckpt' with Validation Loss: 1.0785414
[Scope: Trainer, Epoch: 6, Metrics] [2024-10-08 06:02:51.061] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 7 Final Metrics, Total Batches: 1, Average Error: 1.0785, Median Error: 1.0785, Mode Error: 1.0785, Min Error: 1.0785, Max Error: 1.0785, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0785, 75th Percentile: 1.0785, Interquartile Range: 0.0000, Sum Error: 1.0785, Epoch Duration: 00:00:00.0143404.
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.064] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0782946]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.065] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.40047458, 0.26185077, 0.33767465], Probabilities Sample: [0.35591158, 0.30984077, 0.3342477], Log Probabilities Sample: [-1.033073, -1.1716968, -1.0958729], Target Sample: [0, 0, 1], Per-Sample Loss: [1.0958729, 1.1623573, 1.0983539, 1.0998075, 1.0353913, 1.0331175, 1.1866453, 1.0976768, 0.9940089, 0.884287, 1.1143243, 1.137692], Loss Sum: [12.939535], Mean Loss: [1.0782946]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.065] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.065] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.40047458, 0.26185077, 0.33767465, Gradient Output Sample Values: [0, 0, 1], Gradient Sample Values: [0.0296593, 0.025820063, -0.05547936]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24908404, 0.76803243, -0.8132291, -1.3552371, 0.014414802], Gradients Sample Values: [-0.0021451272, 0.0027231546, -0.00032746006, 0.0003999269, 0.0026269308], Scaled Gradients Sample Values: [-2.1451271E-05, 2.7231545E-05, -3.2746004E-06, 3.999269E-06, 2.6269308E-05]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.00086300547, 6.92234E-05, -0.00010091283, -0.00015931868, 0.0002133527], Gradients Sample Values: [-0.012254306, -0.0010378364, 0.0014250663, 0.0022789314, -0.0031010977], Scaled Gradients Sample Values: [-0.00012254306, -1.0378364E-05, 1.4250662E-05, 2.2789314E-05, -3.1010975E-05]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13416454, 0.052994892, -0.11039485, -0.12653255, 0.13205789], Gradients Sample Values: [0.0006259913, 8.301066E-05, -0.00031740236, 0.0014809734, 0], Scaled Gradients Sample Values: [6.259913E-06, 8.3010656E-07, -3.1740235E-06, 1.4809734E-05, 0]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.071] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0009739687, -2.635175E-05, -9.4774994E-05, -0.0007567832, -3.409214E-05], Gradients Sample Values: [0.013830736, 0.0003672823, 0.0013720609, 0.010821849, 0.0004990129], Scaled Gradients Sample Values: [0.00013830735, 3.672823E-06, 1.3720608E-05, 0.00010821849, 4.9901287E-06]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.072] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.072] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.39814496, -0.13954906, 0.18655543, 0.014971942, 0.0017292084], Gradients Sample Values: [0.008167188, -0.00091351056, -0.0072536767, 0.0077838097, -0.0031997836], Scaled Gradients Sample Values: [8.1671875E-05, -9.135106E-06, -7.2536764E-05, 7.783809E-05, -3.1997835E-05]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.072] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.072] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0024505733, 0.00074132317, 0.0017092503, 0, 0], Gradients Sample Values: [0.034664858, -0.010583647, -0.024081212, 0, 0], Scaled Gradients Sample Values: [0.00034664857, -0.000105836465, -0.0002408121]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-08 06:02:51.072] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 7, Batch 1/1 completed in 0.01s. Loss: 1.0782946
[Scope: Trainer, Epoch: 7, Evaluator] [2024-10-08 06:02:51.072] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 7, Evaluator] [2024-10-08 06:02:51.072] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.073] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0788292]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.073] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33194092, 0.33363533, 0.33442378], Probabilities Sample: [0.33286935, 0.33343384, 0.33369684], Log Probabilities Sample: [-1.1000051, -1.0983108, -1.0975224], Target Sample: [1, 0, 0], Per-Sample Loss: [1.1000051, 1.0983108, 1.0381713], Loss Sum: [3.2364874], Mean Loss: [1.0788292]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.073] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0788292], Total Loss: [1.0788292]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.073] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33194092, 0.33363533, 0.33442378, 0.33194092, 0.33363533]
[Scope: Trainer, Epoch: 7, Evaluator] [2024-10-08 06:02:51.073] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0788292 
[Scope: Trainer, Epoch: 7] [2024-10-08 06:02:51.073] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 7/100, Training Loss: 1.0782946, Validation Loss: 1.0788292
[Scope: Trainer, Epoch: 7] [2024-10-08 06:02:51.073] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_7.ckpt' with Validation Loss: 1.0788292
[Scope: Trainer, Epoch: 7, Metrics] [2024-10-08 06:02:51.076] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 8 Final Metrics, Total Batches: 1, Average Error: 1.0783, Median Error: 1.0783, Mode Error: 1.0783, Min Error: 1.0783, Max Error: 1.0783, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0783, 75th Percentile: 1.0783, Interquartile Range: 0.0000, Sum Error: 1.0783, Epoch Duration: 00:00:00.0145656.
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.079] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0780863]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.079] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.3212616, 0.27131253, 0.40742585], Probabilities Sample: [0.32881072, 0.31279036, 0.35839888], Log Probabilities Sample: [-1.112273, -1.162222, -1.0261087], Target Sample: [0, 1, 0], Per-Sample Loss: [1.162222, 1.0953907, 1.0983108, 1.0975224, 1.1863055, 0.9947586, 1.0335675, 1.0345188, 1.1000051, 1.1138532, 1.1374584, 0.8831219], Loss Sum: [12.937036], Mean Loss: [1.0780863]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.079] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.079] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.3212616, 0.27131253, 0.40742585, Gradient Output Sample Values: [0, 1, 0], Gradient Sample Values: [0.027400894, -0.05726747, 0.029866574]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.085] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.085] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24910547, 0.76800525, -0.81322587, -1.3552411, 0.014388497], Gradients Sample Values: [-0.0021422831, 0.0027176458, -0.00032419764, 0.00039647333, 0.0026305406], Scaled Gradients Sample Values: [-2.1422831E-05, 2.7176458E-05, -3.2419764E-06, 3.9647334E-06, 2.6305406E-05]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.085] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.085] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.000985302, 7.9762634E-05, -0.00011510853, -0.00018211974, 0.00024454005], Gradients Sample Values: [-0.012229655, -0.0010539233, 0.0014195696, 0.0022801058, -0.0031187336], Scaled Gradients Sample Values: [-0.00012229655, -1.0539233E-05, 1.4195695E-05, 2.2801058E-05, -3.1187337E-05]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13417079, 0.052994065, -0.110391684, -0.12654738, 0.13205789], Gradients Sample Values: [0.0006238043, 8.2557824E-05, -0.00031685864, 0.001482802, 0], Scaled Gradients Sample Values: [6.2380427E-06, 8.2557824E-07, -3.1685863E-06, 1.482802E-05, 0]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0011120031, -2.9994637E-05, -0.00010855665, -0.0008650371, -3.9122155E-05], Gradients Sample Values: [0.013803446, 0.0003642887, 0.0013781653, 0.010825388, 0.0005030015], Scaled Gradients Sample Values: [0.00013803445, 3.642887E-06, 1.3781653E-05, 0.00010825388, 5.030015E-06]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.3980636, -0.13954005, 0.18662779, 0.014894353, 0.0017612057], Gradients Sample Values: [0.008136561, -0.00090086507, -0.0072356965, 0.0077588325, -0.0031997284], Scaled Gradients Sample Values: [8.136561E-05, -9.0086505E-06, -7.235696E-05, 7.758832E-05, -3.1997282E-05]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.086] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0027960862, 0.0008471449, 0.0019489415, 0, 0], Gradients Sample Values: [0.0345513, -0.010582177, -0.023969123, 0, 0], Scaled Gradients Sample Values: [0.000345513, -0.00010582177, -0.00023969123]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-08 06:02:51.087] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 8, Batch 1/1 completed in 0.01s. Loss: 1.0780863
[Scope: Trainer, Epoch: 8, Evaluator] [2024-10-08 06:02:51.087] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 8, Evaluator] [2024-10-08 06:02:51.087] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.088] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0791162]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.088] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33174375, 0.33367842, 0.3345778], Probabilities Sample: [0.33280364, 0.33344814, 0.33374816], Log Probabilities Sample: [-1.1002027, -1.0982679, -1.0973686], Target Sample: [1, 0, 0], Per-Sample Loss: [1.1002027, 1.0982679, 1.0388778], Loss Sum: [3.2373486], Mean Loss: [1.0791162]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.088] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0791162], Total Loss: [1.0791162]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.088] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33174375, 0.33367842, 0.3345778, 0.33174375, 0.33367842]
[Scope: Trainer, Epoch: 8, Evaluator] [2024-10-08 06:02:51.088] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0791162 
[Scope: Trainer, Epoch: 8] [2024-10-08 06:02:51.088] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 8/100, Training Loss: 1.0780863, Validation Loss: 1.0791162
[Scope: Trainer, Epoch: 8] [2024-10-08 06:02:51.088] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_8.ckpt' with Validation Loss: 1.0791162
[Scope: Trainer, Epoch: 8, Metrics] [2024-10-08 06:02:51.091] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 9 Final Metrics, Total Batches: 1, Average Error: 1.0781, Median Error: 1.0781, Mode Error: 1.0781, Min Error: 1.0781, Max Error: 1.0781, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0781, 75th Percentile: 1.0781, Interquartile Range: 0.0000, Sum Error: 1.0781, Epoch Duration: 00:00:00.0147272.
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.094] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.077879]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.094] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.39408898, 0.28632838, 0.31958267], Probabilities Sample: [0.35385153, 0.31770295, 0.32844555], Log Probabilities Sample: [-1.0388778, -1.1466385, -1.1133842], Target Sample: [0, 0, 1], Per-Sample Loss: [1.1133842, 1.1002027, 1.1859661, 0.88196266, 1.0340161, 1.0336516, 1.1372267, 1.1620868, 0.99550456, 1.0949105, 1.0973686, 1.0982679], Loss Sum: [12.934547], Mean Loss: [1.077879]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.094] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.094] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.39408898, 0.28632838, 0.31958267, Gradient Output Sample Values: [0, 0, 1], Gradient Sample Values: [0.029487627, 0.026475245, -0.05596287]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.100] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.100] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24912687, 0.76797813, -0.81322265, -1.355245, 0.014362155], Gradients Sample Values: [-0.0021394351, 0.0027121538, -0.0003209405, 0.000392996, 0.0026341465], Scaled Gradients Sample Values: [-2.1394351E-05, 2.7121538E-05, -3.2094051E-06, 3.92996E-06, 2.6341464E-05]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.100] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.100] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0011073526, 9.0461974E-05, -0.00012924933, -0.00020493321, 0.00027590338], Gradients Sample Values: [-0.012205055, -0.0010699339, 0.0014140795, 0.0022813478, -0.0031363338], Scaled Gradients Sample Values: [-0.000122050544, -1.0699338E-05, 1.41407945E-05, 2.2813478E-05, -3.1363335E-05]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.134177, 0.052993245, -0.11038852, -0.12656222, 0.13205789], Gradients Sample Values: [0.00062163715, 8.210799E-05, -0.00031630712, 0.0014846282, 0], Scaled Gradients Sample Values: [6.2163713E-06, 8.210799E-07, -3.1630711E-06, 1.4846281E-05, 0]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0012497662, -3.3607823E-05, -0.00012239956, -0.00097332627, -4.419204E-05], Gradients Sample Values: [0.01377631, 0.00036131876, 0.0013842918, 0.010828922, 0.0005069885], Scaled Gradients Sample Values: [0.0001377631, 3.6131876E-06, 1.3842918E-05, 0.00010828921, 5.069885E-06]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.39798254, -0.13953117, 0.18669997, 0.014817014, 0.0017932032], Gradients Sample Values: [0.008106078, -0.00088830106, -0.007217774, 0.0077339346, -0.0031997445], Scaled Gradients Sample Values: [8.106078E-05, -8.88301E-06, -7.217774E-05, 7.733934E-05, -3.1997446E-05]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0031404672, 0.00095295487, 0.0021875123, 0, 0], Gradients Sample Values: [0.03443809, -0.010580993, -0.02385709, 0, 0], Scaled Gradients Sample Values: [0.00034438088, -0.00010580993, -0.0002385709]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-08 06:02:51.101] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 9, Batch 1/1 completed in 0.01s. Loss: 1.077879
[Scope: Trainer, Epoch: 9, Evaluator] [2024-10-08 06:02:51.102] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 9, Evaluator] [2024-10-08 06:02:51.102] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.102] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0794023]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.102] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33154702, 0.33372158, 0.33473137], Probabilities Sample: [0.33273813, 0.33346248, 0.33379936], Log Probabilities Sample: [-1.1003995, -1.0982249, -1.0972152], Target Sample: [1, 0, 0], Per-Sample Loss: [1.1003995, 1.0982249, 1.0395826], Loss Sum: [3.2382069], Mean Loss: [1.0794023]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.102] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0794023], Total Loss: [1.0794023]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.103] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33154702, 0.33372158, 0.33473137, 0.33154702, 0.33372158]
[Scope: Trainer, Epoch: 9, Evaluator] [2024-10-08 06:02:51.103] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0794023 
[Scope: Trainer, Epoch: 9] [2024-10-08 06:02:51.103] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 9/100, Training Loss: 1.077879, Validation Loss: 1.0794023
[Scope: Trainer, Epoch: 9] [2024-10-08 06:02:51.103] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_9.ckpt' with Validation Loss: 1.0794023
[Scope: Trainer, Epoch: 9, Metrics] [2024-10-08 06:02:51.106] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 10 Final Metrics, Total Batches: 1, Average Error: 1.0779, Median Error: 1.0779, Mode Error: 1.0779, Min Error: 1.0779, Max Error: 1.0779, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0779, 75th Percentile: 1.0779, Interquartile Range: 0.0000, Sum Error: 1.0779, Epoch Duration: 00:00:00.0149814.
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.109] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0776727]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.109] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.44468886, 0.14769869, 0.40761247], Probabilities Sample: [0.36945987, 0.27452767, 0.3560125], Log Probabilities Sample: [-0.9957132, -1.2927033, -1.0327895], Target Sample: [0, 0, 1], Per-Sample Loss: [1.0327895, 0.88080984, 1.0972152, 1.136997, 1.1619519, 1.1003995, 1.0344627, 0.9962465, 1.1856271, 1.0982249, 1.1129171, 1.0944322], Loss Sum: [12.932073], Mean Loss: [1.0776727]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.109] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.109] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.44468886, 0.14769869, 0.40761247, Gradient Output Sample Values: [0, 0, 1], Gradient Sample Values: [0.030788323, 0.022877306, -0.053665627]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.115] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.115] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24914823, 0.7679511, -0.8132195, -1.3552489, 0.014335778], Gradients Sample Values: [-0.0021365837, 0.0027066777, -0.00031768897, 0.00038949563, 0.0026377458], Scaled Gradients Sample Values: [-2.1365837E-05, 2.7066775E-05, -3.1768895E-06, 3.894956E-06, 2.6377458E-05]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.115] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.115] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0012291576, 0.000101320555, -0.00014333529, -0.00022775981, 0.0003074424], Gradients Sample Values: [-0.012180504, -0.0010858583, 0.0014085968, 0.00228266, -0.0031538997], Scaled Gradients Sample Values: [-0.00012180504, -1.0858583E-05, 1.4085967E-05, 2.28266E-05, -3.1538995E-05]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.1341832, 0.05299243, -0.11038536, -0.1265771, 0.13205789], Gradients Sample Values: [0.0006194893, 8.1661216E-05, -0.00031574816, 0.001486452, 0], Scaled Gradients Sample Values: [6.1948926E-06, 8.1661216E-07, -3.1574814E-06, 1.48645195E-05, 0]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0013872595, -3.719155E-05, -0.00013630395, -0.0010816507, -4.930178E-05], Gradients Sample Values: [0.013749325, 0.00035837246, 0.0013904389, 0.010832451, 0.0005109743], Scaled Gradients Sample Values: [0.00013749325, 3.5837245E-06, 1.3904389E-05, 0.00010832451, 5.109743E-06]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.39790177, -0.1395224, 0.18677197, 0.014739923, 0.0018252016], Gradients Sample Values: [0.0080757355, -0.0008758176, -0.007199918, 0.0077091125, -0.0031998355], Scaled Gradients Sample Values: [8.075735E-05, -8.758176E-06, -7.199918E-05, 7.709112E-05, -3.1998356E-05]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0034837194, 0.0010587558, 0.0024249637, 0, 0], Gradients Sample Values: [0.034325227, -0.010580093, -0.02374513, 0, 0], Scaled Gradients Sample Values: [0.00034325226, -0.00010580092, -0.0002374513]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-08 06:02:51.116] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 10, Batch 1/1 completed in 0.01s. Loss: 1.0776727
[Scope: Trainer, Epoch: 10, Evaluator] [2024-10-08 06:02:51.117] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 10, Evaluator] [2024-10-08 06:02:51.117] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.118] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0796877]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.118] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.33135077, 0.3337647, 0.33488452], Probabilities Sample: [0.33267277, 0.3334768, 0.33385044], Log Probabilities Sample: [-1.100596, -1.098182, -1.0970621], Target Sample: [1, 0, 0], Per-Sample Loss: [1.100596, 1.098182, 1.0402853], Loss Sum: [3.2390633], Mean Loss: [1.0796877]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.118] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0796877], Total Loss: [1.0796877]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.118] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.33135077, 0.3337647, 0.33488452, 0.33135077, 0.3337647]
[Scope: Trainer, Epoch: 10, Evaluator] [2024-10-08 06:02:51.118] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0796877 
[Scope: Trainer, Epoch: 10] [2024-10-08 06:02:51.118] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 10/100, Training Loss: 1.0776727, Validation Loss: 1.0796877
[Scope: Trainer, Epoch: 10] [2024-10-08 06:02:51.118] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_10.ckpt' with Validation Loss: 1.0796877
[Scope: Trainer, Epoch: 10, Metrics] [2024-10-08 06:02:51.121] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch: 11 Final Metrics, Total Batches: 1, Average Error: 1.0777, Median Error: 1.0777, Mode Error: 1.0777, Min Error: 1.0777, Max Error: 1.0777, Range: 0.0000, Standard Deviation: 0.0000, Variance: 0.0000, Mean Absolute Deviation: 0.0000, Skewness: N/A (Standard Deviation is zero), Kurtosis: N/A (Standard Deviation is zero), 25th Percentile: 1.0777, 75th Percentile: 1.0777, Interquartile Range: 0.0000, Sum Error: 1.0777, Epoch Duration: 00:00:00.0152515.
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.124] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Mean Loss: [1.0774676]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.124] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.4439411, 0.14759065, 0.40846825], Probabilities Sample: [0.36918408, 0.27449825, 0.3563176], Log Probabilities Sample: [-0.9964599, -1.2928103, -1.0319328], Target Sample: [0, 0, 1], Per-Sample Loss: [1.0319328, 1.0970621, 1.0349076, 1.1852885, 0.99698454, 1.1618171, 1.098182, 1.0939559, 1.1367692, 1.1124518, 0.8796633, 1.100596], Loss Sum: [12.929611], Mean Loss: [1.0774676]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.124] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [12, 3], Target Shape: [12, 3], Gradient Shape: [12, 3]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.124] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.4439411, 0.14759065, 0.40846825, Gradient Output Sample Values: [0, 0, 1], Gradient Sample Values: [0.03076534, 0.022874855, -0.053640198]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.130] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.130] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24916957, 0.7679241, -0.8132163, -1.3552527, 0.014309364], Gradients Sample Values: [-0.0021337294, 0.0027012164, -0.00031444273, 0.000385975, 0.0026413377], Scaled Gradients Sample Values: [-2.1337293E-05, 2.7012164E-05, -3.1444272E-06, 3.85975E-06, 2.6413376E-05]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.130] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.130] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0013507176, 0.00011233754, -0.00015736649, -0.0002506001, 0.00033915666], Gradients Sample Values: [-0.012156012, -0.001101699, 0.0014031208, 0.002284031, -0.0031714258], Scaled Gradients Sample Values: [-0.000121560115, -1.1016989E-05, 1.4031208E-05, 2.2840308E-05, -3.171426E-05]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.131] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.131] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.13418937, 0.052991617, -0.11038221, -0.12659198, 0.13205789], Gradients Sample Values: [0.0006173608, 8.121744E-05, -0.00031518174, 0.0014882734, 0], Scaled Gradients Sample Values: [6.173608E-06, 8.121744E-07, -3.1518173E-06, 1.4882734E-05, 0]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.131] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.131] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0015244844, -4.0746043E-05, -0.00015027, -0.0011900105, -5.4451324E-05], Gradients Sample Values: [0.01372249, 0.00035544962, 0.001396606, 0.010835975, 0.00051495456], Scaled Gradients Sample Values: [0.0001372249, 3.554496E-06, 1.396606E-05, 0.00010835975, 5.1495454E-06]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.131] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.131] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.3978213, -0.13951378, 0.1868438, 0.01466308, 0.0018572016], Gradients Sample Values: [0.00804553, -0.0008634138, -0.0071821157, 0.007684365, -0.0031999967], Scaled Gradients Sample Values: [8.04553E-05, -8.634138E-06, -7.182115E-05, 7.6843644E-05, -3.1999967E-05]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.132] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.132] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0038258464, 0.0011645503, 0.0026612962, 0, 0], Gradients Sample Values: [0.0342127, -0.010579449, -0.02363324, 0, 0], Scaled Gradients Sample Values: [0.000342127, -0.00010579449, -0.00023633239]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-08 06:02:51.132] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 11, Batch 1/1 completed in 0.01s. Loss: 1.0774676
[Scope: Trainer, Epoch: 11, Evaluator] [2024-10-08 06:02:51.132] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Trainer, Epoch: 11, Evaluator] [2024-10-08 06:02:51.132] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.133] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0799724]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.133] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.3311549, 0.33380786, 0.3350372], Probabilities Sample: [0.33260754, 0.33349112, 0.33390135], Log Probabilities Sample: [-1.100792, -1.098139, -1.0969096], Target Sample: [1, 0, 0], Per-Sample Loss: [1.100792, 1.098139, 1.0409862], Loss Sum: [3.2399173], Mean Loss: [1.0799724]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.133] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0799724], Total Loss: [1.0799724]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-08 06:02:51.133] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.3311549, 0.33380786, 0.3350372, 0.3311549, 0.33380786]
[Scope: Trainer, Epoch: 11, Evaluator] [2024-10-08 06:02:51.133] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0799724 
[Scope: Trainer, Epoch: 11] [2024-10-08 06:02:51.133] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 11/100, Training Loss: 1.0774676, Validation Loss: 1.0799724
[Scope: Trainer, Epoch: 11, Use Early Stopping] [2024-10-08 06:02:51.133] [Warning] HyperAI.Core.Trainings.Trainers.Trainer: Early stopping triggered.
[Scope: Evaluator] [2024-10-08 06:02:51.134] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [3, 2], Labels Shape: [3, 3]
[Scope: Evaluator] [2024-10-08 06:02:51.134] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, -0, -0, -0.37750837], Labels Sample: [1, 0, 0, 0, 1]
[Scope: Evaluator, Batch Index: 1] [2024-10-08 06:02:51.135] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [3, 3], Target Shape: [3, 3], Mean Loss: [1.0799724]
[Scope: Evaluator, Batch Index: 1] [2024-10-08 06:02:51.135] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details: Predictions Sample: [0.3311549, 0.33380786, 0.3350372], Probabilities Sample: [0.33260754, 0.33349112, 0.33390135], Log Probabilities Sample: [-1.100792, -1.098139, -1.0969096], Target Sample: [1, 0, 0], Per-Sample Loss: [1.100792, 1.098139, 1.0409862], Loss Sum: [3.2399173], Mean Loss: [1.0799724]
[Scope: Evaluator, Batch Index: 1] [2024-10-08 06:02:51.135] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [3], Batch Data Shape: [3, 2], Batch Labels Shape: [3, 3], Predictions Shape: [3, 3], Batch Loss: [1.0799724], Total Loss: [1.0799724]
[Scope: Evaluator, Batch Index: 1] [2024-10-08 06:02:51.135] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, -0, -0, -0.37750837], Batch Labels Sample: [1, 0, 0, 0, 1], Predictions Sample: [0.3311549, 0.33380786, 0.3350372, 0.3311549, 0.33380786]
[Scope: Evaluator] [2024-10-08 06:02:51.135] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.0799724