can you find wrong calcualtion in this log also tell me if there is anything wrong and tell me how to debug it
[2024-10-07 09:48:56.795] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer1' with 192 parameters.
[2024-10-07 09:48:56.818] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer2' with 4160 parameters.
[2024-10-07 09:48:56.818] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'OutputLayer' with 195 parameters.
[Scope: Trainer] [2024-10-07 09:48:56.849] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer1' with 192 parameters.
[Scope: Trainer] [2024-10-07 09:48:56.853] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'HiddenLayer2' with 4160 parameters.
[Scope: Trainer] [2024-10-07 09:48:56.853] [Information] HyperAI.Core.Layers.DenseLayer: Initialized DenseLayer 'OutputLayer' with 195 parameters.
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.935] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0997785]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.939] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.33333334, 0.33333334, 0.33333334, 0.37115353, 0.2515253], Probabilities Sample: [0.33333334, 0.33333334, 0.33333334, 0.34560975, 0.30664235], Log Probabilities Sample: [-1.0986123, -1.0986123, -1.0986123, -1.062445, -1.1820732], Target Sample: [1, 0, 0, 0, 1], Per-Sample Loss: [1.0986123, 1.1820732, 1.0942398, 1.0993853, 1.1159166] Loss Sum: [26.394684], Mean Loss: [1.0997785]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.941] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.941] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.33333334, 0.33333334, 0.33333334, 0.37115353, 0.2515253, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [-0.027777776, 0.013888889, 0.013888889, 0.014400407, -0.028889902]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.966] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.966] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.48924956, -0.6839153, -0.005007418, 0.49998617, 0.41312796], Gradients Sample Values: [0.0010663995, 0.001529276, -0.0012571453, 0.00318455, 0.0003994583], Scaled Gradients Sample Values: [0.00010663995, 0.0001529276, -0.00012571453, 0.00031845502, 3.9945833E-05]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.966] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.966] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-8.602003E-05, 0.00033580392, 0.00010207433, 1.1767959E-05, -0.00019301007], Gradients Sample Values: [0.0008602003, -0.0033580393, -0.0010207433, -0.00011767959, 0.0019301006], Scaled Gradients Sample Values: [8.602003E-05, -0.00033580392, -0.00010207433, -1.1767959E-05, 0.00019301007]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.967] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.967] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16294326, -0.10061999, -0.19899768, 0.044050276, -0.33589596], Gradients Sample Values: [-0.0028192892, 0, 0.00414118, 0.0007626184, 0.0030012275], Scaled Gradients Sample Values: [-0.00028192892, 0, 0.000414118, 7.626184E-05, 0.00030012275]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.967] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.967] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.000826853, -7.617468E-05, -0.00085042894, 0.00028329724, -0.0009891506], Gradients Sample Values: [-0.00826853, 0.0007617468, 0.008504289, -0.0028329722, 0.009891506], Scaled Gradients Sample Values: [-0.000826853, 7.617468E-05, 0.00085042894, -0.00028329724, 0.0009891506]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.967] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.967] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.23711674, 0.040902555, -0.1611083, 0.01943723, 0.13108872], Gradients Sample Values: [-0.013899924, -0.004405487, 0.01830541, -0.00082485494, 0.00033758173], Scaled Gradients Sample Values: [-0.0013899924, -0.0004405487, 0.001830541, -8.2485494E-05, 3.3758173E-05]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.968] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.968] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0030941383, 0.0024502075, -0.005544346, 0, 0], Gradients Sample Values: [-0.030941382, -0.024502074, 0.05544346, 0, 0], Scaled Gradients Sample Values: [-0.0030941383, -0.0024502075, 0.005544346]
[Scope: Trainer, Epoch: 1, Batch: 1] [2024-10-07 09:48:56.974] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 1/1 completed in 0.10s. Loss: 1.0997785
[Scope: Trainer, Epoch: 1, Evaluator] [2024-10-07 09:48:57.001] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 1, Evaluator] [2024-10-07 09:48:57.001] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.006] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.103485]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.006] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.33500123, 0.3345883, 0.3304105, 0.3426432, 0.32933688], Probabilities Sample: [0.33388907, 0.3337512, 0.3323598, 0.33644375, 0.3319966], Log Probabilities Sample: [-1.0969465, -1.0973594, -1.1015372, -1.0893244, -1.1026306], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1015372, 1.1039476, 1.0784006, 1.097549, 1.1394476] Loss Sum: [6.62091], Mean Loss: [1.103485]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.006] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.103485], Total Loss: [1.103485]
[Scope: Trainer, Epoch: 1, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.006] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.33500123, 0.3345883, 0.3304105, 0.3426432, 0.32933688]
[Scope: Trainer, Epoch: 1, Evaluator] [2024-10-07 09:48:57.006] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.103485 
[Scope: Trainer, Epoch: 1] [2024-10-07 09:48:57.006] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1/100, Training Loss: 1.0997785, Validation Loss: 1.103485
[Scope: Trainer, Epoch: 1] [2024-10-07 09:48:57.010] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_1.ckpt' with Validation Loss: 1.103485
[Scope: Trainer, Epoch: 1] [2024-10-07 09:48:57.010] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_1.ckpt' with Validation Loss: 1.103485
[Scope: Trainer, Epoch: 1] [2024-10-07 09:48:57.021] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1 completed in 0.17s, Average Training Loss: 1.0997785, Min Loss: 1.0997785, Max Loss: 1.0997785.
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.028] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0936375]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.028] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.41460282, 0.2912256, 0.29417157, 0.380986, 0.29924613], Probabilities Sample: [0.3609498, 0.31905445, 0.31999573, 0.34939018, 0.32196712], Log Probabilities Sample: [-1.0190164, -1.1423935, -1.1394476, -1.051566, -1.1333058], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.0190164, 1.051566, 1.0969465, 1.1260782, 1.0367634] Loss Sum: [26.2473], Mean Loss: [1.0936375]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.028] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.028] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.41460282, 0.2912256, 0.29417157, 0.380986, 0.29924613, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [-0.026627092, 0.013293936, 0.013333156, -0.027108744, 0.013415297]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.039] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.039] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.48935926, -0.6840522, -0.0048897727, 0.4996594, 0.41306743], Gradients Sample Values: [0.001097169, 0.0013688633, -0.0011764535, 0.0032678135, 0.00060523476], Scaled Gradients Sample Values: [0.0001097169, 0.00013688633, -0.00011764536, 0.00032678136, 6.0523478E-05]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.039] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.039] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00020181312, 0.0006293307, 0.00020095667, -1.2808014E-05, -0.00040009522], Gradients Sample Values: [0.0011579308, -0.0029352675, -0.0009888234, 0.00024575973, 0.0020708514], Scaled Gradients Sample Values: [0.00011579308, -0.00029352677, -9.888234E-05, 2.4575973E-05, 0.00020708515]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16321108, -0.10061999, -0.19939052, 0.04397415, -0.336174], Gradients Sample Values: [-0.00267825, 0, 0.0039283438, 0.0007612464, 0.0027805995], Scaled Gradients Sample Values: [-0.000267825, 0, 0.0003928344, 7.612464E-05, 0.00027805995]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0015982569, -0.0001537507, -0.0016275051, 0.0005838863, -0.0018714487], Gradients Sample Values: [-0.0077140387, 0.00077576033, 0.0077707614, -0.0030058906, 0.008822981], Scaled Gradients Sample Values: [-0.0007714039, 7.7576035E-05, 0.0007770762, -0.00030058908, 0.00088229816]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.2384398, 0.041308973, -0.16283777, 0.019520337, 0.13105258], Gradients Sample Values: [-0.013230589, -0.0040641716, 0.017294757, -0.0008310676, 0.0003612847], Scaled Gradients Sample Values: [-0.001323059, -0.00040641715, 0.0017294757, -8.310677E-05, 3.6128473E-05]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.040] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0058973897, 0.0048529655, -0.010750354, 0, 0], Gradients Sample Values: [-0.028032515, -0.024027579, 0.052060083, 0, 0], Scaled Gradients Sample Values: [-0.0028032516, -0.002402758, 0.0052060084]
[Scope: Trainer, Epoch: 2, Batch: 1] [2024-10-07 09:48:57.041] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 1/1 completed in 0.02s. Loss: 1.0936375
[Scope: Trainer, Epoch: 2, Evaluator] [2024-10-07 09:48:57.041] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 2, Evaluator] [2024-10-07 09:48:57.041] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.043] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1133695]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.043] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3365342, 0.33584294, 0.32762286, 0.34468383, 0.3321835], Probabilities Sample: [0.33439925, 0.3341682, 0.33143255, 0.3371252, 0.33293727], Log Probabilities Sample: [-1.0954196, -1.0961108, -1.1043309, -1.0873009, -1.0998012], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1043309, 1.1088519, 1.086541, 1.1104528, 1.1542901] Loss Sum: [6.680217], Mean Loss: [1.1133695]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.043] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1133695], Total Loss: [1.1133695]
[Scope: Trainer, Epoch: 2, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.043] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.3365342, 0.33584294, 0.32762286, 0.34468383, 0.3321835]
[Scope: Trainer, Epoch: 2, Evaluator] [2024-10-07 09:48:57.043] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1133695 
[Scope: Trainer, Epoch: 2] [2024-10-07 09:48:57.043] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2/100, Training Loss: 1.0936375, Validation Loss: 1.1133695
[Scope: Trainer, Epoch: 2] [2024-10-07 09:48:57.044] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_2.ckpt' with Validation Loss: 1.1133695
[Scope: Trainer, Epoch: 2] [2024-10-07 09:48:57.045] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2 completed in 0.02s, Average Training Loss: 1.0936375, Min Loss: 1.0936375, Max Loss: 1.0936375.
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.052] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0881859]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.052] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.40854433, 0.27819958, 0.31325608, 0.550157, 0.15355669], Probabilities Sample: [0.35882115, 0.3149706, 0.3262082, 0.4084308, 0.27471173], Log Probabilities Sample: [-1.0249312, -1.155276, -1.1202195, -0.8954328, -1.292033], Target Sample: [0, 0, 1, 1, 0], Per-Sample Loss: [1.1202195, 0.8954328, 1.1120389, 1.1697147, 1.1085826] Loss Sum: [26.116463], Mean Loss: [1.0881859]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.052] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.052] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.40854433, 0.27819958, 0.31325608, 0.550157, 0.15355669, Gradient Output Sample Values: [0, 0, 1, 1, 0], Gradient Sample Values: [0.014950882, 0.013123776, -0.028074658, -0.024648717, 0.011446322]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.063] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.063] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.4894993, -0.6842188, -0.0047784857, 0.4993366, 0.41299632], Gradients Sample Values: [0.001400441, 0.0016657172, -0.0011128689, 0.003228019, 0.00071121805], Scaled Gradients Sample Values: [0.00014004411, 0.00016657173, -0.00011128689, 0.0003228019, 7.112181E-05]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.063] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.063] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00023708813, 0.0009436513, 0.00032337225, -0.00011772637, -0.0007115741], Gradients Sample Values: [0.00035275007, -0.0031432062, -0.0012241558, 0.0010491835, 0.0031147886], Scaled Gradients Sample Values: [3.5275007E-05, -0.00031432064, -0.00012241559, 0.000104918356, 0.00031147886]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.064] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.064] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.1634644, -0.10061999, -0.19976252, 0.043898303, -0.33643106], Gradients Sample Values: [-0.0025331287, 0, 0.0037200968, 0.00075846916, 0.0025705178], Scaled Gradients Sample Values: [-0.00025331287, 0, 0.00037200967, 7.584692E-05, 0.00025705178]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.064] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.068] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0023152907, -0.00023262721, -0.0023357228, 0.000900907, -0.0026551406], Gradients Sample Values: [-0.007170339, 0.0007887649, 0.007082178, -0.0031702071, 0.007836917], Scaled Gradients Sample Values: [-0.0007170339, 7.8876496E-05, 0.0007082178, -0.00031702072, 0.00078369176]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.068] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.068] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.23969066, 0.041688833, -0.1644685, 0.019603934, 0.1310142], Gradients Sample Values: [-0.012508605, -0.0037986087, 0.016307212, -0.0008359738, 0.00038378878], Scaled Gradients Sample Values: [-0.0012508605, -0.00037986087, 0.0016307213, -8.3597384E-05, 3.837888E-05]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.068] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.068] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.008421117, 0.0072201365, -0.015641253, 0, 0], Gradients Sample Values: [-0.02523728, -0.023671709, 0.048908994, 0, 0], Scaled Gradients Sample Values: [-0.0025237282, -0.002367171, 0.0048908996]
[Scope: Trainer, Epoch: 3, Batch: 1] [2024-10-07 09:48:57.068] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 1/1 completed in 0.02s. Loss: 1.0881859
[Scope: Trainer, Epoch: 3, Evaluator] [2024-10-07 09:48:57.072] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 3, Evaluator] [2024-10-07 09:48:57.072] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.074] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1225075]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.074] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.33779907, 0.3372287, 0.32497224, 0.34650823, 0.33496037], Probabilities Sample: [0.3348194, 0.33462846, 0.33055216, 0.3377318, 0.33385414], Log Probabilities Sample: [-1.094164, -1.0947344, -1.1069908, -1.0855032, -1.0970511], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1069908, 1.11348, 1.0941656, 1.1223879, 1.1677109] Loss Sum: [6.7350445], Mean Loss: [1.1225075]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.074] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1225075], Total Loss: [1.1225075]
[Scope: Trainer, Epoch: 3, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.074] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.33779907, 0.3372287, 0.32497224, 0.34650823, 0.33496037]
[Scope: Trainer, Epoch: 3, Evaluator] [2024-10-07 09:48:57.074] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1225075 
[Scope: Trainer, Epoch: 3] [2024-10-07 09:48:57.074] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3/100, Training Loss: 1.0881859, Validation Loss: 1.1225075
[Scope: Trainer, Epoch: 3] [2024-10-07 09:48:57.074] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_3.ckpt' with Validation Loss: 1.1225075
[Scope: Trainer, Epoch: 3] [2024-10-07 09:48:57.076] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3 completed in 0.03s, Average Training Loss: 1.0881859, Min Loss: 1.0881859, Max Loss: 1.0881859.
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.082] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0832926]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.082] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.43613905, 0.29687005, 0.26699093, 0.5649767, 0.23807637], Probabilities Sample: [0.36840853, 0.32051322, 0.31107822, 0.41437855, 0.29883143], Log Probabilities Sample: [-0.9985628, -1.1378318, -1.1677109, -0.88097537, -1.2078756], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [0.9985628, 0.88097537, 1.2933778, 1.0072371, 1.1316643] Loss Sum: [25.999023], Mean Loss: [1.0832926]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.082] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.082] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.43613905, 0.29687005, 0.26699093, 0.5649767, 0.23807637, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [-0.02631631, 0.013354718, 0.0129615925, -0.024400895, 0.01245131]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.093] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.093] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.4896421, -0.6843401, -0.004685412, 0.49901584, 0.41291237], Gradients Sample Values: [0.0014282074, 0.0012129693, -0.0009307365, 0.0032075772, 0.0008394159], Scaled Gradients Sample Values: [0.00014282075, 0.00012129693, -9.307365E-05, 0.0003207577, 8.394159E-05]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.094] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.094] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00022259157, 0.0011920184, 0.0004700795, -0.00020458287, -0.0010176483], Gradients Sample Values: [-0.0001449656, -0.002483671, -0.0014670725, 0.00086856494, 0.0030607418], Scaled Gradients Sample Values: [-1.449656E-05, -0.0002483671, -0.00014670726, 8.68565E-05, 0.0003060742]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.094] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.094] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16370341, -0.10061999, -0.20011446, 0.043822743, -0.33666846], Gradients Sample Values: [-0.0023901442, 0, 0.0035193353, 0.00075560936, 0.0023740414], Scaled Gradients Sample Values: [-0.00023901442, 0, 0.00035193353, 7.5560936E-05, 0.00023740414]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.094] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.094] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0029807035, -0.0003127293, -0.0029793251, 0.001233417, -0.0033485598], Gradients Sample Values: [-0.0066541266, 0.0008010209, 0.0064360243, -0.0033250998, 0.006934192], Scaled Gradients Sample Values: [-0.0006654127, 8.010209E-05, 0.00064360246, -0.00033250998, 0.0006934192]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.095] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.095] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24086896, 0.042047843, -0.1660058, 0.019687923, 0.13097367], Gradients Sample Values: [-0.011782952, -0.0035901, 0.015373052, -0.000839894, 0.0004053591], Scaled Gradients Sample Values: [-0.0011782952, -0.00035901, 0.0015373052, -8.39894E-05, 4.0535913E-05]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.095] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.095] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.010680344, 0.009560464, -0.020240806, 0, 0], Gradients Sample Values: [-0.022592261, -0.02340328, 0.045995533, 0, 0], Scaled Gradients Sample Values: [-0.0022592263, -0.002340328, 0.0045995535]
[Scope: Trainer, Epoch: 4, Batch: 1] [2024-10-07 09:48:57.095] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 4, Batch 1/1 completed in 0.02s. Loss: 1.0832926
[Scope: Trainer, Epoch: 4, Evaluator] [2024-10-07 09:48:57.095] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 4, Evaluator] [2024-10-07 09:48:57.095] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.097] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1308887]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.097] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.33892578, 0.33861178, 0.32246247, 0.34811917, 0.33765444], Probabilities Sample: [0.33519283, 0.3350876, 0.32971963, 0.33826467, 0.33474326], Log Probabilities Sample: [-1.0930493, -1.0933633, -1.1095126, -1.0839267, -1.0943915], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1095126, 1.1178194, 1.1012555, 1.1333196, 1.1796875] Loss Sum: [6.785332], Mean Loss: [1.1308887]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.097] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1308887], Total Loss: [1.1308887]
[Scope: Trainer, Epoch: 4, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.097] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.33892578, 0.33861178, 0.32246247, 0.34811917, 0.33765444]
[Scope: Trainer, Epoch: 4, Evaluator] [2024-10-07 09:48:57.097] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1308887 
[Scope: Trainer, Epoch: 4] [2024-10-07 09:48:57.097] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 4/100, Training Loss: 1.0832926, Validation Loss: 1.1308887
[Scope: Trainer, Epoch: 4] [2024-10-07 09:48:57.097] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_4.ckpt' with Validation Loss: 1.1308887
[Scope: Trainer, Epoch: 4] [2024-10-07 09:48:57.099] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 4 completed in 0.02s, Average Training Loss: 1.0832926, Min Loss: 1.0832926, Max Loss: 1.0832926.
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.105] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0789986]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.105] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.36183688, 0.34209073, 0.29607242, 0.33892578, 0.33861178], Probabilities Sample: [0.34284157, 0.3361382, 0.3210202, 0.33519283, 0.3350876], Log Probabilities Sample: [-1.0704869, -1.090233, -1.1362513, -1.0930493, -1.0933633], Target Sample: [0, 1, 0, 1, 0], Per-Sample Loss: [1.090233, 1.0930493, 0.85291106, 1.245651, 0.86662024] Loss Sum: [25.895966], Mean Loss: [1.0789986]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.105] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.105] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.36183688, 0.34209073, 0.29607242, 0.33892578, 0.33861178, Gradient Output Sample Values: [0, 1, 0, 1, 0], Gradient Sample Values: [0.014285065, -0.027660908, 0.013375841, -0.0277003, 0.013961983]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.116] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.116] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.48976973, -0.68441963, -0.004658223, 0.49867532, 0.41284525], Gradients Sample Values: [0.0012760645, 0.00079528167, -0.00027189142, 0.0034051358, 0.0006710393], Scaled Gradients Sample Values: [0.00012760646, 7.952817E-05, -2.7189142E-05, 0.0003405136, 6.710393E-05]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.117] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.117] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00022319032, 0.0013677498, 0.00056921487, -0.0003013041, -0.0012426483], Gradients Sample Values: [5.9875892E-06, -0.0017573138, -0.0009913536, 0.0009672122, 0.002250001], Scaled Gradients Sample Values: [5.9875896E-07, -0.00017573139, -9.9135366E-05, 9.672122E-05, 0.00022500011]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.117] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.117] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16392879, -0.10061999, -0.20044763, 0.043747436, -0.33688793], Gradients Sample Values: [-0.0022538267, 0, 0.0033317672, 0.0007530544, 0.0021947452], Scaled Gradients Sample Values: [-0.00022538267, 0, 0.00033317672, 7.530544E-05, 0.00021947453]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.117] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.117] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0035985052, -0.0003939962, -0.003563599, 0.0013760016, -0.003960982], Gradients Sample Values: [-0.0061780177, 0.0008126688, 0.005842738, -0.0014258465, 0.0061242227], Scaled Gradients Sample Values: [-0.0006178018, 8.126688E-05, 0.0005842738, -0.00014258466, 0.0006124223]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.118] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.118] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24197873, 0.042388666, -0.16745642, 0.019772163, 0.1309311], Gradients Sample Values: [-0.0110978605, -0.0034082264, 0.014506089, -0.0008423984, 0.0004257055], Scaled Gradients Sample Values: [-0.0011097861, -0.00034082265, 0.0014506088, -8.423984E-05, 4.257055E-05]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.118] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.118] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.012696484, 0.011878328, -0.02457481, 0, 0], Gradients Sample Values: [-0.020161398, -0.023178646, 0.04334004, 0, 0], Scaled Gradients Sample Values: [-0.00201614, -0.0023178647, 0.004334004]
[Scope: Trainer, Epoch: 5, Batch: 1] [2024-10-07 09:48:57.118] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5, Batch 1/1 completed in 0.02s. Loss: 1.0789986
[Scope: Trainer, Epoch: 5, Evaluator] [2024-10-07 09:48:57.118] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 5, Evaluator] [2024-10-07 09:48:57.118] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.120] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1386844]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.120] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.33994272, 0.34001285, 0.32004443, 0.3495288, 0.3403245], Probabilities Sample: [0.335529, 0.3355525, 0.32891852, 0.338728, 0.33562458], Log Probabilities Sample: [-1.0920469, -1.0919768, -1.1119453, -1.0825578, -1.0917621], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1119453, 1.1219399, 1.1078619, 1.1433978, 1.1909373] Loss Sum: [6.8321066], Mean Loss: [1.1386844]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.120] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1386844], Total Loss: [1.1386844]
[Scope: Trainer, Epoch: 5, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.120] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.33994272, 0.34001285, 0.32004443, 0.3495288, 0.3403245]
[Scope: Trainer, Epoch: 5, Evaluator] [2024-10-07 09:48:57.120] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1386844 
[Scope: Trainer, Epoch: 5] [2024-10-07 09:48:57.120] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5/100, Training Loss: 1.0789986, Validation Loss: 1.1386844
[Scope: Trainer, Epoch: 5] [2024-10-07 09:48:57.120] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_5.ckpt' with Validation Loss: 1.1386844
[Scope: Trainer, Epoch: 5] [2024-10-07 09:48:57.122] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5 completed in 0.02s, Average Training Loss: 1.0789986, Min Loss: 1.0789986, Max Loss: 1.0789986.
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.128] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0751035]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.128] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.48633525, 0.33930656, 0.1743582, 0.33994272, 0.34001285], Probabilities Sample: [0.38531673, 0.33263206, 0.2820512, 0.335529, 0.3355525], Log Probabilities Sample: [-0.95368963, -1.1007183, -1.2656666, -1.0920469, -1.0919768], Target Sample: [0, 1, 0, 1, 0], Per-Sample Loss: [1.1007183, 1.0920469, 0.98383695, 0.841925, 1.0130839] Loss Sum: [25.802485], Mean Loss: [1.0751035]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.128] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.128] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.48633525, 0.33930656, 0.1743582, 0.33994272, 0.34001285, Gradient Output Sample Values: [0, 1, 0, 1, 0], Gradient Sample Values: [0.016054863, -0.027806997, 0.011752133, -0.027686292, 0.013981354]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.139] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.139] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.4898827, -0.6844865, -0.0046334947, 0.4983386, 0.4127725], Gradients Sample Values: [0.0011298531, 0.00066851336, -0.00024728445, 0.00336716, 0.000727372], Scaled Gradients Sample Values: [0.00011298531, 6.685134E-05, -2.4728446E-05, 0.000336716, 7.27372E-05]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.139] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.139] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00018431619, 0.0014891479, 0.0006330982, -0.00039716944, -0.0014506833], Gradients Sample Values: [-0.00038874126, -0.0012139812, -0.0006388331, 0.0009586534, 0.0020803502], Scaled Gradients Sample Values: [-3.887413E-05, -0.00012139812, -6.388331E-05, 9.586534E-05, 0.00020803502]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16414116, -0.10061999, -0.20076321, 0.04367238, -0.33709088], Gradients Sample Values: [-0.0021236567, 0, 0.0031557642, 0.00075058086, 0.002029425], Scaled Gradients Sample Values: [-0.00021236567, 0, 0.0003155764, 7.505809E-05, 0.00020294251]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.004171869, -0.00047635045, -0.0040931795, 0.0015276653, -0.0045002024], Gradients Sample Values: [-0.0057336343, 0.0008235427, 0.005295804, -0.0015166372, 0.005392203], Scaled Gradients Sample Values: [-0.00057336345, 8.235427E-05, 0.0005295804, -0.00015166371, 0.00053922035]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24302246, 0.04271378, -0.16882525, 0.019856535, 0.13088666], Gradients Sample Values: [-0.010437155, -0.003251134, 0.0136882905, -0.0008437183, 0.00044435784], Scaled Gradients Sample Values: [-0.0010437155, -0.0003251134, 0.001368829, -8.437183E-05, 4.4435783E-05]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.014487952, 0.0141769275, -0.028664878, 0, 0], Gradients Sample Values: [-0.017914683, -0.022985987, 0.040900674, 0, 0], Scaled Gradients Sample Values: [-0.0017914682, -0.0022985989, 0.0040900675]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 09:48:57.140] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 6, Batch 1/1 completed in 0.02s. Loss: 1.0751035
[Scope: Trainer, Epoch: 6, Evaluator] [2024-10-07 09:48:57.140] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 6, Evaluator] [2024-10-07 09:48:57.141] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.142] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1459543]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.142] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.34081635, 0.34145403, 0.31772962, 0.3507355, 0.34301257], Probabilities Sample: [0.33581662, 0.33603084, 0.32815254, 0.33912122, 0.3365123], Log Probabilities Sample: [-1.09119, -1.0905523, -1.1142768, -1.0813977, -1.0891206], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1142768, 1.1258813, 1.1141452, 1.1527187, 1.2012773] Loss Sum: [6.8757257], Mean Loss: [1.1459543]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.142] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1459543], Total Loss: [1.1459543]
[Scope: Trainer, Epoch: 6, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.142] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.34081635, 0.34145403, 0.31772962, 0.3507355, 0.34301257]
[Scope: Trainer, Epoch: 6, Evaluator] [2024-10-07 09:48:57.142] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1459543 
[Scope: Trainer, Epoch: 6] [2024-10-07 09:48:57.142] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 6/100, Training Loss: 1.0751035, Validation Loss: 1.1459543
[Scope: Trainer, Epoch: 6] [2024-10-07 09:48:57.142] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_6.ckpt' with Validation Loss: 1.1459543
[Scope: Trainer, Epoch: 6] [2024-10-07 09:48:57.144] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 6 completed in 0.02s, Average Training Loss: 1.0751035, Min Loss: 1.0751035, Max Loss: 1.0751035.
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.150] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0715908]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.150] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.54348093, 0.1374552, 0.31906384, 0.6360778, 0.1530088], Probabilities Sample: [0.40563428, 0.2702713, 0.32409444, 0.44042203, 0.27169073], Log Probabilities Sample: [-0.9023033, -1.308329, -1.1267203, -0.82002187, -1.3030909], Target Sample: [0, 1, 0, 1, 0], Per-Sample Loss: [1.308329, 0.82002187, 1.0118341, 1.099539, 1.1142768] Loss Sum: [25.71818], Mean Loss: [1.0715908]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.150] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.150] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.54348093, 0.1374552, 0.31906384, 0.6360778, 0.1530088, Gradient Output Sample Values: [0, 1, 0, 1, 0], Gradient Sample Values: [0.016901428, -0.030405363, 0.013503935, -0.023315748, 0.011320447]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.161] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.161] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.48999396, -0.6845483, -0.00456801, 0.49800688, 0.41268018], Gradients Sample Values: [0.0011126602, 0.00061802584, -0.00065484765, 0.0033172749, 0.00092328433], Scaled Gradients Sample Values: [0.00011126602, 6.180259E-05, -6.548477E-05, 0.0003317275, 9.232843E-05]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.161] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.161] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00025450933, 0.0016059696, 0.00076172815, -0.0004861214, -0.00166054], Gradients Sample Values: [0.0007019314, -0.0011682176, -0.0012862997, 0.0008895196, 0.0020985666], Scaled Gradients Sample Values: [7.019314E-05, -0.000116821764, -0.00012862997, 8.895196E-05, 0.00020985666]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.161] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16434135, -0.10061999, -0.20106246, 0.043597504, -0.33727872], Gradients Sample Values: [-0.0020018034, 0, 0.0029924645, 0.0007487323, 0.001878317], Scaled Gradients Sample Values: [-0.00020018034, 0, 0.00029924646, 7.4873235E-05, 0.0001878317]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0047042677, -0.00055974675, -0.0045721326, 0.0016873408, -0.0049730362], Gradients Sample Values: [-0.005323988, 0.0008339629, 0.0047895303, -0.0015967553, 0.0047283377], Scaled Gradients Sample Values: [-0.0005323988, 8.339629E-05, 0.00047895304, -0.00015967553, 0.00047283378]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24400325, 0.04302677, -0.17011903, 0.019941026, 0.13084044], Gradients Sample Values: [-0.009807892, -0.0031299235, 0.012937817, -0.0008449072, 0.000462242], Scaled Gradients Sample Values: [-0.0009807892, -0.00031299234, 0.0012937817, -8.449072E-05, 4.62242E-05]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.016071463, 0.016459329, -0.032530792, 0, 0], Gradients Sample Values: [-0.01583511, -0.022824015, 0.03865914, 0, 0], Scaled Gradients Sample Values: [-0.001583511, -0.0022824015, 0.0038659142]
[Scope: Trainer, Epoch: 7, Batch: 1] [2024-10-07 09:48:57.162] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 7, Batch 1/1 completed in 0.02s. Loss: 1.0715908
[Scope: Trainer, Epoch: 7, Evaluator] [2024-10-07 09:48:57.162] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 7, Evaluator] [2024-10-07 09:48:57.162] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.164] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1527245]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.164] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.34158355, 0.34290364, 0.31551287, 0.35177588, 0.34567147], Probabilities Sample: [0.33606812, 0.33651206, 0.32741982, 0.33945683, 0.33739096], Log Probabilities Sample: [-1.0904413, -1.0891213, -1.1165121, -1.0804085, -1.0865129], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1165121, 1.1296318, 1.1200074, 1.1613694, 1.2107373] Loss Sum: [6.916347], Mean Loss: [1.1527245]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.164] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1527245], Total Loss: [1.1527245]
[Scope: Trainer, Epoch: 7, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.164] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.34158355, 0.34290364, 0.31551287, 0.35177588, 0.34567147]
[Scope: Trainer, Epoch: 7, Evaluator] [2024-10-07 09:48:57.164] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1527245 
[Scope: Trainer, Epoch: 7] [2024-10-07 09:48:57.164] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 7/100, Training Loss: 1.0715908, Validation Loss: 1.1527245
[Scope: Trainer, Epoch: 7] [2024-10-07 09:48:57.164] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_7.ckpt' with Validation Loss: 1.1527245
[Scope: Trainer, Epoch: 7] [2024-10-07 09:48:57.166] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 7 completed in 0.02s, Average Training Loss: 1.0715908, Min Loss: 1.0715908, Max Loss: 1.0715908.
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.172] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0683926]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.172] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.57061565, 0.15620057, 0.2731838, 0.35157484, 0.34563577], Probabilities Sample: [0.4160686, 0.27490762, 0.3090238, 0.33938986, 0.33738017], Log Probabilities Sample: [-0.8769051, -1.2913202, -1.174337, -1.0806057, -1.0865449], Target Sample: [0, 1, 0, 0, 0], Per-Sample Loss: [1.2913202, 1.1293913, 1.1393147, 1.0985132, 1.1165121] Loss Sum: [25.641422], Mean Loss: [1.0683926]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.172] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.172] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.57061565, 0.15620057, 0.2731838, 0.35157484, 0.34563577, Gradient Output Sample Values: [0, 1, 0, 0, 0], Gradient Sample Values: [0.017336192, -0.030212184, 0.012875992, 0.014141244, 0.014057507]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.183] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.183] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.49010676, -0.68460137, -0.004505225, 0.49767992, 0.41258037], Gradients Sample Values: [0.001128143, 0.0005305478, -0.0006278495, 0.00326968, 0.0009982137], Scaled Gradients Sample Values: [0.000112814305, 5.305478E-05, -6.278495E-05, 0.000326968, 9.9821365E-05]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.183] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.183] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0002847683, 0.0016950943, 0.0008660109, -0.0005797049, -0.0019030035], Gradients Sample Values: [0.0003025896, -0.0008912466, -0.0010428276, 0.0009358349, 0.002424635], Scaled Gradients Sample Values: [3.025896E-05, -8.912466E-05, -0.000104282764, 9.358349E-05, 0.0002424635]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.183] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.183] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.1645301, -0.10061999, -0.20134667, 0.043522812, -0.33751145], Gradients Sample Values: [-0.0018874662, 0, 0.0028420947, 0.00074692466, 0.0023274005], Scaled Gradients Sample Values: [-0.00018874662, 0, 0.00028420947, 7.469247E-05, 0.00023274006]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.005198747, -0.00064412295, -0.0050046565, 0.001854114, -0.0055026715], Gradients Sample Values: [-0.0049447897, 0.0008437622, 0.004325237, -0.0016677319, 0.0052963523], Scaled Gradients Sample Values: [-0.00049447897, 8.437622E-05, 0.00043252372, -0.0001667732, 0.00052963523]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.2449242, 0.043330863, -0.17134409, 0.020025544, 0.13079259], Gradients Sample Values: [-0.009209568, -0.0030409344, 0.012250502, -0.00084516837, 0.0004785412], Scaled Gradients Sample Values: [-0.0009209568, -0.00030409344, 0.0012250502, -8.451684E-05, 4.785412E-05]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.017463366, 0.018727835, -0.036191203, 0, 0], Gradients Sample Values: [-0.013919032, -0.022685058, 0.03660409, 0, 0], Scaled Gradients Sample Values: [-0.0013919033, -0.002268506, 0.0036604092]
[Scope: Trainer, Epoch: 8, Batch: 1] [2024-10-07 09:48:57.184] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 8, Batch 1/1 completed in 0.02s. Loss: 1.0683926
[Scope: Trainer, Epoch: 8, Evaluator] [2024-10-07 09:48:57.184] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 8, Evaluator] [2024-10-07 09:48:57.184] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.186] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1590518]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.186] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.34226742, 0.34433275, 0.31339982, 0.35265836, 0.34831148], Probabilities Sample: [0.33629128, 0.33698657, 0.32672215, 0.3397377, 0.33826414], Log Probabilities Sample: [-1.0897776, -1.0877122, -1.1186452, -1.0795814, -1.0839282], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1186452, 1.1332095, 1.1255137, 1.1694148, 1.2194752] Loss Sum: [6.954311], Mean Loss: [1.1590518]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.186] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1590518], Total Loss: [1.1590518]
[Scope: Trainer, Epoch: 8, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.186] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.34226742, 0.34433275, 0.31339982, 0.35265836, 0.34831148]
[Scope: Trainer, Epoch: 8, Evaluator] [2024-10-07 09:48:57.186] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1590518 
[Scope: Trainer, Epoch: 8] [2024-10-07 09:48:57.186] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 8/100, Training Loss: 1.0683926, Validation Loss: 1.1590518
[Scope: Trainer, Epoch: 8] [2024-10-07 09:48:57.186] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_8.ckpt' with Validation Loss: 1.1590518
[Scope: Trainer, Epoch: 8] [2024-10-07 09:48:57.188] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 8 completed in 0.02s, Average Training Loss: 1.0683926, Min Loss: 1.0683926, Max Loss: 1.0683926.
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.194] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0654404]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.194] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.40391, 0.2997257, 0.29636425, 0.55166465, 0.21798804], Probabilities Sample: [0.35725838, 0.32191095, 0.32083067, 0.4095879, 0.2933818], Log Probabilities Sample: [-1.029296, -1.1334803, -1.1368418, -0.89260375, -1.2262805], Target Sample: [0, 1, 0, 0, 1], Per-Sample Loss: [1.1334803, 1.2262805, 1.1233757, 1.0341661, 1.1329559] Loss Sum: [25.57057], Mean Loss: [1.0654404]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.194] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.195] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.40391, 0.2997257, 0.29636425, 0.55166465, 0.21798804, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.0148857655, -0.02825371, 0.013367944, 0.017066162, -0.029442424]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.205] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.205] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.49021783, -0.6846468, -0.0044273227, 0.4973554, 0.41247943], Gradients Sample Values: [0.0011107995, 0.00045395715, -0.000779025, 0.0032451916, 0.0010093743], Scaled Gradients Sample Values: [0.00011107995, 4.5395715E-05, -7.790251E-05, 0.00032451918, 0.00010093743]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.205] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.205] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00031334654, 0.0017629247, 0.0010654429, -0.0006911604, -0.0021356912], Gradients Sample Values: [0.0002857824, -0.00067830435, -0.00199432, 0.0011145549, 0.0023268778], Scaled Gradients Sample Values: [2.8578239E-05, -6.7830435E-05, -0.000199432, 0.0001114555, 0.00023268779]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16470823, -0.10061999, -0.20161708, 0.04344825, -0.33773348], Gradients Sample Values: [-0.0017812403, 0, 0.0027040788, 0.0007456287, 0.0022202411], Scaled Gradients Sample Values: [-0.00017812404, 0, 0.00027040788, 7.456287E-05, 0.00022202411]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0056582973, -0.00072944915, -0.0053945817, 0.0020270804, -0.0059814677], Gradients Sample Values: [-0.0045955037, 0.0008532618, 0.003899251, -0.0017296643, 0.004787962], Scaled Gradients Sample Values: [-0.00045955039, 8.532618E-05, 0.00038992512, -0.00017296644, 0.0004787962]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24578892, 0.043628372, -0.1725063, 0.020110026, 0.13074319], Gradients Sample Values: [-0.008647094, -0.002975077, 0.011622172, -0.0008448237, 0.0004939086], Scaled Gradients Sample Values: [-0.0008647094, -0.0002975077, 0.0011622172, -8.448237E-05, 4.939086E-05]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.018678894, 0.020984698, -0.039663594, 0, 0], Gradients Sample Values: [-0.012155282, -0.022568628, 0.034723908, 0, 0], Scaled Gradients Sample Values: [-0.0012155282, -0.0022568628, 0.0034723908]
[Scope: Trainer, Epoch: 9, Batch: 1] [2024-10-07 09:48:57.206] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 9, Batch 1/1 completed in 0.02s. Loss: 1.0654404
[Scope: Trainer, Epoch: 9, Evaluator] [2024-10-07 09:48:57.207] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 9, Evaluator] [2024-10-07 09:48:57.207] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.208] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.165001]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.208] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.34293863, 0.3456892, 0.31137216, 0.35342216, 0.3510127], Probabilities Sample: [0.33650988, 0.33743677, 0.32605338, 0.3399767, 0.33915854], Log Probabilities Sample: [-1.0891278, -1.0863771, -1.1206942, -1.0788782, -1.0812876], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1206942, 1.1367352, 1.1306753, 1.1769187, 1.2275659] Loss Sum: [6.9900064], Mean Loss: [1.165001]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.208] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.165001], Total Loss: [1.165001]
[Scope: Trainer, Epoch: 9, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.208] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.34293863, 0.3456892, 0.31137216, 0.35342216, 0.3510127]
[Scope: Trainer, Epoch: 9, Evaluator] [2024-10-07 09:48:57.208] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.165001 
[Scope: Trainer, Epoch: 9] [2024-10-07 09:48:57.208] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 9/100, Training Loss: 1.0654404, Validation Loss: 1.165001
[Scope: Trainer, Epoch: 9] [2024-10-07 09:48:57.208] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_9.ckpt' with Validation Loss: 1.165001
[Scope: Trainer, Epoch: 9] [2024-10-07 09:48:57.211] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 9 completed in 0.02s, Average Training Loss: 1.0654404, Min Loss: 1.0654404, Max Loss: 1.0654404.
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.217] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0627015]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.217] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.37123802, 0.3520224, 0.27673957, 0.40074107, 0.31313974], Probabilities Sample: [0.34592536, 0.33934167, 0.314733, 0.356147, 0.32627553], Log Probabilities Sample: [-1.0615323, -1.0807478, -1.1560307, -1.0324117, -1.1200131], Target Sample: [0, 1, 0, 0, 1], Per-Sample Loss: [1.0807478, 1.1200131, 0.81867105, 1.2217945, 1.0891278] Loss Sum: [25.504835], Mean Loss: [1.0627015]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.217] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.217] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.37123802, 0.3520224, 0.27673957, 0.40074107, 0.31313974, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.014413557, -0.027527431, 0.013113875, 0.014839458, -0.028071852]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.235] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.235] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.49032742, -0.6846852, -0.004354502, 0.49703538, 0.41239604], Gradients Sample Values: [0.0010957092, 0.00038363, -0.0007282077, 0.0032001757, 0.00083396444], Scaled Gradients Sample Values: [0.000109570916, 3.8363E-05, -7.2820774E-05, 0.00032001757, 8.3396444E-05]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.235] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.235] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.0003400749, 0.001810832, 0.001247915, -0.0008070261, -0.0023347815], Gradients Sample Values: [0.00026728382, -0.00047907257, -0.0018247208, 0.0011586575, 0.001990902], Scaled Gradients Sample Values: [2.6728381E-05, -4.790726E-05, -0.00018247208, 0.00011586575, 0.00019909021]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.235] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.235] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16487646, -0.10061999, -0.20187475, 0.043373805, -0.33794567], Gradients Sample Values: [-0.0016823718, 0, 0.0025767586, 0.00074445555, 0.0021220143], Scaled Gradients Sample Values: [-0.00016823718, 0, 0.00025767586, 7.4445554E-05, 0.00021220143]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0060854536, -0.0008156849, -0.005745242, 0.0022053795, -0.0064139483], Gradients Sample Values: [-0.0042715603, 0.0008623576, 0.0035065997, -0.0017829922, 0.004324807], Scaled Gradients Sample Values: [-0.00042715605, 8.623576E-05, 0.00035065998, -0.00017829922, 0.00043248068]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24660042, 0.04392156, -0.173611, 0.020194529, 0.1306923], Gradients Sample Values: [-0.008115019, -0.0029318957, 0.011046917, -0.0008450213, 0.0005088076], Scaled Gradients Sample Values: [-0.0008115019, -0.00029318957, 0.0011046918, -8.450213E-05, 5.088076E-05]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.019731434, 0.023230905, -0.042962343, 0, 0], Gradients Sample Values: [-0.0105253905, -0.022462074, 0.032987464, 0, 0], Scaled Gradients Sample Values: [-0.0010525391, -0.0022462073, 0.0032987464]
[Scope: Trainer, Epoch: 10, Batch: 1] [2024-10-07 09:48:57.236] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 10, Batch 1/1 completed in 0.02s. Loss: 1.0627015
[Scope: Trainer, Epoch: 10, Evaluator] [2024-10-07 09:48:57.236] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 10, Evaluator] [2024-10-07 09:48:57.236] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.238] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1705402]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.238] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3435575, 0.3470214, 0.30942106, 0.35407624, 0.35365808], Probabilities Sample: [0.3367106, 0.33787894, 0.32541046, 0.3401774, 0.34003517], Log Probabilities Sample: [-1.0885315, -1.0850676, -1.1226679, -1.0782881, -1.0787063], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1226679, 1.1400987, 1.1355217, 1.183747, 1.2350665] Loss Sum: [7.023241], Mean Loss: [1.1705402]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.238] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1705402], Total Loss: [1.1705402]
[Scope: Trainer, Epoch: 10, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.238] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.3435575, 0.3470214, 0.30942106, 0.35407624, 0.35365808]
[Scope: Trainer, Epoch: 10, Evaluator] [2024-10-07 09:48:57.238] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1705402 
[Scope: Trainer, Epoch: 10] [2024-10-07 09:48:57.238] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 10/100, Training Loss: 1.0627015, Validation Loss: 1.1705402
[Scope: Trainer, Epoch: 10] [2024-10-07 09:48:57.238] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_10.ckpt' with Validation Loss: 1.1705402
[Scope: Trainer, Epoch: 10] [2024-10-07 09:48:57.241] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 10 completed in 0.03s, Average Training Loss: 1.0627015, Min Loss: 1.0627015, Max Loss: 1.0627015.
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.248] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.0601466]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.248] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.43729153, 0.26343653, 0.29927197, 0.6931315, 0.14909619], Probabilities Sample: [0.3687995, 0.30994603, 0.3212545, 0.46171027, 0.2679773], Log Probabilities Sample: [-0.99750215, -1.171357, -1.1355217, -0.7728177, -1.316853], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [0.99750215, 0.7728177, 1.1221188, 1.0054578, 1.1396048] Loss Sum: [25.443518], Mean Loss: [1.0601466]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.248] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Gradient Shape: [24, 3]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.248] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.43729153, 0.26343653, 0.29927197, 0.6931315, 0.14909619, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [-0.02630002, 0.012914418, 0.013385604, -0.022428738, 0.01116572]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.259] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.259] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.49044526, -0.6847276, -0.004282618, 0.49672768, 0.41231012], Gradients Sample Values: [0.0011784397, 0.00042436516, -0.00071884005, 0.003077034, 0.0008591368], Scaled Gradients Sample Values: [0.00011784397, 4.2436517E-05, -7.188401E-05, 0.0003077034, 8.5913685E-05]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.259] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.259] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.00031983558, 0.0019020059, 0.0013841655, -0.00097562454, -0.0025717996], Gradients Sample Values: [-0.00020239316, -0.0009117388, -0.0013625056, 0.001685984, 0.002370181], Scaled Gradients Sample Values: [-2.0239317E-05, -9.117388E-05, -0.00013625056, 0.0001685984, 0.0002370181]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.16503559, -0.10061999, -0.20212081, 0.04329942, -0.33814898], Gradients Sample Values: [-0.0015913225, 0, 0.0024605566, 0.0007438424, 0.002033251], Scaled Gradients Sample Values: [-0.00015913225, 0, 0.00024605566, 7.438424E-05, 0.00020332511]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.1]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0064829024, -0.0009027965, -0.006060073, 0.0023881493, -0.006804579], Gradients Sample Values: [-0.003974489, 0.00087111635, 0.0031483117, -0.0018276983, 0.003906306], Scaled Gradients Sample Values: [-0.0003974489, 8.7111635E-05, 0.00031483118, -0.00018276983, 0.0003906306]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.2473625, 0.044211734, -0.17466325, 0.020278985, 0.13064004], Gradients Sample Values: [-0.007620682, -0.002901745, 0.010522427, -0.00084456615, 0.0005226373], Scaled Gradients Sample Values: [-0.0007620682, -0.0002901745, 0.0010522427, -8.445662E-05, 5.2263727E-05]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.1]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.020634921, 0.025467284, -0.04610221, 0, 0], Gradients Sample Values: [-0.00903487, -0.0223638, 0.031398676, 0, 0], Scaled Gradients Sample Values: [-0.00090348703, -0.00223638, 0.0031398677]
[Scope: Trainer, Epoch: 11, Batch: 1] [2024-10-07 09:48:57.260] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 11, Batch 1/1 completed in 0.02s. Loss: 1.0601466
[Scope: Trainer, Epoch: 11, Evaluator] [2024-10-07 09:48:57.261] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Trainer, Epoch: 11, Evaluator] [2024-10-07 09:48:57.261] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.262] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1757565]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.262] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3440736, 0.34842744, 0.30749902, 0.3545938, 0.35633743], Probabilities Sample: [0.3368762, 0.33834612, 0.3247777, 0.34033045, 0.34092438], Log Probabilities Sample: [-1.0880398, -1.0836859, -1.1246144, -1.0778382, -1.0760946], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1246144, 1.1433631, 1.140067, 1.190029, 1.2421312] Loss Sum: [7.0545387], Mean Loss: [1.1757565]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.262] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1757565], Total Loss: [1.1757565]
[Scope: Trainer, Epoch: 11, Evaluator, Batch Index: 1] [2024-10-07 09:48:57.262] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.3440736, 0.34842744, 0.30749902, 0.3545938, 0.35633743]
[Scope: Trainer, Epoch: 11, Evaluator] [2024-10-07 09:48:57.262] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1757565 
[Scope: Trainer, Epoch: 11] [2024-10-07 09:48:57.262] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 11/100, Training Loss: 1.0601466, Validation Loss: 1.1757565
[Scope: Trainer, Epoch: 11] [2024-10-07 09:48:57.262] [Warning] HyperAI.Core.Trainings.Trainers.Trainer: Early stopping triggered.
[Scope: Evaluator] [2024-10-07 09:48:57.263] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Shape: [6, 2], Labels Shape: [6, 3]
[Scope: Evaluator] [2024-10-07 09:48:57.263] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate started with Input Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Labels Sample: [1, 0, 0, 1, 0]
[Scope: Evaluator, Batch Index: 1] [2024-10-07 09:48:57.264] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [6, 3], Target Shape: [6, 3], Mean Loss: [1.1757565]
[Scope: Evaluator, Batch Index: 1] [2024-10-07 09:48:57.264] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3440736, 0.34842744, 0.30749902, 0.3545938, 0.35633743], Probabilities Sample: [0.3368762, 0.33834612, 0.3247777, 0.34033045, 0.34092438], Log Probabilities Sample: [-1.0880398, -1.0836859, -1.1246144, -1.0778382, -1.0760946], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1246144, 1.1433631, 1.140067, 1.190029, 1.2421312] Loss Sum: [7.0545387], Mean Loss: [1.1757565]
[Scope: Evaluator, Batch Index: 1] [2024-10-07 09:48:57.264] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Index: [0], Batch Start: [0], currentBatchSize: [6], Batch Data Shape: [6, 2], Batch Labels Shape: [6, 3], Predictions Shape: [6, 3], Batch Loss: [1.1757565], Total Loss: [1.1757565]
[Scope: Evaluator, Batch Index: 1] [2024-10-07 09:48:57.264] [Debug] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluate completed with Batch Data Sample: [0, 0, 0.09588582, 0.028388549, 0.09611897], Batch Labels Sample: [1, 0, 0, 1, 0], Predictions Sample: [0.3440736, 0.34842744, 0.30749902, 0.3545938, 0.35633743]
[Scope: Evaluator] [2024-10-07 09:48:57.264] [Information] HyperAI.Core.Trainings.Evaluators.Evaluator: Evaluation Loss: 1.1757565