can you find calculation problems in this log?
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.851] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.858] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.015146853, 0.0024597952, -0.039666627, 0.037461713, 0.02505939], Output Sample Values: [0, 0.0024597952, 0, 0.037461713, 0.02505939]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.866] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.866] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.09399357, -0.13828765, 0.028771881, -0.06322589, 0.032807276], Output Sample Values: [0, 0, 0.028771881, 0, 0.032807276]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.876] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.876] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [-0.07801417, 0.08422563, -0.013849381, -1.0535948, 0.74881846], Output Sample Values: [0.30841252, 0.362737, 0.32885042, 0.10775632, 0.65346277]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.886] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.0989454]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.886] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.30841252, 0.362737, 0.32885042, 0.10775632, 0.65346277], Target Sample: [0, 0, 1, 0, 1], Per-Sample Loss: [1.1033467, 0.8064681, 0.893846, 1.2065333, 1.1280603], LogSumExp: [1.4321971, 1.4599309, 1.4444548, 1.4356595, 1.4322822], Log Probabilities: [-1.1237845, -1.0694602, -1.1033467, -1.3521745, -0.8064681], Product: [-0, -0, -1.1033467, -0, -0.8064681], Loss Sum: [35.166252], Mean Loss: [1.0989454]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.889] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.889] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.30841252, 0.362737, 0.32885042, 0.10775632, 0.65346277, Gradient Output Sample Values: [0, 0, 1, 0, 1], Gradient Sample Values: [0.010157728, 0.010724804, -0.020882534, 0.008083661, -0.017299]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.890] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [32, 3], GradOutput Shape [32, 3], GradInput Shape [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.890] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.30841252, 0.362737, 0.32885042, 0.10775632, 0.65346277, Gradient Output Sample Values: [0.010157728, 0.010724804, -0.020882534, 0.008083661, -0.017299], Gradient Input Sample Values: [0.0030847122, 0.00383376, -0.0069184727, 0.0017581955, -0.005924463]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.895] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.895] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0.028771881, 0, 0.032807276, Gradient Output Sample Values: [0.0020380723, 0.00045216267, 0.0015185005, -0.00020286994, 0.0025062528], Gradient Input Sample Values: [0, 0, 0.0015185005, 0, 0.0025062528]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.907] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.907] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.0024597952, 0, 0.037461713, 0.02505939, Gradient Output Sample Values: [-0.00092912134, 0.000298868, 0.00016499894, -0.002273432, 0.0026068534], Gradient Input Sample Values: [0, 0.000298868, 0, -0.002273432, 0.0026068534]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:44:02.927] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 1/75 completed in 0.11s. Loss: 1.0989454
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.930] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.931] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.015145765, 0.0025325324, -0.039675612, 0.037483837, 0.025126882], Output Sample Values: [0, 0.0025325324, 0, 0.037483837, 0.025126882]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.937] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.937] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.093985945, -0.13833442, 0.028774317, -0.063271604, 0.03309678], Output Sample Values: [0, 0, 0.028774317, 0, 0.03309678]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.937] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.937] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [-0.07683091, 0.08399101, -0.014771654, -1.0491041, 0.7497633], Output Sample Values: [0.3087848, 0.3626603, 0.32855487, 0.10828002, 0.65431446]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.937] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1340474]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.938] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.3087848, 0.3626603, 0.32855487, 0.10828002, 0.65431446], Target Sample: [0, 0, 1, 0, 1], Per-Sample Loss: [1.1234089, 1.222636, 1.185216, 0.9966366, 1.1039764], LogSumExp: [1.4321938, 1.4600415, 1.4443004, 1.4356202, 1.4322784], Log Probabilities: [-1.1234089, -1.0695335, -1.1036389, -1.3517615, -0.80572706], Product: [-1.1234089, -0, -0, -0, -0], Loss Sum: [36.289516], Mean Loss: [1.1340474]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.938] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.938] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.3087848, 0.3626603, 0.32855487, 0.10828002, 0.65431446, Gradient Output Sample Values: [0, 0, 1, 0, 1], Gradient Sample Values: [-0.021088455, 0.010724018, 0.0103644375, 0.008087001, 0.013961344]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.938] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [32, 3], GradOutput Shape [32, 3], GradInput Shape [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.938] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.3087848, 0.3626603, 0.32855487, 0.10828002, 0.65431446, Gradient Output Sample Values: [-0.021088455, 0.010724018, 0.0103644375, 0.008087001, 0.013961344], Gradient Input Sample Values: [-0.0067534707, 0.0036053332, 0.0031481371, 0.000358475, 0.0060098604]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.939] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.939] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0.028774317, 0, 0.03309678, Gradient Output Sample Values: [-0.0030188493, -0.00050078396, -0.00021526401, 0.00026833755, -0.0010734079], Gradient Input Sample Values: [0, 0, -0.00021526401, 0, -0.0010734079]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.952] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.952] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.0025325324, 0, 0.037483837, 0.025126882, Gradient Output Sample Values: [-0.0006109326, -0.0012630315, -0.0008319214, 0.00015536584, -0.0023021272], Gradient Input Sample Values: [0, -0.0012630315, 0, 0.00015536584, -0.0023021272]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:44:02.954] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 2/75 completed in 0.02s. Loss: 1.1340474
[Scope: Trainer, Epoch: 0, Batch: 2] [2024-10-07 04:44:02.955] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 2] [2024-10-07 04:44:02.955] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.015176873, 0.0025220437, -0.039642982, 0.037526265, 0.025225775], Output Sample Values: [0, 0.0025220437, 0, 0.037526265, 0.025225775]
[Scope: Trainer, Epoch: 0, Batch: 2] [2024-10-07 04:44:02.967] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 0, Batch: 2] [2024-10-07 04:44:02.968] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.093925014, -0.13823959, 0.028800389, -0.06319437, 0.03311229], Output Sample Values: [0, 0, 0.028800389, 0, 0.03311229]
[Scope: Trainer, Epoch: 0, Batch: 2] [2024-10-07 04:44:02.971] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 0, Batch: 2] [2024-10-07 04:44:02.971] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [-0.07578884, 0.08310424, -0.015097386, -1.0446388, 0.743847], Output Sample Values: [0.30913967, 0.36237743, 0.32848287, 0.10911191, 0.6525318]