can you find calculation errors in this log:
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.759] [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 04:22:49.766] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.15403008, -0.8304818, -0.062918544, -1.1866908, -1.0239053], Output Sample Values: [0.15403008, 0, 0, 0, 0]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.773] [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 04:22:49.773] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.118830174, -1.7562406, -0.1490201, 0.40583697, 0.58482033], Output Sample Values: [0, 0, 0, 0.40583697, 0.58482033]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.783] [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 04:22:49.783] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.4795514, 0.054449026, 0.11971915, 0.3736572, -0.36198485], Output Sample Values: [0.42526117, 0.27799442, 0.2967444, 0.4834637, 0.23167469]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.788] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.0908052]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.789] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.42526117, 0.27799442, 0.2967444, 0.4834637, 0.23167469], Target Sample: [0, 1, 0, 0, 1], Batch Size: [32], Epsilon: [1E-12], LogSumExp: [1.4341174, 1.4379303, 1.4513799, 1.4344172, 1.4337159], Log Probabilities: [-1.0088563, -1.156123, -1.137373, -0.95446664, -1.2062557], Product: [-0, -1.156123, -0, -0, -1.2062557], Loss Sum: [-34.905766], Loss: [34.905766], Mean Loss: [1.0908052]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.792] [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 04:22:49.792] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.42526117, 0.27799442, 0.2967444, 0.4834637, 0.23167469, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.011394868, -0.021415502, 0.010020633, 0.012031795, -0.021896377]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.793] [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 04:22:49.793] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.42526117, 0.27799442, 0.2967444, 0.4834637, 0.23167469, Gradient Output Sample Values: [0.011394868, -0.021415502, 0.010020633, 0.012031795, -0.021896377], Gradient Input Sample Values: [0.0040522693, -0.00647212, 0.0024198503, 0.004098639, -0.0058962405]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.801] [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 04:22:49.801] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0, 0.40583697, 0.58482033, Gradient Output Sample Values: [0.0006327063, 0.0014819971, -0.002439248, 0.0009327347, -0.001026985], Gradient Input Sample Values: [0, 0, 0, 0.0009327347, -0.001026985]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.817] [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 04:22:49.817] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.15403008, 0, 0, 0, 0, Gradient Output Sample Values: [0.0010515177, -0.0013557285, 2.8766088E-05, 0.001863194, 0.0010863426], Gradient Input Sample Values: [0.0010515177, 0, 0, 0, 0]
[Scope: Trainer, Epoch: 0, Batch: 0] [2024-10-07 04:22:49.834] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 1/75 completed in 0.10s. Loss: 1.0908052
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.837] [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 04:22:49.837] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.15403207, -0.8305405, -0.06287999, -1.1866478, -1.0239398], Output Sample Values: [0.15403207, 0, 0, 0, 0]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.844] [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 04:22:49.844] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.119232915, -1.756222, -0.14917085, 0.40634358, 0.5848361], Output Sample Values: [0, 0, 0, 0.40634358, 0.5848361]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.844] [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 04:22:49.844] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.4826387, 0.05517688, 0.1151419, 0.37482718, -0.3597826], Output Sample Values: [0.4265069, 0.27815175, 0.29534134, 0.48398936, 0.23216611]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.845] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.101775]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.845] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4265069, 0.27815175, 0.29534134, 0.48398936, 0.23216611], Target Sample: [0, 1, 0, 0, 1], Batch Size: [32], Epsilon: [1E-12], LogSumExp: [1.4341719, 1.437959, 1.4516301, 1.4345124, 1.4337946], Log Probabilities: [-1.007665, -1.1560202, -1.1388305, -0.9539696, -1.2057929], Product: [-0, -1.1560202, -0, -0.9539696, -0], Loss Sum: [-35.2568], Loss: [35.2568], Mean Loss: [1.101775]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.845] [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 04:22:49.845] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.4265069, 0.27815175, 0.29534134, 0.48398936, 0.23216611, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.011408451, -0.021414492, 0.010006039, -0.019212224, 0.0093579525]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.845] [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 04:22:49.845] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.4265069, 0.27815175, 0.29534134, 0.48398936, 0.23216611, Gradient Output Sample Values: [0.011408451, -0.021414492, 0.010006039, -0.019212224, 0.0093579525], Gradient Input Sample Values: [0.00407056, -0.006475093, 0.0024045326, -0.0072034034, 0.0031776074]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.846] [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 04:22:49.846] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0, 0.40634358, 0.5848361, Gradient Output Sample Values: [0.0006294261, 0.0014773427, -0.0024375382, 0.00093964604, -0.0010274127], Gradient Input Sample Values: [0, 0, 0, 0.00093964604, -0.0010274127]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.858] [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 04:22:49.858] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.15403207, 0, 0, 0, 0, Gradient Output Sample Values: [0.0010507181, -0.0013675282, 2.9705177E-05, 0.001869203, 0.0010847691], Gradient Input Sample Values: [0.0010507181, 0, 0, 0, 0]
[Scope: Trainer, Epoch: 0, Batch: 1] [2024-10-07 04:22:49.861] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 2/75 completed in 0.02s. Loss: 1.101775