find any calculation problems in this log:
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.935] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.935] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.935] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.935] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.935] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.935] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 232], [2024-10-06 05:31:55.936] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 233/240 completed in 0.01s. Loss: 1.008073
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.936] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.936] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.23302022, 0.4100607, 0.14050612, -0.39273798, 0.011359939], Output Sample Values: [0, 0.4100607, 0.14050612, 0, 0.011359939]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.032840706, 0.26477256, 0.58320665, 0.4470917, 0.2779417], Output Sample Values: [0.032840706, 0.26477256, 0.58320665, 0.4470917, 0.2779417]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.157932, 1.0065047, -1.9313436, 1.4969972, 1.3766525], Output Sample Values: [0.5249302, 0.45116714, 0.023902658, 0.52393144, 0.46452537]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.0270622]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.5249302, 0.45116714, 0.023902658, 0.52393144, 0.46452537], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4550447, 1.4566414, 1.4534007, 1.4554352, 1.4494102], Log Probabilities: [-0.93011457, -1.0038776, -1.4311421, -0.93271, -0.9921161], Product: [-0, -1.0038776, -0, -0.93271, -0], Loss Sum: [-10.270622], Loss: [10.270622], Mean Loss: [1.0270622]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.5249302, 0.45116714, 0.023902658, 0.52393144, 0.46452537, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [0.03945085, -0.06335443, 0.023903575, -0.06065141, 0.037079122]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.5249302, 0.45116714, 0.023902658, 0.52393144, 0.46452537, Gradient Output Sample Values: [0.03945085, -0.06335443, 0.023903575, -0.06065141, 0.037079122], Gradient Input Sample Values: [0.02454258, -0.025288504, 0.0007459233, -0.024294974, 0.023858028]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.938] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.032840706, 0.26477256, 0.58320665, 0.4470917, 0.2779417, Gradient Output Sample Values: [-0.005591077, -0.0012916429, 0.0010337112, 0.0042907083, -0.00032164954], Gradient Input Sample Values: [-0.005591077, -0.0012916429, 0.0010337112, 0.0042907083, -0.00032164954]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.942] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.942] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.4100607, 0.14050612, 0, 0.011359939, Gradient Output Sample Values: [-0.0018433122, -0.0017898388, -0.0021394673, 0.0012622854, 0.0030122572], Gradient Input Sample Values: [0, -0.0017898388, -0.0021394673, 0, 0.0030122572]
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.943] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.943] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.943] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.943] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.944] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.944] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 233], [2024-10-06 05:31:55.944] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 234/240 completed in 0.01s. Loss: 1.0270622
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.944] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.944] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.2320386, 0.40996647, 0.14089006, -0.39282435, 0.010675898], Output Sample Values: [0, 0.40996647, 0.14089006, 0, 0.010675898]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.039966427, 0.26648363, 0.5819309, 0.4441081, 0.2781577], Output Sample Values: [0.039966427, 0.26648363, 0.5819309, 0.4441081, 0.2781577]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.140576, 1.0209824, -1.9323949, 1.5610824, 1.310353], Output Sample Values: [0.51717955, 0.45888355, 0.023936855, 0.5558492, 0.43258017]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.0632287]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.51717955, 0.45888355, 0.023936855, 0.5558492, 0.43258017], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4548457, 1.4577513, 1.4537649, 1.4554402, 1.4494728], Log Probabilities: [-0.9376661, -0.99596214, -1.4309088, -0.9019021, -1.025171], Product: [-0, -0, -1.4309088, -0.9019021, -0], Loss Sum: [-10.632287], Loss: [10.632287], Mean Loss: [1.0632287]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.51717955, 0.45888355, 0.023936855, 0.5558492, 0.43258017, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [0.03915406, 0.03693679, -0.07609085, -0.0594203, 0.035873506]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.946] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.51717955, 0.45888355, 0.023936855, 0.5558492, 0.43258017, Gradient Output Sample Values: [0.03915406, 0.03693679, -0.07609085, -0.0594203, 0.035873506], Gradient Input Sample Values: [0.001952908, 0.00071530906, -0.0026682133, -0.023446938, 0.022975031]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.947] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.947] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.039966427, 0.26648363, 0.5819309, 0.4441081, 0.2781577, Gradient Output Sample Values: [-0.00012223402, -0.0007982011, 9.73155E-05, 0.0016349936, 0.0006161041], Gradient Input Sample Values: [-0.00012223402, -0.0007982011, 9.73155E-05, 0.0016349936, 0.0006161041]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.951] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.951] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.40996647, 0.14089006, 0, 0.010675898, Gradient Output Sample Values: [0.0009783629, -0.00043085063, 0.00055909815, 0.0020671014, -0.000652154], Gradient Input Sample Values: [0, -0.00043085063, 0.00055909815, 0, -0.000652154]
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.951] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.952] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.952] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.952] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.952] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.952] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 234], [2024-10-06 05:31:55.952] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 235/240 completed in 0.01s. Loss: 1.0632287
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.952] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.952] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.23268479, 0.41001967, 0.14081167, -0.3921801, 0.010527842], Output Sample Values: [0, 0.41001967, 0.14081167, 0, 0.010527842]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.954] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.954] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.03694147, 0.26562983, 0.5813263, 0.44207883, 0.27789968], Output Sample Values: [0.03694147, 0.26562983, 0.5813263, 0.44207883, 0.27789968]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.954] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.954] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.1116068, 1.0507497, -1.9368434, 1.5065805, 1.3742619], Output Sample Values: [0.50291944, 0.4732259, 0.023854665, 0.52693033, 0.46162355]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.954] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [0.99923754]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.954] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.50291944, 0.4732259, 0.023854665, 0.52693033, 0.46162355], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.454618, 1.4567261, 1.4532621, 1.4555272, 1.449357], Log Probabilities: [-0.95169854, -0.9813921, -1.4307634, -0.92979574, -0.9951025], Product: [-0, -0.9813921, -0, -0, -0.9951025], Loss Sum: [-9.992375], Loss: [9.992375], Mean Loss: [0.99923754]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.955] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.955] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.50291944, 0.4732259, 0.023854665, 0.52693033, 0.46162355, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [0.03860847, -0.0625211, 0.023912633, 0.039463434, -0.06303145]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.955] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.955] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.50291944, 0.4732259, 0.023854665, 0.52693033, 0.46162355, Gradient Output Sample Values: [0.03860847, -0.0625211, 0.023912633, 0.039463434, -0.06303145], Gradient Input Sample Values: [0.024244588, -0.025044, 0.00079941424, 0.025027078, -0.025388783]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.955] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.955] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.03694147, 0.26562983, 0.5813263, 0.44207883, 0.27789968, Gradient Output Sample Values: [-0.005489412, -0.0013167029, 0.00090042956, 0.004059731, -0.00047233922], Gradient Input Sample Values: [-0.005489412, -0.0013167029, 0.00090042956, 0.004059731, -0.00047233922]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.959] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.959] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.41001967, 0.14081167, 0, 0.010527842, Gradient Output Sample Values: [-0.0018324283, -0.0017965973, -0.0021012751, 0.0012371031, 0.003114034], Gradient Input Sample Values: [0, -0.0017965973, -0.0021012751, 0, 0.003114034]
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.959] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.960] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.961] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.961] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.961] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.961] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 235], [2024-10-06 05:31:55.961] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 236/240 completed in 0.01s. Loss: 0.99923754
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.962] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.962] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.23197684, 0.41041428, 0.14129731, -0.39247364, 0.0104774665], Output Sample Values: [0, 0.41041428, 0.14129731, 0, 0.0104774665]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.037104934, 0.2663921, 0.5818598, 0.44414997, 0.27819616], Output Sample Values: [0.037104934, 0.2663921, 0.5818598, 0.44414997, 0.27819616]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.1351514, 1.0346931, -1.9439375, 1.5197072, 1.3653115], Output Sample Values: [0.512709, 0.46370575, 0.023585202, 0.53241056, 0.45624012]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [0.9813653]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.512709, 0.46370575, 0.023585202, 0.53241056, 0.45624012], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4548006, 1.456887, 1.4535909, 1.4554782, 1.4494497], Log Probabilities: [-0.9420916, -0.9910948, -1.4312154, -0.92447644, -1.0006468], Product: [-0, -0.9910948, -0, -0.92447644, -0], Loss Sum: [-9.813653], Loss: [9.813653], Mean Loss: [0.9813653]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.512709, 0.46370575, 0.023585202, 0.53241056, 0.45624012, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [0.038981166, -0.062883, 0.023901824, -0.060326092, 0.036764152]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.512709, 0.46370575, 0.023585202, 0.53241056, 0.45624012, Gradient Output Sample Values: [0.038981166, -0.062883, 0.023901824, -0.060326092, 0.036764152], Gradient Input Sample Values: [0.024400154, -0.025166942, 0.0007667857, -0.0240908, 0.023652267]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.964] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.037104934, 0.2663921, 0.5818598, 0.44414997, 0.27819616, Gradient Output Sample Values: [-0.0055472623, -0.001295379, 0.00094192865, 0.004336085, -0.0002708229], Gradient Input Sample Values: [-0.0055472623, -0.001295379, 0.00094192865, 0.004336085, -0.0002708229]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.968] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.968] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.41041428, 0.14129731, 0, 0.0104774665, Gradient Output Sample Values: [-0.001833963, -0.0016697331, -0.0020524657, 0.0013726233, 0.003036013], Gradient Input Sample Values: [0, -0.0016697331, -0.0020524657, 0, 0.003036013]
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.969] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.969] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.969] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.969] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.970] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.970] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 236], [2024-10-06 05:31:55.970] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 237/240 completed in 0.01s. Loss: 0.9813653
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.970] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.970] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.23131332, 0.41067743, 0.14166336, -0.39207637, 0.009787904], Output Sample Values: [0, 0.41067743, 0.14166336, 0, 0.009787904]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.044671625, 0.26586843, 0.581968, 0.4456286, 0.27879274], Output Sample Values: [0.044671625, 0.26586843, 0.581968, 0.4456286, 0.27879274]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.1487058, 1.0223053, -1.9443715, 1.5966918, 1.291309], Output Sample Values: [0.5190427, 0.45741245, 0.023544893, 0.5692654, 0.41945824]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [0.9951415]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.5190427, 0.45741245, 0.023544893, 0.5692654, 0.41945824], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4549391, 1.4584861, 1.4541633, 1.4555182, 1.4496193], Log Probabilities: [-0.93589646, -0.99752665, -1.4313942, -0.88922065, -1.0390278], Product: [-0.93589646, -0, -0, -0.88922065, -0], Loss Sum: [-9.951415], Loss: [9.951415], Mean Loss: [0.9951415]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.5190427, 0.45741245, 0.023544893, 0.5692654, 0.41945824, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [-0.06077659, 0.03687904, 0.023897547, -0.058902413, 0.035379842]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.972] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.5190427, 0.45741245, 0.023544893, 0.5692654, 0.41945824, Gradient Output Sample Values: [-0.06077659, 0.03687904, 0.023897547, -0.058902413, 0.035379842], Gradient Input Sample Values: [-0.024219852, 0.023324871, 0.0008949789, -0.02304211, 0.022569092]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.973] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.973] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.044671625, 0.26586843, 0.581968, 0.4456286, 0.27879274, Gradient Output Sample Values: [0.005257299, 0.0017826894, -0.00081496185, -0.005347704, -0.0001139879], Gradient Input Sample Values: [0.005257299, 0.0017826894, -0.00081496185, -0.005347704, -0.0001139879]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.976] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.977] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.41067743, 0.14166336, 0, 0.009787904, Gradient Output Sample Values: [0.00089218037, 0.0018508631, 0.0012876412, -0.0030154856, -0.0025242646], Gradient Input Sample Values: [0, 0.0018508631, 0.0012876412, 0, -0.0025242646]
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.977] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.978] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.978] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.978] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.978] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.978] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 237], [2024-10-06 05:31:55.978] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 238/240 completed in 0.01s. Loss: 0.9951415
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.979] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.979] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.23076278, 0.4108164, 0.14149079, -0.3917248, 0.009864589], Output Sample Values: [0, 0.4108164, 0.14149079, 0, 0.009864589]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.039869472, 0.26287308, 0.5833247, 0.45337683, 0.27900365], Output Sample Values: [0.039869472, 0.26287308, 0.5833247, 0.45337683, 0.27900365]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.213935, 0.9670367, -1.947884, 1.6260722, 1.2707523], Output Sample Values: [0.54837525, 0.4284018, 0.023222946, 0.58138835, 0.40752372]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.1303813]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.54837525, 0.4284018, 0.023222946, 0.58138835, 0.40752372], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4559922, 1.4592569, 1.4549598, 1.4560584, 1.449877], Log Probabilities: [-0.907617, -1.0275904, -1.4327693, -0.87786853, -1.0517331], Product: [-0, -1.0275904, -0, -0.87786853, -0], Loss Sum: [-11.303813], Loss: [11.303813], Mean Loss: [1.1303813]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.54837525, 0.4284018, 0.023222946, 0.58138835, 0.40752372, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [0.040348463, -0.06421317, 0.023864713, -0.058433197, 0.03493318]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.54837525, 0.4284018, 0.023222946, 0.58138835, 0.40752372, Gradient Output Sample Values: [0.040348463, -0.06421317, 0.023864713, -0.058433197, 0.03493318], Gradient Input Sample Values: [0.024774054, -0.025440402, 0.0006663462, -0.022649428, 0.022172915]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.981] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.039869472, 0.26287308, 0.5833247, 0.45337683, 0.27900365, Gradient Output Sample Values: [-0.0055739875, -0.0012850533, 0.0010293941, 0.0049825003, -7.10038E-05], Gradient Input Sample Values: [-0.0055739875, -0.0012850533, 0.0010293941, 0.0049825003, -7.10038E-05]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.985] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.985] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.4108164, 0.14149079, 0, 0.009864589, Gradient Output Sample Values: [-0.0014864053, -0.0015644361, -0.0017023727, 0.002062614, 0.0030516023], Gradient Input Sample Values: [0, -0.0015644361, -0.0017023727, 0, 0.0030516023]
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.986] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.986] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.986] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.986] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.987] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.987] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 238], [2024-10-06 05:31:55.987] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 239/240 completed in 0.01s. Loss: 1.1303813
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.987] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.987] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.23075965, 0.4109352, 0.14171407, -0.39217788, 0.009225532], Output Sample Values: [0, 0.4109352, 0.14171407, 0, 0.009225532]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.04202798, 0.26399538, 0.58255345, 0.45175624, 0.27870595], Output Sample Values: [0.04202798, 0.26399538, 0.58255345, 0.45175624, 0.27870595]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [1.2170048, 0.9553305, -1.9413342, 1.6858869, 1.1975286], Output Sample Values: [0.5517978, 0.4247528, 0.023449348, 0.6127838, 0.37602434]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.073683]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.5517978, 0.4247528, 0.023449348, 0.6127838, 0.37602434], Target Sample: [1, 0, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4561261, 1.4617031, 1.4556551, 1.4562886, 1.4500355], Log Probabilities: [-0.9043283, -1.0313733, -1.4326768, -0.8489193, -1.0856787], Product: [-0, -1.0313733, -0, -0.8489193, -0], Loss Sum: [-10.73683], Loss: [10.73683], Mean Loss: [1.073683]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.5517978, 0.4247528, 0.023449348, 0.6127838, 0.37602434, Gradient Output Sample Values: [1, 0, 0, 0, 1], Gradient Sample Values: [0.04048137, -0.064348295, 0.023866922, -0.057212293, 0.03376725]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.5517978, 0.4247528, 0.023449348, 0.6127838, 0.37602434, Gradient Output Sample Values: [0.04048137, -0.064348295, 0.023866922, -0.057212293, 0.03376725], Gradient Input Sample Values: [0.024784712, -0.025448373, 0.0006636598, -0.021516819, 0.021007095]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.989] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.04202798, 0.26399538, 0.58255345, 0.45175624, 0.27870595, Gradient Output Sample Values: [-0.005511733, -0.0013174361, 0.00094674836, 0.005182734, -5.093265E-05], Gradient Input Sample Values: [-0.005511733, -0.0013174361, 0.00094674836, 0.005182734, -5.093265E-05]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.993] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.993] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.4109352, 0.14171407, 0, 0.009225532, Gradient Output Sample Values: [-0.001322697, -0.0015109681, -0.0013724387, 0.002462835, 0.003086701], Gradient Input Sample Values: [0, -0.0015109681, -0.0013724387, 0, 0.003086701]
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.994] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.995] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.995] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.995] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.995] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.995] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 1, Batch: 239], [2024-10-06 05:31:55.995] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2, Batch 240/240 completed in 0.01s. Loss: 1.073683
[2024-10-06 05:31:55.996] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:55.996] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:55.998] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:55.998] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:55.998] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:55.998] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:55.998] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:55.998] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:55.998] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:55.998] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.000] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.000] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.001] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.001] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.001] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.001] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.002] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.002] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.004] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.004] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.004] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.004] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.004] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.004] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.004] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.004] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.006] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.006] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.006] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.006] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.006] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.006] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.006] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.006] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.008] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.008] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.009] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.009] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.009] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.009] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.009] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.009] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.011] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.011] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.011] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.011] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.011] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.011] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.011] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.011] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.013] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.013] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.013] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.013] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.013] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.013] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.014] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.014] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.016] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.016] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.016] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.016] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.016] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.016] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.016] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.016] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.018] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.018] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.018] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.018] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.018] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.018] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.018] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.018] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.020] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.020] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.020] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.020] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.020] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.020] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.021] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.021] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.023] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.023] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.023] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.023] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.023] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.023] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.023] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.023] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.025] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.025] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.025] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.025] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.025] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.025] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.025] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.025] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.027] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.027] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.027] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.027] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.028] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.028] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.028] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.028] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.030] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.030] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.030] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.030] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.030] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.030] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.030] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.030] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.032] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.032] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.032] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.032] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.032] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.032] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.033] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.033] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.035] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.035] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.035] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.035] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.035] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.035] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.035] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.035] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.037] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.037] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.037] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.037] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.037] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.037] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.037] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.037] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.039] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.039] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.039] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.039] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.039] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.039] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.040] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.040] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.042] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.042] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.042] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.042] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.042] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.042] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.042] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.042] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.044] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.044] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.044] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.044] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.044] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.044] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.044] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.044] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.046] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.046] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.046] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.047] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.047] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.047] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.047] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.047] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.049] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.049] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.049] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.049] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.049] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.049] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.049] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.049] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.051] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.051] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.051] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.051] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.051] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.051] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.052] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.052] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.053] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.053] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.054] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.054] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.054] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.054] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.054] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.054] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.056] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.056] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.056] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.056] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.056] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.056] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.056] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.056] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.058] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.058] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.058] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.058] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.058] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.058] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.059] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.059] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.061] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.061] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.061] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.061] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.061] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.061] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.061] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.061] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.063] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.063] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.063] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.063] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.063] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.063] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.063] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.063] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.065] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.065] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.065] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.065] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.065] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.065] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.066] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.066] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.067] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.067] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.068] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.068] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.068] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.068] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.068] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.068] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.070] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.070] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.070] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.070] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.070] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.070] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.072] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.072] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.074] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.074] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.074] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.074] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.074] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.074] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.074] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.074] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.076] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.076] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.076] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.076] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.076] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.076] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.076] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.076] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.078] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.078] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.078] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.078] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.078] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.078] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.079] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.079] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.081] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.081] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.081] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.081] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.081] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.081] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.081] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.081] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.083] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.083] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.083] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.083] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.083] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.083] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.083] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.083] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.085] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.085] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.085] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.085] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.085] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.085] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.086] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.086] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.088] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.088] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.088] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.088] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.088] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.088] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.088] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.088] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.090] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.090] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.090] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.090] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.090] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.090] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.090] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.090] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.092] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.092] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.092] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.092] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.092] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.092] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.093] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.093] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.094] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.094] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.095] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.095] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.095] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.095] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.095] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.095] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.097] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.097] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.097] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.097] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.097] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.097] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.097] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.097] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.099] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.099] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.099] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.099] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.099] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.099] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.099] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.099] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.101] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.101] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.101] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.101] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.101] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.101] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.102] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.102] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.104] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.104] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.104] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.104] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.104] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.104] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.104] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.104] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.106] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.106] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.106] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.106] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.106] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.106] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.106] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.107] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.108] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.108] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.109] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.109] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.109] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.109] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.109] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.109] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.111] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.111] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.111] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.111] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.111] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.111] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.111] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.111] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.113] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.113] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.113] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.113] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.113] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.114] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.114] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.114] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.116] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.116] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.116] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.116] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.116] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.116] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.116] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.116] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.118] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.118] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.118] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.118] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.118] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.118] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.119] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.119] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.120] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.120] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.121] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.121] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.121] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.121] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.121] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.121] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.123] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.123] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.123] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.123] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.123] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.123] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.123] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.123] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.125] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.125] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.125] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.125] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.125] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.125] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.126] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.126] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.128] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.128] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.128] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.128] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.128] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.128] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.128] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.128] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.130] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.130] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.130] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.130] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.130] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.130] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.130] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.130] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.132] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.132] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.132] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.132] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.132] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.132] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.133] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.133] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.135] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.135] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.135] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.135] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.135] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.135] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.135] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.135] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.137] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.137] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.137] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.137] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.137] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.137] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.137] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.138] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.018626913, -0.027019387, 0.06136144, 0.08258093, -0.027377957], Output Sample Values: [0.018626913, 0, 0.06136144, 0.08258093, 0]
[2024-10-06 05:31:56.139] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[2024-10-06 05:31:56.139] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.015387267, -0.112509936, -0.0039392267, 0.28752798, 0.008911286], Output Sample Values: [0.015387267, 0, 0, 0.28752798, 0.008911286]
[2024-10-06 05:31:56.140] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[2024-10-06 05:31:56.140] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.68080693, 0.65545684, -1.1318718, 0.68144953, 0.6549086], Output Sample Values: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976]
[2024-10-06 05:31:56.140] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.140] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4676864, 0.45597953, 0.07633406, 0.4679461, 0.45568976], Target Sample: [1, 0, 0, 1, 0], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4477041, 1.4477017, 1.4477062, 1.4477131, 1.4477175], Log Probabilities: [-0.98001766, -0.99172455, -1.3713701, -0.9797556, -0.9920119], Product: [-0, -0, -1.3713701, -0, -0], Loss Sum: [-13.71548], Loss: [13.71548], Mean Loss: [1.3715479]
[2024-10-06 05:31:56.140] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2/3, Training Loss: 1.049634, Validation Loss: 1.3715485
[2024-10-06 05:31:56.140] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_2.ckpt' with Validation Loss: 1.3715485
[2024-10-06 05:31:56.141] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 2 completed in 2.25s, Average Training Loss: 1.0496337, Min Loss: 0.9289069, Max Loss: 1.2103611.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.143] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.143] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5723188, -0.764257, 0.80748814, 1.2464867, 0.05129438], Output Sample Values: [0.5723188, 0, 0.80748814, 1.2464867, 0.05129438]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.145] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.145] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5979729, -1.5918076, -1.1592599, 1.2069252, -0.63265187], Output Sample Values: [0.5979729, 0, 0, 1.2069252, 0]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.145] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.145] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [2.876022, 2.3349235, -4.109117, 2.5689754, 1.8644191], Output Sample Values: [0.6316984, 0.36771694, 0.0005846588, 0.66868186, 0.33054808]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.145] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.0326538]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.145] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.6316984, 0.36771694, 0.0005846588, 0.66868186, 0.33054808], Target Sample: [0, 1, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4646018, 1.4688334, 1.4614358, 1.4574066, 1.4596546], Log Probabilities: [-0.8329033, -1.0968848, -1.4640172, -0.8001516, -1.1382854], Product: [-0, -1.0968848, -0, -0, -1.1382854], Loss Sum: [-10.326538], Loss: [10.326538], Mean Loss: [1.0326538]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.146] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.146] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.6316984, 0.36771694, 0.0005846588, 0.66868186, 0.33054808, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.04347851, -0.06660904, 0.023130523, 0.044926085, -0.06796321]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.146] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.146] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.6316984, 0.36771694, 0.0005846588, 0.66868186, 0.33054808, Gradient Output Sample Values: [0.04347851, -0.06660904, 0.023130523, 0.044926085, -0.06796321], Gradient Input Sample Values: [0.025579333, -0.025591113, 1.1777931E-05, 0.024963362, -0.024975253]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.146] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.146] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5979729, 0, 0, 1.2069252, 0, Gradient Output Sample Values: [-0.005646259, -0.0017370783, 0.0006871451, 0.005037689, -0.00025869222], Gradient Input Sample Values: [-0.005646259, 0, 0, 0.005037689, 0]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.150] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.150] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5723188, 0, 0.80748814, 1.2464867, 0.05129438, Gradient Output Sample Values: [-0.0008984067, 0.00097274303, 0.00085801, 0.0018570311, -0.003542453], Gradient Input Sample Values: [-0.0008984067, 0, 0.00085801, 0.0018570311, -0.003542453]
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.150] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.151] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.151] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.151] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.152] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.152] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 0], [2024-10-06 05:31:56.152] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 1/240 completed in 0.01s. Loss: 1.0326538
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.152] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.152] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5730739, -0.76421773, 0.80726784, 1.2473724, 0.05237145], Output Sample Values: [0.5730739, 0, 0.80726784, 1.2473724, 0.05237145]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.6084979, -1.5912695, -1.1605934, 1.2017176, -0.6311731], Output Sample Values: [0.6084979, 0, 0, 1.2017176, 0]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [2.8513422, 2.3836808, -4.1250424, 2.555392, 1.8852152], Output Sample Values: [0.6144774, 0.38494894, 0.0005737207, 0.66103476, 0.33819735]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.0402265]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.6144774, 0.38494894, 0.0005737207, 0.66103476, 0.33819735], Target Sample: [0, 1, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4629784, 1.4678699, 1.4612224, 1.4574659, 1.45892], Log Probabilities: [-0.84850097, -1.0780294, -1.4624046, -0.8068351, -1.1296725], Product: [-0.84850097, -0, -0, -0, -1.1296725], Loss Sum: [-10.402265], Loss: [10.402265], Mean Loss: [1.0402265]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.6144774, 0.38494894, 0.0005737207, 0.66103476, 0.33819735, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [-0.05719439, 0.03402654, 0.02316785, 0.04462682, -0.067686096]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.154] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.6144774, 0.38494894, 0.0005737207, 0.66103476, 0.33819735, Gradient Output Sample Values: [-0.05719439, 0.03402654, 0.02316785, 0.04462682, -0.067686096], Gradient Input Sample Values: [-0.021605948, 0.021580016, 2.5932599E-05, 0.025119647, -0.025132263]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.155] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.155] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.6084979, 0, 0, 1.2017176, 0, Gradient Output Sample Values: [0.0048220265, 0.001444754, -0.0005905782, -0.0042933356, 0.00027626797], Gradient Input Sample Values: [0.0048220265, 0, 0, -0.0042933356, 0]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.159] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.159] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5730739, 0, 0.80726784, 1.2473724, 0.05237145, Gradient Output Sample Values: [0.0008088915, -0.0008320494, -0.0005600803, -0.0015186239, 0.0029501084], Gradient Input Sample Values: [0.0008088915, 0, -0.0005600803, -0.0015186239, 0.0029501084]
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.160] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.160] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.160] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.160] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.161] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.161] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 1], [2024-10-06 05:31:56.161] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 2/240 completed in 0.01s. Loss: 1.0402265
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.161] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.161] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.57263064, -0.7640801, 0.8075253, 1.2481315, 0.051365387], Output Sample Values: [0.57263064, 0, 0.8075253, 1.2481315, 0.051365387]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5837134, -1.5919633, -1.1599953, 1.2194341, -0.63063776], Output Sample Values: [0.5837134, 0, 0, 1.2194341, 0]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [3.058043, 2.1725972, -4.1053066, 2.6985903, 1.7353615], Output Sample Values: [0.7075615, 0.2918905, 0.0005479753, 0.723237, 0.2760296]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [0.9808301]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.7075615, 0.2918905, 0.0005479753, 0.723237, 0.2760296], Target Sample: [0, 1, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4744294, 1.4769835, 1.4666624, 1.4598494, 1.4577891], Log Probabilities: [-0.7668679, -1.1825389, -1.4738814, -0.75374657, -1.200954], Product: [-0, -1.1825389, -0, -0.75374657, -0], Loss Sum: [-9.808301], Loss: [9.808301], Mean Loss: [0.9808301]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.7075615, 0.2918905, 0.0005479753, 0.723237, 0.2760296, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.046446558, -0.06935004, 0.02290348, -0.05293999, 0.0300907]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.163] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.7075615, 0.2918905, 0.0005479753, 0.723237, 0.2760296, Gradient Output Sample Values: [0.046446558, -0.06935004, 0.02290348, -0.05293999, 0.0300907], Gradient Input Sample Values: [0.023924656, -0.023930283, 5.6275703E-06, -0.016616018, 0.016577281]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.164] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.164] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5837134, 0, 0, 1.2194341, 0, Gradient Output Sample Values: [-0.005247375, -0.001549771, 0.00066888076, 0.0051200073, -0.0002467892], Gradient Input Sample Values: [-0.005247375, 0, 0, 0.0051200073, 0]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.168] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.168] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.57263064, 0, 0.8075253, 1.2481315, 0.051365387, Gradient Output Sample Values: [-0.00057998317, 0.0008865646, 0.0010446045, 0.0022397584, -0.0033038189], Gradient Input Sample Values: [-0.00057998317, 0, 0.0010446045, 0.0022397584, -0.0033038189]
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.168] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.169] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.169] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.169] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.169] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.169] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 2], [2024-10-06 05:31:56.169] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 3/240 completed in 0.01s. Loss: 0.9808301
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.169] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.169] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5725306, -0.7639671, 0.8072283, 1.2470134, 0.05240746], Output Sample Values: [0.5725306, 0, 0.8072283, 1.2470134, 0.05240746]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.171] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.171] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.6079049, -1.5932251, -1.1600839, 1.1979444, -0.6315188], Output Sample Values: [0.6079049, 0, 0, 1.1979444, 0]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.171] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.171] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [2.9296725, 2.2934117, -4.129691, 2.8464417, 1.5981565], Output Sample Values: [0.65354043, 0.3458979, 0.00056160364, 0.7764723, 0.22284481]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.0226133]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.65354043, 0.3458979, 0.00056160364, 0.7764723, 0.22284481], Target Sample: [0, 1, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4669878, 1.4870669, 1.4745317, 1.4642799, 1.4602584], Log Probabilities: [-0.8134474, -1.1210899, -1.4664263, -0.7105946, -1.264222], Product: [-0, -0, -1.4664263, -0, -1.264222], Loss Sum: [-10.226132], Loss: [10.226132], Mean Loss: [1.0226133]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.65354043, 0.3458979, 0.00056160364, 0.7764723, 0.22284481, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.044332705, 0.032592434, -0.076925136, 0.049135193, -0.07175411]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.65354043, 0.3458979, 0.00056160364, 0.7764723, 0.22284481, Gradient Output Sample Values: [0.044332705, 0.032592434, -0.076925136, 0.049135193, -0.07175411], Gradient Input Sample Values: [0.0026984927, -0.00263271, -6.577996E-05, 0.020931879, -0.020932179]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.172] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.6079049, 0, 0, 1.1979444, 0, Gradient Output Sample Values: [-0.0005954936, -0.00018529582, 7.776106E-05, 0.0006497987, 3.5544086E-05], Gradient Input Sample Values: [-0.0005954936, 0, 0, 0.0006497987, 0]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.176] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.176] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5725306, 0, 0.8072283, 1.2470134, 0.05240746, Gradient Output Sample Values: [-6.158547E-05, 8.359678E-05, 0.00015896774, 0.0002861653, -0.00036385647], Gradient Input Sample Values: [-6.158547E-05, 0, 0.00015896774, 0.0002861653, -0.00036385647]
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 3], [2024-10-06 05:31:56.177] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 4/240 completed in 0.01s. Loss: 1.0226133
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.178] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.178] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.57270366, -0.76389694, 0.8072574, 1.2475333, 0.052618314], Output Sample Values: [0.57270366, 0, 0.8072574, 1.2475333, 0.052618314]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.60324943, -1.5933105, -1.1603566, 1.2012162, -0.63083905], Output Sample Values: [0.60324943, 0, 0, 1.2012162, 0]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [2.9755676, 2.262673, -4.1365166, 2.8596783, 1.5963659], Output Sample Values: [0.67067355, 0.3287797, 0.0005467291, 0.7790738, 0.22025664]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.0872171]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.67067355, 0.3287797, 0.0005467291, 0.7790738, 0.22025664], Target Sample: [0, 1, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4691116, 1.4876134, 1.4753227, 1.4652705, 1.4621501], Log Probabilities: [-0.798438, -1.1403319, -1.4685649, -0.70853966, -1.2673568], Product: [-0.798438, -0, -0, -0, -1.2673568], Loss Sum: [-10.872171], Loss: [10.872171], Mean Loss: [1.0872171]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.67067355, 0.3287797, 0.0005467291, 0.7790738, 0.22025664, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [-0.05499686, 0.03197129, 0.02302557, 0.049236264, -0.071842514]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.67067355, 0.3287797, 0.0005467291, 0.7790738, 0.22025664, Gradient Output Sample Values: [-0.05499686, 0.03197129, 0.02302557, 0.049236264, -0.071842514], Gradient Input Sample Values: [-0.019205423, 0.01917842, 2.7000986E-05, 0.020790549, -0.020790583]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.180] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.60324943, 0, 0, 1.2012162, 0, Gradient Output Sample Values: [0.004289544, 0.00117436, -0.00055536657, -0.0044808527, -9.601953E-05], Gradient Input Sample Values: [0.004289544, 0, 0, -0.0044808527, 0]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.184] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.184] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.57270366, 0, 0.8072574, 1.2475333, 0.052618314, Gradient Output Sample Values: [0.0005388062, -0.00071927556, -0.0008619785, -0.0019668485, 0.0026226195], Gradient Input Sample Values: [0.0005388062, 0, -0.0008619785, -0.0019668485, 0.0026226195]
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.185] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.185] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.186] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.186] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.186] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.186] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 4], [2024-10-06 05:31:56.186] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 5/240 completed in 0.01s. Loss: 1.0872171
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.186] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.186] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5714961, -0.7638101, 0.8074793, 1.2472665, 0.051936958], Output Sample Values: [0.5714961, 0, 0.8074793, 1.2472665, 0.051936958]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [10, 64], Output Shape: [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.5892841, -1.5943182, -1.1591113, 1.2030165, -0.63151276], Output Sample Values: [0.5892841, 0, 0, 1.2030165, 0]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [10, 3], Output Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [2.950369, 2.2805421, -4.123214, 2.6596556, 1.7969496], Output Sample Values: [0.6610939, 0.33834603, 0.00056007376, 0.7027107, 0.29655698]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Mean Loss: [1.1668577]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.6610939, 0.33834603, 0.00056007376, 0.7027107, 0.29655698], Target Sample: [0, 1, 0, 0, 1], Batch Size: [10], Epsilon: [1E-12], LogSumExp: [1.4678965, 1.4736573, 1.4660919, 1.4611261, 1.459167], Log Probabilities: [-0.8068026, -1.1295505, -1.4673364, -0.77094656, -1.1771003], Product: [-0, -0, -1.4673364, -0, -1.1771003], Loss Sum: [-11.668577], Loss: [11.668577], Mean Loss: [1.1668577]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [10, 3], Target Shape: [10, 3], Gradient Shape: [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.6610939, 0.33834603, 0.00056007376, 0.7027107, 0.29655698, Gradient Output Sample Values: [0, 1, 0, 0, 1], Gradient Sample Values: [0.044628274, 0.032317854, -0.076946124, 0.0462575, -0.069182895]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [10, 3], GradOutput Shape [10, 3], GradInput Shape [10, 3]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.188] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.6610939, 0.33834603, 0.00056007376, 0.7027107, 0.29655698, Gradient Output Sample Values: [0.044628274, 0.032317854, -0.076946124, 0.0462575, -0.069182895], Gradient Input Sample Values: [0.0027985894, -0.002732871, -6.571969E-05, 0.024069065, -0.02407706]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.189] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.189] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5892841, 0, 0, 1.2030165, 0, Gradient Output Sample Values: [-0.0006104475, -0.00018944468, 8.057834E-05, 0.0006281785, -6.069379E-06], Gradient Input Sample Values: [-0.0006104475, 0, 0, 0.0006281785, 0]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.193] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [10, 64], GradOutput Shape [10, 64], GradInput Shape [10, 64]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.193] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.5714961, 0, 0.8074793, 1.2472665, 0.051936958, Gradient Output Sample Values: [-6.6428016E-05, 6.6540655E-05, 0.00014907056, 0.00027769638, -0.00037639885], Gradient Input Sample Values: [-6.6428016E-05, 0, 0.00014907056, 0.00027769638, -0.00037639885]
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.193] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.194] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.194] [Information] HyperAI.Core.Layers.DenseLayer: Weights updated for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.194] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer1'.
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.194] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'HiddenLayer2'.
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.194] [Information] HyperAI.Core.Layers.DenseLayer: Gradients cleared for layer 'OutputLayer'.
[Scope: Trainer, Epoch: 2, Batch: 5], [2024-10-06 05:31:56.194] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 3, Batch 6/240 completed in 0.01s. Loss: 1.1668577