find calculation erros in the following log:
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.228] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.40635723, 0.3592378, 0.23440501, 0.3172442, 0.50026417], Probabilities Sample: [0.3576547, 0.3411931, 0.3011522, 0.32522753, 0.39054573], Log Probabilities Sample: [-1.0281873, -1.0753067, -1.2001395, -1.1232302, -0.9402102], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.0281873, 0.9402102, 1.0332217, 0.9816357, 1.0331892] Loss Sum: [34.19414], Mean Loss: [1.0685669]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.228] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.228] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.40635723, 0.3592378, 0.23440501, 0.3172442, 0.50026417, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [-0.02007329, 0.010662285, 0.009411006, 0.01016336, -0.019045446]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.228] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [32, 3], GradOutput Shape [32, 3], GradInput Shape [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.228] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.40635723, 0.3592378, 0.23440501, 0.3172442, 0.50026417, Gradient Output Sample Values: [-0.02007329, 0.010662285, 0.009411006, 0.01016336, -0.019045446], Gradient Input Sample Values: [-0.007295188, 0.0045921113, 0.0027030767, 0.0047097886, -0.0071852272]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.229] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.229] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.064368285, 0.033318497, 0, 0, 0.0015775437, Gradient Output Sample Values: [-0.0014439067, -2.1210551E-05, -0.0008918388, 0.00042566494, -0.0015418255], Gradient Input Sample Values: [-0.0014439067, -2.1210551E-05, 0, 0, -0.0015418255]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.241] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.241] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0, 0.055914827, 0, Gradient Output Sample Values: [-0.00021959632, -0.001623522, -0.0011906411, 0.00044034215, 0.0008426177], Gradient Input Sample Values: [0, 0, 0, 0.00044034215, 0]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.30782443, -1.2601463, -0.47012058, 0.1013604, -0.65020823], Gradients Sample Values: [0.0022484346, -0.0024247295, 0.00022137872, 0.0011128555, -0.00055315084], Scaled Gradients Sample Values: [2.2484346E-05, -2.4247294E-05, 2.2137872E-06, 1.1128554E-05, -5.531508E-06]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.016494272, 0.011168014, 0.004035326, -0.010005236, -0.01212168], Gradients Sample Values: [-0.0069013573, -0.002216745, 0.0030424725, 0.008318476, 0.0003937923], Scaled Gradients Sample Values: [-6.901357E-05, -2.2167449E-05, 3.0424724E-05, 8.3184765E-05, 3.937923E-06]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.116384774, 0.1644706, -0.30116948, 0.035148352, -0.12438686], Gradients Sample Values: [0.00032541913, -4.0996274E-05, 0.00011199257, 0, 0], Scaled Gradients Sample Values: [3.2541911E-06, -4.0996272E-07, 1.1199257E-06, 0, 0]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.011232605, 0.0021920174, 0.0059773843, -0.0018255296, -0.0005008493], Gradients Sample Values: [-0.008392887, -0.0002711675, 0.0018438973, -8.7400884E-05, 0.0021064319], Scaled Gradients Sample Values: [-8.392887E-05, -2.7116748E-06, 1.8438972E-05, -8.7400883E-07, 2.1064317E-05]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24718793, 0.064608015, 0.022630628, 0.087082155, 0.1093849], Gradients Sample Values: [-0.013818863, 0.00711891, 0.0066999528, -0.0016143316, -0.0024320285], Scaled Gradients Sample Values: [-0.00013818862, 7.11891E-05, 6.699953E-05, -1.6143316E-05, -2.4320285E-05]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.07542144, 0.02827524, -0.103696704, 0, 0], Gradients Sample Values: [-0.042627104, 0.020323262, 0.022303846, 0, 0], Scaled Gradients Sample Values: [-0.00042627103, 0.00020323262, 0.00022303846]
[Scope: Trainer, Epoch: 5, Batch: 72] [2024-10-07 08:02:58.242] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5, Batch 72/75 completed in 0.02s. Loss: 1.0685669
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.243] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.243] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.01433981, -0.053342182, -0.03988627, 0.055829935, -0.04963703], Output Sample Values: [0, 0, 0, 0.055829935, 0]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.249] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.249] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.064574756, 0.03321151, -0.05684098, -0.023378018, 0.0015303418], Output Sample Values: [0.064574756, 0.03321151, 0, 0, 0.0015303418]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.2091255, 0.084430695, -0.3428135, 0.19831038, 0.6513729], Output Sample Values: [0.4067358, 0.35905266, 0.23421156, 0.31782153, 0.49997222]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.0289617]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.4067358, 0.35905266, 0.23421156, 0.31782153, 0.49997222], Probabilities Sample: [0.3577851, 0.34112513, 0.30108973, 0.3254177, 0.39043453], Log Probabilities Sample: [-1.0278227, -1.075506, -1.200347, -1.1226457, -0.94049495], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.075506, 1.1226457, 1.0067959, 0.9814916, 1.0025059] Loss Sum: [32.926773], Mean Loss: [1.0289617]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.4067358, 0.35905266, 0.23421156, 0.31782153, 0.49997222, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [0.011180785, -0.02058984, 0.009409054, -0.021080697, 0.012201079]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [32, 3], GradOutput Shape [32, 3], GradInput Shape [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.250] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.4067358, 0.35905266, 0.23421156, 0.31782153, 0.49997222, Gradient Output Sample Values: [0.011180785, -0.02058984, 0.009409054, -0.021080697, 0.012201079], Gradient Input Sample Values: [0.0048085474, -0.0071625034, 0.0023539567, -0.007023513, 0.0055911173]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.251] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.251] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.064574756, 0.03321151, 0, 0, 0.0015303418, Gradient Output Sample Values: [0.0007791313, -0.00026750402, -0.00021042034, -4.1035542E-05, 0.0015897446], Gradient Input Sample Values: [0.0007791313, -0.00026750402, 0, 0, 0.0015897446]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.262] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.262] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0, 0.055829935, 0, Gradient Output Sample Values: [0.00056170404, -2.805458E-05, -0.000114480994, 6.993499E-05, 0.0014496141], Gradient Input Sample Values: [0, 0, 0, 6.993499E-05, 0]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.263] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.263] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.30782163, -1.2601308, -0.4701263, 0.10135974, -0.6501942], Gradients Sample Values: [-0.00028124542, -0.0015531725, 0.00057247566, 6.619991E-05, -0.0014016363], Scaled Gradients Sample Values: [-2.812454E-06, -1.5531725E-05, 5.7247566E-06, 6.619991E-07, -1.4016363E-05]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.263] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.263] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.01642326, 0.01113518, 0.0040814644, -0.010014776, -0.012142595], Gradients Sample Values: [0.007101236, 0.0032835014, -0.0046138144, 0.00095403823, 0.002091479], Scaled Gradients Sample Values: [7.101236E-05, 3.2835014E-05, -4.6138142E-05, 9.540382E-06, 2.091479E-05]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.11638208, 0.16447107, -0.3011694, 0.035148352, -0.12438686], Gradients Sample Values: [0.00026998736, -4.8032078E-05, -8.347627E-06, 0, 0], Scaled Gradients Sample Values: [2.6998734E-06, -4.8032075E-07, -8.3476266E-08, 0, 0]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.011262593, 0.0021939666, 0.0059946617, -0.0018337965, -0.00051667617], Gradients Sample Values: [-0.002998806, -0.00019492, -0.0017277321, 0.00082669186, 0.0015826888], Scaled Gradients Sample Values: [-2.998806E-05, -1.9492E-06, -1.7277322E-05, 8.2669185E-06, 1.5826887E-05]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24718663, 0.064644024, 0.022595914, 0.08718922, 0.10931661], Gradients Sample Values: [0.00012928998, -0.0036006304, 0.0034713408, -0.010706559, 0.006829283], Scaled Gradients Sample Values: [1.2928998E-06, -3.6006302E-05, 3.471341E-05, -0.000107065585, 6.829283E-05]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.075371616, 0.028594986, -0.10396662, 0, 0], Gradients Sample Values: [0.004982473, -0.031974453, 0.026991982, 0, 0], Scaled Gradients Sample Values: [4.982473E-05, -0.00031974452, 0.0002699198]
[Scope: Trainer, Epoch: 5, Batch: 73] [2024-10-07 08:02:58.265] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5, Batch 73/75 completed in 0.02s. Loss: 1.0289617
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.266] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.266] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.014409654, -0.053374317, -0.039840765, 0.055820283, -0.04965678], Output Sample Values: [0, 0, 0, 0.055820283, 0]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.06467453, 0.03323624, -0.05683437, -0.023426607, 0.0014389977], Output Sample Values: [0.06467453, 0.03323624, 0, 0, 0.0014389977]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.20942366, 0.08497593, -0.34373668, 0.19692284, 0.6544052], Output Sample Values: [0.406816, 0.3592122, 0.23397182, 0.31712523, 0.50108665]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.0598131]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.406816, 0.3592122, 0.23397182, 0.31712523, 0.50108665], Probabilities Sample: [0.35780987, 0.34117582, 0.30101424, 0.32516193, 0.39083472], Log Probabilities Sample: [-1.0277535, -1.0753573, -1.2005976, -1.1234319, -0.9394705], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.2005976, 1.1234319, 1.2781838, 0.98032534, 1.0335233] Loss Sum: [33.91402], Mean Loss: [1.0598131]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.406816, 0.3592122, 0.23397182, 0.31712523, 0.50108665, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [0.0111815585, 0.0106617445, -0.021843305, -0.02108869, 0.012213585]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [32, 3], GradOutput Shape [32, 3], GradInput Shape [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.272] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.406816, 0.3592122, 0.23397182, 0.31712523, 0.50108665, Gradient Output Sample Values: [0.0111815585, 0.0106617445, -0.021843305, -0.02108869, 0.012213585], Gradient Input Sample Values: [0.0032193833, 0.0026559418, -0.0058753253, -0.0070193727, 0.0055960794]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.273] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.273] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.06467453, 0.03323624, 0, 0, 0.0014389977, Gradient Output Sample Values: [0.00083472097, 0.0003285893, 0.0012969159, -0.00045916392, 3.1080883E-05], Gradient Input Sample Values: [0.00083472097, 0.0003285893, 0, 0, 3.1080883E-05]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.286] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.286] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0, 0.055820283, 0, Gradient Output Sample Values: [-0.00037425262, 0.0019605083, 0.0015434566, -0.0006004635, -0.0026442404], Gradient Input Sample Values: [0, 0, 0, -0.0006004635, 0]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.3078517, -1.2601442, -0.4701265, 0.101361826, -0.65018654], Gradients Sample Values: [0.0030073079, 0.0013478237, 2.2281558E-05, -0.00020824489, -0.000767457], Scaled Gradients Sample Values: [3.0073079E-05, 1.3478236E-05, 2.2281557E-07, -2.0824489E-06, -7.67457E-06]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.016580576, 0.01114675, 0.00408627, -0.010005872, -0.012180137], Gradients Sample Values: [-0.015731465, -0.0011570324, -0.0004805845, -0.00089040026, 0.003754286], Scaled Gradients Sample Values: [-0.00015731464, -1.1570324E-05, -4.805845E-06, -8.904002E-06, 3.754286E-05]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.11638204, 0.16447155, -0.30116984, 0.035148352, -0.12438686], Gradients Sample Values: [3.3688673E-06, -4.7122398E-05, 4.480317E-05, 0, 0], Scaled Gradients Sample Values: [3.368867E-08, -4.7122396E-07, 4.480317E-07, 0, 0]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.011260265, 0.002201795, 0.0060057538, -0.0018359977, -0.0005535115], Gradients Sample Values: [0.00023286464, -0.00078284234, -0.001109192, 0.00022012297, 0.0036835312], Scaled Gradients Sample Values: [2.3286464E-06, -7.828423E-06, -1.109192E-05, 2.2012296E-06, 3.683531E-05]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24711923, 0.06479176, 0.022515574, 0.08722549, 0.10933299], Gradients Sample Values: [0.006739568, -0.014773454, 0.008033884, -0.0036266027, -0.0016386344], Scaled Gradients Sample Values: [6.7395675E-05, -0.00014773454, 8.033884E-05, -3.6266025E-05, -1.6386342E-05]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.07558771, 0.028608799, -0.10419653, 0, 0], Gradients Sample Values: [-0.021609686, -0.001381339, 0.022991022, 0, 0], Scaled Gradients Sample Values: [-0.00021609686, -1.381339E-05, 0.00022991022]
[Scope: Trainer, Epoch: 5, Batch: 74] [2024-10-07 08:02:58.287] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5, Batch 74/75 completed in 0.02s. Loss: 1.0598131
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.288] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.288] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [-0.014255455, -0.053364377, -0.039835654, 0.055829782, -0.04969453], Output Sample Values: [0, 0, 0, 0.055829782, 0]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.294] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.294] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.06466005, 0.03327018, -0.056828465, -0.02339146, 0.001224566], Output Sample Values: [0.06466005, 0.03327018, 0, 0, 0.001224566]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.20963368, 0.08538514, -0.344387, 0.1998215, 0.65355104], Output Sample Values: [0.40686873, 0.3593303, 0.2338009, 0.31796208, 0.5005271]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.0485606]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.40686873, 0.3593303, 0.2338009, 0.31796208, 0.5005271], Probabilities Sample: [0.357826, 0.3412135, 0.30096048, 0.3254423, 0.3906259], Log Probabilities Sample: [-1.0277084, -1.0752469, -1.2007763, -1.12257, -0.94000494], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.2007763, 1.12257, 1.0065643, 1.0110301, 1.002227] Loss Sum: [33.55394], Mean Loss: [1.0485606]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.40686873, 0.3593303, 0.2338009, 0.31796208, 0.5005271, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [0.011182062, 0.010662922, -0.021844985, -0.021079928, 0.012207059]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass completed with Input Shape [32, 3], GradOutput Shape [32, 3], GradInput Shape [32, 3]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.295] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.40686873, 0.3593303, 0.2338009, 0.31796208, 0.5005271, Gradient Output Sample Values: [0.011182062, 0.010662922, -0.021844985, -0.021079928, 0.012207059], Gradient Input Sample Values: [0.003217639, 0.0026551478, -0.0058727865, -0.007026261, 0.0056004934]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.296] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.296] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.06466005, 0.03327018, 0, 0, 0.001224566, Gradient Output Sample Values: [0.00083494297, 0.00032892457, 0.0012964197, -0.00045886886, 3.0987663E-05], Gradient Input Sample Values: [0.00083494297, 0.00032892457, 0, 0, 3.0987663E-05]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.308] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Backward pass completed with Input Shape [32, 64], GradOutput Shape [32, 64], GradInput Shape [32, 64]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.308] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0, 0, 0.055829782, 0, Gradient Output Sample Values: [-0.00037325994, 0.001960402, 0.0015432527, -0.0006003025, -0.0026435936], Gradient Input Sample Values: [0, 0, 0, -0.0006003025, 0]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [2, 64], Gradients Shape: [2, 64], Scaled Gradients Shape: [2, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.30784547, -1.2601424, -0.4701297, 0.10135079, -0.6501834], Gradients Sample Values: [-0.00062140595, -0.00018224973, 0.00031868403, 0.001103441, -0.00031851456], Scaled Gradients Sample Values: [-6.2140593E-06, -1.8224972E-06, 3.1868403E-06, 1.103441E-05, -3.1851455E-06]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Gradients Sample Values: [-0.003169602, 0.00392826, -0.005423866, 0.00020750222, 0.0048955944], Scaled Gradients Sample Values: [-3.1696018E-05, 3.9282597E-05, -5.4238662E-05, 2.0750222E-06, 4.8955942E-05]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 64], Gradients Shape: [64, 64], Scaled Gradients Shape: [64, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.116384394, 0.16447167, -0.30116904, 0.035148352, -0.12438686], Gradients Sample Values: [-0.00023548766, -1.2564451E-05, -7.9500496E-05, 0, 0], Scaled Gradients Sample Values: [-2.3548766E-06, -1.256445E-07, -7.9500495E-07, 0, 0]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 64], Gradients Shape: [1, 64], Scaled Gradients Shape: [1, 64],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.011279041, 0.002206722, 0.0060423226, -0.001842671, -0.0005369], Gradients Sample Values: [-0.0018776574, -0.00049268885, -0.0036568865, 0.00066732394, -0.0016611489], Scaled Gradients Sample Values: [-1.8776573E-05, -4.9268883E-06, -3.6568865E-05, 6.673239E-06, -1.6611488E-05]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [64, 3], Gradients Shape: [64, 3], Scaled Gradients Shape: [64, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.24711919, 0.06484546, 0.022461917, 0.087286994, 0.10931485], Gradients Sample Values: [4.038273E-06, -0.005369685, 0.005365647, -0.0061504627, 0.0018145337], Scaled Gradients Sample Values: [4.0382726E-08, -5.369685E-05, 5.365647E-05, -6.150462E-05, 1.8145336E-05]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Debug] HyperMath.src.Core.Optimizers.SGDOptimizer: Update completed with Input Shape: [1, 3], Gradients Shape: [1, 3], Scaled Gradients Shape: [1, 3],LearningRate: [0.01]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.309] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.07570491, 0.028633226, -0.10433816, 0, 0], Gradients Sample Values: [-0.011720076, -0.0024426207, 0.014162697, 0, 0], Scaled Gradients Sample Values: [-0.00011720076, -2.4426206E-05, 0.00014162697]
[Scope: Trainer, Epoch: 5, Batch: 75] [2024-10-07 08:02:58.310] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5, Batch 75/75 completed in 0.02s. Loss: 1.0485606
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.311] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.311] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.317] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.317] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.317] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.317] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.317] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.317] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.318] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.318] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.324] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.324] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.324] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.324] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.325] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.325] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.325] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.325] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.331] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.331] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.332] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.332] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.332] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.332] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.333] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.333] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.339] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.339] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.339] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.339] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.339] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.339] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.340] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.340] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.346] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.346] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.346] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.346] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.347] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.347] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.348] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.348] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.354] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.354] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.354] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.354] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.355] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.355] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.355] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.355] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.361] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.361] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.362] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.362] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.362] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.362] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.363] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.363] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.369] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.369] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.369] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.369] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.369] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.369] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.370] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.370] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.376] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.376] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.376] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.376] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.376] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.376] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.377] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.377] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.383] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.383] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.384] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.384] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.384] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.384] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.384] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.384] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.391] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.391] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.391] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.391] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.392] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.392] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.392] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.392] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.400] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.400] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.400] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.400] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.400] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.400] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.401] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.401] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.407] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.407] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.407] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.407] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.407] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.407] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.408] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.408] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.414] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.414] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.414] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.414] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.415] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.415] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.415] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.415] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.421] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.421] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.422] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.422] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.422] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.422] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.424] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.424] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.430] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.430] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.430] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.430] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.430] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.430] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.431] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.431] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.437] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.437] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.437] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.437] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.437] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.437] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.438] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.438] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.444] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.444] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.444] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.444] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.444] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.444] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [37.658325], Mean Loss: [1.1768227]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.445] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [24, 64], Output Shape: [24, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.445] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.016612273, 0.011107468, 0.004140509, -0.010007947, -0.012229093], Output Sample Values: [0.016612273, 0.011107468, 0.004140509, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [24, 64], Output Shape: [24, 64]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.028606988, 0.016691314, -0.013722023, -0.029626934, -0.017434105], Output Sample Values: [0.028606988, 0.016691314, 0, 0, 0]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [24, 3], Output Shape: [24, 3]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.1779727, 0.068255916, -0.23093891, 0.17678589, 0.06732194], Output Sample Values: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.1754662]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.3905547, 0.34997132, 0.25947398, 0.39029634, 0.34982827], Probabilities Sample: [0.3524383, 0.3384215, 0.30914018, 0.35235268, 0.3383783], Log Probabilities Sample: [-1.0428797, -1.0834632, -1.1739604, -1.0431226, -1.0835907], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.1739604, 1.1735436, 1.1731972, 1.1729287, 1.1727872] Loss Sum: [28.21119], Mean Loss: [1.1754662]
[Scope: Trainer, Epoch: 5, Evaluate}] [2024-10-07 08:02:58.450] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5/100, Training Loss: 1.064403, Validation Loss: 1.1767513
[Scope: Trainer, Epoch: 5] [2024-10-07 08:02:58.450] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_5.ckpt' with Validation Loss: 1.1767513
[Scope: Trainer, Epoch: 5] [2024-10-07 08:02:58.451] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 5 completed in 1.81s, Average Training Loss: 1.064403, Min Loss: 1.0170742, Max Loss: 1.1082914.
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.452] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.452] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0626369, 1.2044396, 0.12602587, 0.88161147, 0.5346206], Output Sample Values: [0.0626369, 1.2044396, 0.12602587, 0.88161147, 0.5346206]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.458] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.458] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.96651584, -0.4364951, -1.3908745, -1.7981895, -2.4621623], Output Sample Values: [0.96651584, 0, 0, 0, 0]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.459] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.459] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.09005412, 1.7608707, -0.9919945, 0.24408832, 0.117450684], Output Sample Values: [0.15025377, 0.7988252, 0.05092107, 0.41418597, 0.36491972]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.459] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.0138496]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.459] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss Details:
Predictions Sample: [0.15025377, 0.7988252, 0.05092107, 0.41418597, 0.36491972], Probabilities Sample: [0.26190025, 0.5009644, 0.23713532, 0.36020857, 0.3428925], Log Probabilities Sample: [-1.3397915, -0.6912202, -1.4391243, -1.021072, -1.0703382], Target Sample: [1, 0, 0, 0, 0], Per-Sample Loss: [1.3397915, 1.2143637, 1.2053665, 0.84079766, 1.1550549] Loss Sum: [32.443188], Mean Loss: [1.0138496]
[Scope: Trainer, Epoch: 6, Batch: 1] [2024-10-07 08:02:58.459] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Gradient Shape: [32, 3]
[