can you find calculation problems in the following log?
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.283] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.4195901, 0.40217784, 0.17823203, 0.40580392, 0.27861375], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.2595911, 1.1178014, 1.3168427, 1.1958325, 1.0888752], LogSumExp: [1.4378232, 1.4333837, 1.4462367, 1.434743, 1.4320776], Log Probabilities: [-1.0182331, -1.0356454, -1.2595911, -1.0275798, -1.1547699], Product: [-0, -0, -1.2595911, -0, -0], Loss Sum: [35.055748], Mean Loss: [1.0954921]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.283] [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: 1, Batch: 75] [2024-10-07 06:36:28.283] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Gradient Input Sample Values: 0.4195901, 0.40217784, 0.17823203, 0.40580392, 0.27861375, Gradient Output Sample Values: [1, 0, 0, 1, 0], Gradient Sample Values: [0.011288521, 0.011093664, -0.022382185, 0.011183501, 0.009847813]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.283] [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: 1, Batch: 75] [2024-10-07 06:36:28.283] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Backward pass Input Sample Values: 0.4195901, 0.40217784, 0.17823203, 0.40580392, 0.27861375, Gradient Output Sample Values: [0.011288521, 0.011093664, -0.022382185, 0.011183501, 0.009847813], Gradient Input Sample Values: [0.0025509254, 0.0023666993, -0.004917625, 0.004276592, 0.0025640486]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.284] [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: 1, Batch: 75] [2024-10-07 06:36:28.284] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0.09835901, 1.2833632, 0.114628, 0.8967201, 0.35435838, Gradient Output Sample Values: [0.0007524844, 0.0018305033, -0.0016663602, -6.192221E-05, 0.0010268909], Gradient Input Sample Values: [0.0007524844, 0.0018305033, -0.0016663602, -6.192221E-05, 0.0010268909]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.296] [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: 1, Batch: 75] [2024-10-07 06:36:28.296] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: 0, 0.719992, 0.114789724, 1.3749208, 1.1038622, Gradient Output Sample Values: [-0.001110018, -0.00012940164, -0.0011529112, 0.0008068677, 0.00043236584], Gradient Input Sample Values: [0, -0.00012940164, -0.0011529112, 0.0008068677, 0.00043236584]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.297] [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: 1, Batch: 75] [2024-10-07 06:36:28.297] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [-0.3715915, -0.29450038, 0.15190129, 0.75284696, -0.36870232], Gradients Sample Values: [-0.0002213418, 0.0037106164, 0.0005767533, -2.6316018E-05, 0.0010662316], Scaled Gradients Sample Values: [-2.213418E-06, 3.710616E-05, 5.767533E-06, -2.631602E-07, 1.06623165E-05]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.297] [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: 1, Batch: 75] [2024-10-07 06:36:28.297] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Gradients Sample Values: [-0.0033474022, -0.011218503, -0.0031863956, 0.004214109, -0.0043036095], Scaled Gradients Sample Values: [-3.347402E-05, -0.00011218503, -3.1863954E-05, 4.2141088E-05, -4.3036092E-05]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.298] [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: 1, Batch: 75] [2024-10-07 06:36:28.298] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.1433318, -0.095209815, 0.087072045, 0.2976664, 0.27491567], Gradients Sample Values: [-0.00022207224, -0.0017341903, 1.7241284E-05, -2.2876666E-06, 3.0274188E-05], Scaled Gradients Sample Values: [-2.2207223E-06, -1.7341903E-05, 1.7241284E-07, -2.2876666E-08, 3.0274188E-07]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.298] [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: 1, Batch: 75] [2024-10-07 06:36:28.298] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.0043165437, 0.012213326, -0.0041841026, -0.0001702524, 0.0028439625], Gradients Sample Values: [-0.0054335683, -0.016278889, -0.0035863635, -0.00044689217, -0.00033032184], Scaled Gradients Sample Values: [-5.433568E-05, -0.00016278889, -3.5863635E-05, -4.468922E-06, -3.3032184E-06]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.298] [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: 1, Batch: 75] [2024-10-07 06:36:28.298] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.09969141, 0.14403242, -0.032007992, 0.3579323, 0.28373742], Gradients Sample Values: [-0.0061607976, 0.004866189, 0.0012946089, -0.022976816, 0.03202765], Scaled Gradients Sample Values: [-6.160797E-05, 4.866189E-05, 1.2946089E-05, -0.00022976815, 0.0003202765]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.298] [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: 1, Batch: 75] [2024-10-07 06:36:28.298] [Trace] HyperMath.src.Core.Optimizers.SGDOptimizer: Update Input Sample Values: [0.017108457, 0.016872944, -0.033981405, 0, 0], Gradients Sample Values: [-0.043614425, 0.004495609, 0.03911882, 0, 0], Scaled Gradients Sample Values: [-0.00043614424, 4.495609E-05, 0.0003911882]
[Scope: Trainer, Epoch: 1, Batch: 75] [2024-10-07 06:36:28.298] [Debug] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1, Batch 75/75 completed in 0.02s. Loss: 1.0954921
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.316] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.316] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.322] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.322] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.323] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.323] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.323] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.323] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.324] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.324] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.330] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.330] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.331] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.331] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.331] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.331] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.332] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.332] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.338] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.338] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.338] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.338] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.339] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.339] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.339] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.339] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.345] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.345] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.346] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.346] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.346] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.346] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.347] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.347] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.353] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.353] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.354] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.354] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.354] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.354] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.355] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.355] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.361] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.361] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.361] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.361] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.361] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.361] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.362] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.362] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.368] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.368] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.369] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.369] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.369] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.369] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.370] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.370] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.376] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.376] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.377] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.377] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.377] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.377] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.378] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.378] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.384] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.384] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.384] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.384] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.384] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.384] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.385] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.385] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.391] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.391] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.392] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.392] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.392] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.392] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.394] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.394] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.400] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.400] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.401] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.401] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.401] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.401] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.402] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.402] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.408] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.408] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.408] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.408] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.408] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.408] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.409] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.409] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.416] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.416] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.416] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.416] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.416] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.416] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.417] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.417] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.423] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.423] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.424] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.424] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.424] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.424] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.425] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.425] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.431] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.431] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.431] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.431] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.431] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.431] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.432] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.432] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.438] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.438] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.439] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.439] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.439] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.439] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.440] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.440] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.446] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.446] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.446] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.446] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.446] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.447] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.447] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.447] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.454] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [32, 64], Output Shape: [32, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.454] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.454] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [32, 3], Output Shape: [32, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.454] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.454] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [32, 3], Target Shape: [32, 3], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.454] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [35.866062], Mean Loss: [1.1208144]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.455] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [24, 64], Output Shape: [24, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.455] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.0008564906, -1.6183323E-05, -0.004842794, -6.164149E-05, 0.0023604194], Output Sample Values: [0.0008564906, 0, 0, 0, 0.0023604194]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Debug] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Forward pass completed with Input Shape: [24, 64], Output Shape: [24, 64]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Trace] HyperMath.src.Core.ActivationFunctions.ReLUActivation: Input Sample Values: [0.004867705, 0.020650059, -0.004647133, 0.0011211063, 0.0012883679], Output Sample Values: [0.004867705, 0.020650059, 0, 0.0011211063, 0.0012883679]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Debug] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass completed with Input Shape: [24, 3], Output Shape: [24, 3]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Trace] HyperMath.src.Core.ActivationFunctions.SoftmaxActivation: Forward pass Input Sample Values: [0.041579876, 0.032413423, -0.050784785, 0.04047657, 0.032169446], Output Sample Values: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Debug] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Shape: [24, 3], Target Shape: [24, 3], Mean Loss: [1.1200732]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Trace] HyperMath.src.Core.LossFunctions.CrossEntropyLoss: Compute Loss completed with Predictions Sample: [0.344513, 0.34136948, 0.31411752, 0.3441817, 0.34133437], Target Sample: [1, 0, 0, 1, 0], Per-Sample Loss: [1.117921, 1.1175508, 1.1175526, 1.1176578, 1.1180024], LogSumExp: [1.4320385, 1.4320347, 1.4320349, 1.4320357, 1.4320389], Log Probabilities: [-1.0875256, -1.090669, -1.117921, -1.0878531, -1.0907004], Product: [-0, -0, -1.117921, -0, -0], Loss Sum: [26.881758], Mean Loss: [1.1200732]
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.460] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1/100, Training Loss: 1.0890914, Validation Loss: 1.1207756
[Scope: Trainer, Epoch: 1, Evaluate}] [2024-10-07 06:36:28.463] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_1.ckpt' with Validation Loss: 1.1207756
[Scope: Trainer, Epoch: 1] [2024-10-07 06:36:28.464] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Checkpoint saved at 'checkpoints\model_epoch_1.ckpt' with Validation Loss: 1.1207756
[Scope: Trainer, Epoch: 1] [2024-10-07 06:36:28.475] [Information] HyperAI.Core.Trainings.Trainers.Trainer: Epoch 1 completed in 2.09s, Average Training Loss: 1.0890914, Min Loss: 1.0505646, Max Loss: 1.116773.