Genome = [[[[3, 0], [6, 0]], [[2, 1], [4, 1]], [[10, 3], [11, 0]], [[6, 3], [9, 1]], [[6, 5], [7, 5]]], [[[5, 1], [7, 1]], [[4, 0], [8, 2]], [[7, 2], [9, 0]], [[0, 2], [0, 0]], [[6, 1], [6, 3]]]]
Genotype = Genotype(normal=[('linear_128', 0), ('dropout_05', 0), ('linear_64', 1), ('linear_C', 1), ('leaky_relu', 3), ('selu', 0), ('dropout_05', 3), ('relu', 1), ('dropout_05', 5), ('skip_connect', 5)], normal_concat=[2, 4, 6], reduce=[('dropout_02', 1), ('skip_connect', 1), ('linear_C', 0), ('batch_norm', 2), ('skip_connect', 2), ('relu', 0), ('none', 2), ('none', 0), ('dropout_05', 1), ('dropout_05', 3)], reduce_concat=[4, 5, 6])
param size = 1.11109MB
total params = 291265
flops = 0.2920M
valid_acc = 97.46

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Model Configuration
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search_space = micro
model_config_id = mlp_small
task_type = regression
input_dim = 3
output_dim = 1
C (channels) = 32
layers = 4
epochs = 5

==================================================
Training Progress
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Epoch   1 | Train Loss: 0.773420 | Val Loss: 0.030161 | LR: 0.000905
Epoch   2 | Train Loss: 0.401356 | Val Loss: 0.025193 | LR: 0.000655
Epoch   3 | Train Loss: 0.294131 | Val Loss: 0.066469 | LR: 0.000345
Epoch   4 | Train Loss: 0.294504 | Val Loss: 0.096129 | LR: 0.000095
Epoch   5 | Train Loss: 0.252544 | Val Loss: 0.021878 | LR: 0.000000

Final val_loss = 0.021878
