Genome = [[[[3, 0], [7, 0]], [[9, 2], [10, 2]], [[4, 3], [5, 3]], [[4, 3], [5, 3]], [[0, 2], [11, 5]]], [[[11, 1], [2, 0]], [[4, 2], [3, 1]], [[9, 1], [1, 3]], [[6, 4], [2, 3]], [[7, 1], [5, 3]]]]
Genotype = Genotype(normal=[('linear_128', 0), ('skip_connect', 0), ('relu', 2), ('leaky_relu', 2), ('linear_C', 3), ('dropout_02', 3), ('linear_C', 3), ('dropout_02', 3), ('none', 2), ('selu', 5)], normal_concat=[4, 6], reduce=[('selu', 1), ('linear_64', 0), ('linear_C', 2), ('linear_128', 1), ('relu', 1), ('linear_32', 3), ('dropout_05', 4), ('linear_64', 3), ('skip_connect', 1), ('dropout_02', 3)], reduce_concat=[5, 6])
param size = 1.44251MB
total params = 378145
flops = 0.3779M
valid_acc = 98.40

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

==================================================
Training Progress
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Epoch   1 | Train Loss: 0.509847 | Val Loss: 0.019421 | LR: 0.000750
Epoch   2 | Train Loss: 0.339967 | Val Loss: 0.012688 | LR: 0.000250
Epoch   3 | Train Loss: 0.168445 | Val Loss: 0.015182 | LR: 0.000000

Final val_loss = 0.012688
