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final_regressor = models/0021.model
Num weight bits = 16
learning rate = 10
initial_t = 1
power_t = 0.5
decay_learning_rate = 1
randomly initializing neural network output weights and hidden bias
creating cache_file = train-sets/3parity.cache
Reading from train-sets/3parity
num sources = 1
average since example example current current current
loss last counter weight label predict features
1.435731 1.435731 3 3.0 1.0000 -1.0000 4
1.965783 2.495835 6 6.0 1.0000 0.8038 4
2.377791 2.872201 11 11.0 1.0000 -1.0000 4
2.396167 2.414544 22 22.0 1.0000 0.7269 4
2.680468 2.964769 44 44.0 -1.0000 0.9079 4
2.787991 2.898014 87 87.0 -1.0000 1.0000 4
2.829916 2.871841 174 174.0 1.0000 0.8361 4
2.747754 2.665593 348 348.0 -1.0000 -0.0307 4
2.313654 1.879553 696 696.0 1.0000 -0.7951 4
2.025584 1.737515 1392 1392.0 1.0000 -0.6059 4
1.454971 0.884358 2784 2784.0 1.0000 0.8282 4
1.066638 0.678304 5568 5568.0 1.0000 1.0000 4
0.550489 0.034248 11135 11135.0 -1.0000 -1.0000 4
finished run
number of examples = 16000
weighted example sum = 1.6e+004
weighted label sum = 0
average loss = 0.3831
best constant = -6.25e-005
total feature number = 64000
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