ftrl train output

1 files changed, 31 insertions, 0 deletions

diff --git a/test/train-sets/ref/0001_ftrl.stderr b/test/train-sets/ref/0001_ftrl.stderr
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+using l1 regularization = 2
+final_regressor = models/ftrl.model
+Enabling FTRL-Proximal based optimization
+ftrl_alpha = 0.01
+ftrl_beta = 0
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+using no cache
+Reading datafile = train-sets/0001.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+1.000000   1.000000            1         1.0   1.0000   0.0000       51
+0.500000   0.000000            2         2.0   0.0000   0.0000      104
+0.250000   0.000000            4         4.0   0.0000   0.0000      135
+0.250000   0.250000            8         8.0   0.0000   0.0000      146
+0.312500   0.375000           16        16.0   1.0000   0.0000       24
+0.343750   0.375000           32        32.0   0.0000   0.0000       32
+0.359375   0.375000           64        64.0   0.0000   0.0000       61
+0.414062   0.468750          128       128.0   1.0000   0.0000      106
+
+finished run
+number of examples = 200
+weighted example sum = 200
+weighted label sum = 91
+average loss = 0.455
+best constant = 0.455
+best constant's loss = 0.247975
+total feature number = 15482