Keras, append to logs from callback

Question

I have a callback that computes a couple of additional metrics in on_epoch_end for validation data and every 10 epochs for test data.

I also have a CSVLogger callback that saves normal metrics to a log file.

Is there an easy way from my callback to add a column or two to the logs that gets properly written by CSVLogger?

Answer 1

You can insert your additional metrics into the dictionary logs.

from keras.callbacks import Callback

class ComputeMetrics(Callback):
    def on_epoch_end(self, epoch, logs):
        logs['val_metric'] = epoch ** 2  # replace it with your metrics
        if (epoch + 1) % 10 == 0:
            logs['test_metric'] = epoch ** 3  # same
        else:
            logs['test_metric'] = np.nan

Just remember to place this callback before CSVLogger in your fit call. Callbacks that appear later in the list would receive a modified version of logs. For example,

model = Sequential([Dense(1, input_shape=(10,))])
model.compile(loss='mse', optimizer='adam')
model.fit(np.random.rand(100, 10),
          np.random.rand(100),
          epochs=30,
          validation_data=(np.random.rand(100, 10), np.random.rand(100)),
          callbacks=[ComputeMetrics(), CSVLogger('1.log')])

Now if you take a look at the output log file, you'll see two additional columns test_metric and val_metric:

epoch,loss,test_metric,val_loss,val_metric
0,0.547923130989,nan,0.370979120433,0
1,0.525437340736,nan,0.35585285902,1
2,0.501358469725,nan,0.341958616376,4
3,0.479624577463,nan,0.329370084703,9
4,0.460121934414,nan,0.317930338383,16
5,0.440655426979,nan,0.307486981452,25
6,0.422990380526,nan,0.298160370588,36
7,0.406809270382,nan,0.289906248748,49
8,0.3912438941,nan,0.282540213466,64
9,0.377326357365,729,0.276457450986,81
10,0.364721306562,nan,0.271435074806,100
11,0.353612961769,nan,0.266939682364,121
12,0.343238875866,nan,0.263228923082,144
13,0.333940329552,nan,0.260326927304,169
14,0.325931007862,nan,0.25773427248,196
15,0.317790198028,nan,0.255648627281,225
16,0.310636150837,nan,0.25411529541,256
17,0.304091459513,nan,0.252928718328,289
18,0.298703012466,nan,0.252127869725,324
19,0.292693507671,6859,0.251701972485,361
20,0.287824733257,nan,0.251610517502,400
21,0.283586999774,nan,0.251790778637,441
22,0.27927801609,nan,0.252100949883,484
23,0.276239238977,nan,0.252632959485,529
24,0.273072380424,nan,0.253150621653,576
25,0.270296501517,nan,0.253555388451,625
26,0.268056542277,nan,0.254015884399,676
27,0.266158599854,nan,0.254496408701,729
28,0.264166412354,nan,0.254723013639,784
29,0.262506003976,24389,0.255338237286,841