在每个时期之后进行自定义回调以记录某些信息 [英] Custom callback after each epoch to log certain information

问题描述

我知道如何在每个时期后保存模型:

savemodel = ModelCheckpoint(filepath='models/model_{epoch:02d}-{loss:.2f}.h5')
model.fit(X, Y, batch_size=4, epochs=32, verbose=1, callbacks=[savemodel])

如何具有自定义的回调函数来记录某些信息:

How to have a custom callback function to log certain informations:

def write_metrics(): 
    with open('log.txt', 'a') as f:  # append to the log file
        f.write('{epoch:02d}: loss = {loss:.1f}')

model.fit(X, Y, batch_size=4, epochs=32, verbose=1, callbacks=[savemodel, write_metrics])

?

使用此代码将无法使用，因为f.write('{epoch:02d}: loss = {loss:.1f}')中未定义{loss}和{epoch}.

With this code it won't work because {loss} and {epoch} are not defined in f.write('{epoch:02d}: loss = {loss:.1f}').

推荐答案

这是通过将Callback子类化的解决方案:

Here is the solution, by subclassing Callback:

from keras.callbacks import Callback

class MyLogger(Callback):
    def on_epoch_end(self, epoch, logs=None):
        with open('log.txt', 'a+') as f:
            f.write('%02d %.3f\n' % (epoch, logs['loss']))

然后

mylogger = MyLogger()
model.fit(X, Y, batch_size=32, epochs=32, verbose=1, callbacks=[mylogger])

甚至

model.fit(X, Y, batch_size=32, epochs=32, verbose=1, callbacks=[MyLogger()])

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