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When you run a Keras neural network model you might see something like this in the console:

Epoch 1/3

   6/1000 [..............................] - ETA: 7994s - loss: 5111.7661

As time goes on the loss hopefully improves. I want to log these losses to a file over time so that I can learn from them. I have tried:

logging.basicConfig(filename='example.log', filemode='w', level=logging.DEBUG)

but this doesn't work. I am not sure what level of logging I need in this situation.

I have also tried using a callback like in:

def generate_train_batch():

    while 1:

        for i in xrange(0,dset_X.shape[0],3):

            yield dset_X[i:i+3,:,:,:],dset_y[i:i+3,:,:]

class LossHistory(keras.callbacks.Callback):

    def on_train_begin(self, logs={}):

        self.losses = []

def on_batch_end(self, batch, logs={}):


logloss=LossHistory() colorize.fit_generator(generate_train_batch(),samples_per_epoch=1000,nb_epoch=3,callbacks=['logloss'])

but obviously, this isn't writing to a file. Whatever the method, through a callback or the logging module or anything else, I would love to hear your solutions for logging loss of a keras neural network to a file. Thanks!

1 Answer

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by (33.2k points)

You can simply use CSVLogger function in the callbacks class of keras. A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training

For example:

from keras.callbacks import CSVLogger

csv_logger = CSVLogger('log.csv', append=True, separator=';')

model.fit(X_train, Y_train, callbacks=[csv_logger])

Hope this answer helps.

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