How to log Keras loss output to a file

PythonLoggingMachine LearningNeural NetworkKeras

Python Problem Overview

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={}):

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!

Python Solutions

Solution 1 - Python

You can use CSVLogger callback.

as example:

from keras.callbacks import CSVLogger

csv_logger = CSVLogger('log.csv', append=True, separator=';'), Y_train, callbacks=[csv_logger])

Look at: Keras Callbacks

Solution 2 - Python

There is a simple solution to your problem. Every time any of the fit methods are used - as a result the special callback called History Callback is returned. It has a field history which is a dictionary of all metrics registered after every epoch. So to get list of loss function values after every epoch you can easly do:

history_callback =
loss_history = history_callback.history["loss"]

It's easy to save such list to a file (e.g. by converting it to numpy array and using savetxt method).



import numpy
numpy_loss_history = numpy.array(loss_history)
numpy.savetxt("loss_history.txt", numpy_loss_history, delimiter=",")


The solution to the problem of recording a loss after every batch is written in Keras Callbacks Documentation in a Create a Callback paragraph.

Solution 3 - Python

Old question, but here goes. Keras history output perfectly matches pandas DataSet input.

If you want the entire history to csv in one line:



Solution 4 - Python

You can redirect the sys.stdout object to a file before the method and reassign it to the standard console after method as follows:

import sys
oldStdout = sys.stdout
file = open('logFile', 'w')
sys.stdout = file, Ytrain)
sys.stdout = oldStdout

Solution 5 - Python

So In TensorFlow 2.0, it is quite easy to get Loss and Accuracy of each epoch because it returns a History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values

If you have validation Data

History =,trainY,validation_data = (testX,testY),batch_size= 100, epochs = epochs,verbose = 1)
train_loss = History.history['loss']
val_loss   = History.history['val_loss']
acc = History.history['accuracy']
val_acc = History.history['val_accuracy']

If you don't have validation Data

History =,trainY,batch_size= 100, epochs = epochs,verbose = 1)
train_loss = History.history['loss']
acc = History.history['accuracy']

Then to save list data into text file use the below code

import numpy as np
train_loss = np.array(loss_history)
np.savetxt("train_loss.txt", train_loss, delimiter=",")

Solution 6 - Python

Best is to create a LambdaCallback:

from keras.callbacks import LambdaCallback

txt_log = open('loss_log.txt', mode='wt', buffering=1)

save_op_callback = LambdaCallback(
  on_epoch_end = lambda epoch, logs: txt_log.write(
    {'epoch': epoch, 'loss': logs['loss']} + '\n'),
  on_train_end = lambda logs: txt_log.close()

Now,Just add it like this in the function:,callbacks = [save_op_callback])


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Content TypeOriginal AuthorOriginal Content on Stackoverflow
QuestionBigBoy1337View Question on Stackoverflow
Solution 1 - PythonAlex GlinskyView Answer on Stackoverflow
Solution 2 - PythonMarcin MożejkoView Answer on Stackoverflow
Solution 3 - PythonBenjamin StrinerView Answer on Stackoverflow
Solution 4 - PythonNagabhushan BaddiView Answer on Stackoverflow
Solution 5 - PythonRishabh JainView Answer on Stackoverflow
Solution 6 - PythonSayantan DasView Answer on Stackoverflow