How to log Keras loss output to a file

Question

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

Epoch 1/3
   6/1000 [..............................] - ETA: 7994         


        

Answer 1

You can use CSVLogger callback.

as example:

from keras.callbacks import CSVLogger

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

Look at: Keras Callbacks

Answer 2

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

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

Answer 3

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

If you want the entire history to csv in one line: pandas.DataFrame(model.fit(...).history).to_csv("history.csv")

Cheers

Answer 4

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 model.fit function:

model.fit(...,callbacks = [save_op_callback])

Answer 5

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 = model.fit(params...)
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).

UPDATE:

Try:

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

UPDATE 2:

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

Answer 6

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 = model.fit(trainX,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 = model.fit(trainX,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=",")