Visualizing Loss

Outputting Training and Validation Loss Metrics

In Keras, the model.fit() and model.fit_generator() methods have a verbose parameter
that tells Keras to output loss metrics as the model trains. The verbose parameter can optionally be set to verbose = 1 or verbose = 2.

Setting model.fit(verbose = 1) will

output a progress bar in the terminal as the model trains.
output the loss metric on the training set as the model trains.
output the loss on the training and validation sets after each epoch.

With model.fit(verbose = 2), Keras will only output the loss on the training set and validation set after each epoch.

Model History Object

When calling model.fit() or model.fit_generator(), Keras outputs a history object that contains the training and validation loss for each epoch. Here is an example of how you can use the history object to visualize the loss:

The following code shows how to use the model.fit() history object to produce the visualization.

from keras.models import Model
import matplotlib.pyplot as plt

history_object = model.fit_generator(train_generator, samples_per_epoch =
    len(train_samples), validation_data = 
    validation_generator,
    nb_val_samples = len(validation_samples), 
    nb_epoch=5, verbose=1)

### print the keys contained in the history object
print(history_object.history.keys())

### plot the training and validation loss for each epoch
plt.plot(history_object.history['loss'])
plt.plot(history_object.history['val_loss'])
plt.title('model mean squared error loss')
plt.ylabel('mean squared error loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validation set'], loc='upper right')
plt.show()