In this blog, we will discuss Keras ProgbarLogger callback. As clear from the name, this deals with the logging of the progress bar that we usually see during fit() method depending upon the verbosity argument. So, let’s first discuss its API.
|
1 |
keras.callbacks.ProgbarLogger(count_mode='samples', stateful_metrics=None) |
Here, “count_mode” argument controls whether the progress bar displays the samples seen or the steps. This argument can take one of the values from ‘samples‘ or ‘steps‘. If set equal to ‘steps’, make sure that you provide the “steps_per_epoch” argument in the fit() method. Otherwise, this will give you an error. The ‘steps’ is basically used with generators like fit_generator, etc.
Below is the figure where first I’ve trained on 5000 samples and the count_mode argument is set to “samples“. For the second one, I’ve used 12 steps in the fit_generator for 1 epoch. The count_mode argument is set to “steps“.
The second argument “stateful_metrics” controls whether to display the average value of the metric specified or display its value at the last step of every epoch. This should be passed as an iterable like list etc. For more details, refer to Keras callbacks BaseLogger where we have discussed this argument in detail.
This callback, in turn, calls the Keras Progbar class that controls how to display the progress bar like the width of the progress bar, its update interval, etc. Now, let’s see how to use this.
|
1 2 3 4 5 6 7 8 9 |
# Load data, preprocessing and build model ... # First, create an instance of this ProgbarLogger class from keras.callbacks import ProgbarLogger call = ProgbarLogger(count_mode='samples', stateful_metrics=['acc']) #Then pass this as a list in the fit() method record = model.fit(..., callbacks=[call],...) |
Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.