HistoryΒΆ
A NeuralNet object logs training progress internally using a
History object, stored in the history attribute. Among
other use cases, history is used to print the training progress
after each epoch:
net.fit(X, y)
# prints
epoch train_loss valid_acc valid_loss dur
------- ------------ ----------- ------------ ------
1 0.7111 0.5100 0.6894 0.1345
2 0.6928 0.5500 0.6803 0.0608
3 0.6833 0.5650 0.6741 0.0620
4 0.6763 0.5850 0.6674 0.0594
All this information (and more) is stored in and can be accessed
through net.history. It is thus best practice to make use of
history for storing training-related data.
In general, History works like a list of dictionaries, where
each item in the list corresponds to one epoch, and each key of the
dictionary to one column. Thus, if you would like to access the
'train_loss' of the last epoch, you can call
net.history[-1]['train_loss']. To make the history more
accessible, though, it is possible to just pass the indices separated
by a comma: net.history[-1, 'train_loss'].
Moreover, History stores the results from each individual
batch under the batches key during each epoch. So to get the train
loss of the 3rd batch of the 7th epoch, use net.history[7,
'batches', 3, 'train_loss'].
Here are some examples showing how to index history:
# history of a fitted neural net
history = net.history
# get current epoch, a dict
history[-1]
# get train losses from all epochs, a list of floats
history[:, 'train_loss']
# get train and valid losses from all epochs, a list of tuples
history[:, ('train_loss', 'valid_loss')]
# get current batches, a list of dicts
history[-1, 'batches']
# get latest batch, a dict
history[-1, 'batches', -1]
# get train losses from current batch, a list of floats
history[-1, 'batches', :, 'train_loss']
# get train and valid losses from current batch, a list of tuples
history[-1, 'batches', :, ('train_loss', 'valid_loss')]
As History essentially is a list of dictionaries, you can
also write to it as if it were a list of dictionaries. Here too,
skorch provides some convenience functions to make life easier. First
there is new_epoch(), which will add a
new epoch dictionary to the end of the list. Also, there is
new_batch() for adding new batches to
the current epoch.
To add a new item to the current epoch, use history.record('foo',
123). This will set the value 123 for the key foo of the
current epoch. To write a value to the current batch, use
history.record_batch('bar', 456). Below are some more examples:
# history of a fitted neural net
history = net.history
# add new epoch row
history.new_epoch()
# add an entry to current epoch
history.record('my-score', 123)
# add a batch row to the current epoch
history.new_batch()
# add an entry to the current batch
history.record_batch('my-batch-score', 456)
# overwrite entry of current batch
history.record_batch('my-batch-score', 789)