Logging dataΒΆ

Apart from printing loss, accuracy and other metrics during training it is often useful to log these numbers to a file. nuts-ml provides logging functionality within and outside of data pipelines via LogToFile. The following example demonstrates the basics. We have a list of samples that we log to a CSV file:

>>> filepath = 'tests/data/temp_logfile.csv'
>>> samples = [(1, 2, 3), (4, 5, 6)]
>>> with LogToFile(filepath) as logtofile:
...     samples >> logtofile >> Consume()

>>> open(filepath).read()

LogToFile allows to extract sample columns to log and to specify column names for the log file. In this next example we also show how to manually close and delete a created log file:

>>> logtofile = LogToFile(filepath, cols=(2, 0), colnames=['3rd', '1st'])
>>> samples >> logtofile >> Consume()

>>> open(filepath).read()

>>> logtofile.close()
>>> logtofile.delete()

In this more complex code sketch we will use LogToFile within a training a loop and log loss and accuracy per batch, and epoch, mean loss and mean accuracy per epoch:

log_batch = LogToFile('batchlog.csv', colnames=['loss', 'acc'])
log_epoch = LogToFile('epochlog.csv', colnames=['epoch', 'loss', 'acc'])
mean = Mean()

for epoch in range(EPOCHS):
    t_loss, t_acc = (train_samples >> ... >> build_batch >>
                     network.train() >> log_batch >> Unzip())
    log_epoch( (epoch, mean(t_loss), mean(t_acc) )


The output of network.train() is a NumPy array, containing the loss and accuracy per mini-batch (these are the outputs that Keras produces during training). Note that we call log_epoch explicitly (outside of the pipeline) and can simply provide the values to log as a tuple, list or array. Of course, the number of values must match the number column names defined. The same syntactical feature is used for Mean here. For instance, the following three constructs are equivalent:

>>> [1, 2, 3] >> Mean()
>>> Mean()([1, 2, 3])
>>> mean = Mean()
>>> mean([1, 2, 3])

Similar to logging we can also plot data. This is the topic of the next section.