Input / Output

Verbose Mode

To see the output of the neural network’s training, configure the Python logger called sknn or the default root logger. This is possible using the standard logging module which you can setup as follows:

import sys
import logging


Change the log level to logging.INFO for less information about each epoch, or logging.WARNING only to receive messages about problems or failures.

Using the flag verbose=True on either sknn.mlp.Classifier and sknn.mlp.Regressor will setup a default logger at DEBUG level if it does not exist, and verbose=False will setup a default logger at level WARNING if no logging has been configured.

Saving & Loading

To save a trained neural network to disk, you can do the following after having initialized your multi-layer perceptron as the variable nn and trained it:

import pickle
pickle.dump(nn, open('nn.pkl', 'wb'))

After this, the file nn.pkl will be available in the current working directory — which you can reload at any time:

import pickle
nn = pickle.load(open('nn.pkl', 'rb'))

In this case, you can use the reloaded multi-layer perceptron as if it had just been trained. This will also work on different machines, whether CPU or GPU.

NOTE: You can serialize complex pipelines (for example from this section sklearn Pipeline) using this exact same approach.

Extracting Parameters

To access the weights and biases from the neural network layers, you can call the following function on any initialized neural network:

> nn.get_parameters()
[Parameters(layer='hidden0', weights=array([[...]]), biases=array([[...]])),
 Parameters(layer='output', weights=array(...), biases=array(...))]

The list is ordered in the same way as the layers parameter passed to the constructor. Each item in the list is a named-tuple with names (string), weights and biases (both numpy.array).