In this module, a neural network is made up of multiple layers — hence the name multi-layer perceptron! You need to specify these layers by instantiating one of two types of specifications:
sknn.mlp.Layer: A standard feed-forward layer that can use linear or non-linear activations.sknn.mlp.Convolution: An image-based convolve operation with shared weights, linear or not.
In practice, you need to create a list of these specifications and provide them as the layers parameter to the sknn.mlp.Regressor or sknn.mlp.Classifier constructors.
sknn.mlp.Layer
sknn.mlp.Convolution
Most of the functionality provided to simulate and train multi-layer perceptron is implemented in the (abstract) class sknn.mlp.MultiLayerPerceptron. This class documents all the construction parameters for Regressor and Classifier derived classes (see below), as well as their various helper functions.
sknn.mlp.MultiLayerPerceptron
When using the multi-layer perceptron, you should initialize a Regressor or a Classifier directly.
See the class sknn.mlp.MultiLayerPerceptron for inherited construction parameters.
sknn.mlp.Regressor
Also check the sknn.mlp.MultiLayerPerceptron class for inherited construction parameters.
sknn.mlp.Classifier