Working version of the deepy backend (for dense MLPs) using the new…

… architecture.

sknn/backend/base.py

@@ -0,0 +1,11 @@
+# -*- coding: utf-8 -*-
+from __future__ import (absolute_import, unicode_literals, print_function)
+
+
+class BackendBase(object):
+
+    def __init__(self, spec):
+        self.spec = spec
+
+    def __getattr__(self, key):
+        return getattr(self.spec, key)

sknn/backend/deepy/mlp.py

@@ -26,16 +26,17 @@
 from deepy.trainers import MomentumTrainer, LearningRateAnnealer
 
 from ...nn import Layer, Convolution, ansi
-from ...nn import NeuralNetwork
+from ..base import BackendBase
 
 
-class MultiLayerPerceptron(NeuralNetwork):
+class MultiLayerPerceptron(BackendBase):
     """
     Abstract base class for wrapping the multi-layer perceptron functionality
     from ``deepy``.
     """
 
-    def _setup(self):
+    def __init__(self, spec):
+        super(MultiLayerPerceptron, self).__init__(spec)
         self.iterations = 0        
         self.trainer = None
         self.mlp = None
@@ -95,7 +96,7 @@ def _create_mlp(self):
         model.stack_layer(Softmax())
         self.mlp = model
 
-    def _initialize(self, X, y=None):
+    def _initialize_impl(self, X, y=None):
         assert not self.is_initialized,\
             "This neural network has already been initialized."
         self._create_specs(X, y)

sknn/backend/pylearn2/nn.py

@@ -16,14 +16,10 @@
 from .pywrap2 import learning_rule as lr, termination_criteria as tc
 from .dataset import SparseDesignMatrix, FastVectorSpace
 
+from ..base import BackendBase
 
-class NeuralNetwork(object):
 
-    def __init__(self, spec):
-        self.spec = spec
-
-    def __getattr__(self, key):
-        return getattr(self.spec, key)
+class NeuralNetwork(BackendBase):
 
     def _create_input_space(self, X):
         if self.is_convolution: