Adding full coverage for the new codepaths in tests.

sknn/ae.py

@@ -80,8 +80,10 @@ def __init__(self,
             raise NotImplementedError("AutoEncoder layer type `%s` is not implemented." % type)
         if cost not in ['msre', 'mbce']:
             raise NotImplementedError("Error type '%s' is not implemented." % cost)
+        if activation not in ['Sigmoid', 'Tanh']:
+            raise NotImplementedError("Activation type '%s' is not implemented." % activation)
 
-        self.activation = activation.lower()
+        self.activation = activation
         self.type = type
         self.name = name
         self.units = units
@@ -161,18 +163,27 @@ def transform(self, X):
         return self.dca.perform(X)
 
     def transfer(self, nn):
+        for a, l in zip(self.layers, nn.layers):
+            assert a.activation == l.type,\
+                "Mismatch in activation types in target MLP; expected `%s` but found `%s`."\
+                % (a.activation, l.type)
+            assert a.units == l.units,\
+                "Different number of units in target MLP; expected `%i` but found `%i`."\
+                % (a.units, l.units)
+
         nn.weights = []
         for a in self.dca.autoencoders:
             nn.weights.append((a.weights.get_value(), a.hidbias.get_value()))
 
     def _create_ae_layer(self, size, layer):
         """Construct an internal pylearn2 layer based on the requested layer type.
         """
+        activation = layer.activation.lower()
         if layer.type == 'autoencoder':
             return autoencoder.Autoencoder(size,
                                            layer.units,
-                                           layer.activation,
-                                           layer.activation,
+                                           activation,
+                                           activation,
                                            layer.tied_weights,
                                            rng=self.random_state)
         if layer.type == 'denoising':
@@ -181,8 +192,8 @@ def _create_ae_layer(self, size, layer):
             return autoencoder.DenoisingAutoencoder(corruptor,
                                                     size,
                                                     layer.units,
-                                                    layer.activation,
-                                                    layer.activation,
+                                                    activation,
+                                                    activation,
                                                     tied_weights=layer.tied_weights,
                                                     rng=self.random_state)
 

sknn/tests/test_ae.py

@@ -4,7 +4,7 @@
 import numpy
 
 from sknn.ae import AutoEncoder as AE, Layer as L
-
+from sknn import mlp
 
 class TestAutoEncoder(unittest.TestCase):
 
@@ -17,6 +17,29 @@ def test_FitData(self):
         ae = AE(layers=[L("Sigmoid", units=8)], n_iter=1)
         ae.fit(X)
 
+    def test_FitVerbose(self):
+        X = numpy.zeros((8,4))
+        ae = AE(layers=[L("Sigmoid", units=8)], n_iter=1, verbose=1)
+        ae.fit(X)
+
+    def test_TransferSuccess(self):
+        X = numpy.zeros((8,4))
+        ae = AE(layers=[L("Tanh", units=4)], n_iter=1)
+        ae.fit(X)
+
+        nn = mlp.MultiLayerPerceptron(
+                layers=[mlp.Layer("Tanh", units=4)])
+        ae.transfer(nn)
+
+    def test_TransferFailure(self):
+        X = numpy.zeros((8,4))
+        ae = AE(layers=[L("Tanh", units=8)], n_iter=1)
+        ae.fit(X)
+
+        nn = mlp.MultiLayerPerceptron(
+                layers=[mlp.Layer("Tanh", units=4)])
+        assert_raises(AssertionError, ae.transfer, nn)
+
 
 class TestParameters(unittest.TestCase):
 
@@ -43,3 +66,6 @@ def test_UnknownCostFunction(self):
 
     def test_UnknownType(self):
         assert_raises(NotImplementedError, L, "Sigmoid", type="unknown")
+
+    def test_UnknownActivation(self):
+        assert_raises(NotImplementedError, L, "Unknown")