scikit-neuralnetwork mismatch error in dataset size
Question: I'm trying to train an MLP classifier for the XOR problem using sknn.mlp

    
    
    from sknn.mlp import Classifier, Layer
    X=numpy.array([[0,1],[0,0],[1,0]])
    print X.shape
    y=numpy.array([[1],[0],[1]])
    print y.shape
    nn=Classifier(layers=[Layer("Sigmoid",units=2),Layer("Sigmoid",units=1)],n_iter=100)
    nn.fit(X,y)
    

This results in:

    
    
    No handlers could be found for logger "sknn"
    Traceback (most recent call last):
    File "xorclassifier.py", line 10, in <module>
    nn.fit(X,y)
    File "/usr/local/lib/python2.7/site-packages/sknn/mlp.py", line 343, in fit
    return super(Classifier, self)._fit(X, yp)
    File "/usr/local/lib/python2.7/site-packages/sknn/mlp.py", line 179, in _fit
    X, y = self._initialize(X, y)
    File "/usr/local/lib/python2.7/site-packages/sknn/mlp.py", line 37, in _initialize
    self._create_specs(X, y)
    File "/usr/local/lib/python2.7/site-packages/sknn/mlp.py", line 64, in _create_specs
    "Mismatch between dataset size and units in output layer."
    AssertionError: Mismatch between dataset size and units in output layer.
    


Answer: Scikit seems to turn your `y` vector into a binary vector of shape
(n_samples,n_classes). n_classes is in your case two. So try

    
    
    nn=Classifier(layers=[Layer("Sigmoid",units=2),Layer("Sigmoid",units=2)],n_iter=100)
    

