我尝试使用 sknn.mlp 训练一个 MLP 分类器来解决 XOR 问题

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)

这导致了以下结果：

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.

回答：

Scikit 似乎将你的 y 向量转换成了形状为 (n_samples,n_classes) 的二进制向量。在你的情况下，n_classes 为 2。因此尝试

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