数据集大小中的scikit-neuralnetwork不匹配错误 [英] scikit-neuralnetwork mismatch error in dataset size

问题描述

我正在尝试使用sknn.mlp训练针对XOR问题的MLP分类器

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)

结果是:

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.

推荐答案

Scikit似乎将您的y向量变成了形状为(n_samples，n_classes)的二进制向量.在您的情况下，n_classes是两个.因此，尝试

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)

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