class MLPClassifier(Theanifiable):
def __init__(self, D, H, C, n_layers):
super(MLPClassifier, self).__init__()
self.mlp = MultilayerPerceptron("mlp", D, H, C, n_layers=n_layers)
self.compile_method("errors")
@theanify(T.matrix("X"), T.ivector("y"))
def cost(self, X, y):
ypred = self.mlp.forward(X)
return T.nnet.categorical_crossentropy(ypred, y).mean()
@theanify(T.matrix("X"), T.ivector("y"))
def errors(self, X, y):
y_pred = self.mlp.forward(X).argmax(axis=1)
return T.mean(T.neq(y_pred, y))
def get_parameters(self):
return self.mlp.get_parameters()
def __init__(self, D, H, C, n_layers):
super(MLPClassifier, self).__init__()
self.mlp = MultilayerPerceptron("mlp", D, H, C, n_layers=n_layers)
self.compile_method("errors")