layer.py 文件源码

def errors(self, y):
        """
        returns a float representing the number of errors in the minibatch
        over the total number of examples of the minibatch. Zero one loss
        over the size of the minibatch.

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
        correct label.
        """
        if y.ndim != self.y_decision.ndim:
            raise TypeError("y should have the same shape as self.y_decision",
                            ('y', y.type, "y_decision", self.y_decision.type))
        if y.dtype.startswith('int') or y.dtype.startswith('uint'):
            # The T.neq operator returns a vector of 0s and 1s, where:
            # 1 represents a mistake in classification
            return T.mean(T.neq(self.y_decision, y))
        else:
            raise NotImplementedError()