layer.py 文件源码

def error_per_calss(self, y):
        """
        Return an array where each value is the error for the corresponding
        classe in 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'):
            y_decision_res = T.neq(self.y_decision, y)
            for (i, y_decision_r) in enumerate(y_decision_res):
                self.n_classes_seen[y[i]] += 1
                if y_decision_r:
                    self.n_wrong_classif_made[y[i]] += 1
            pred_per_class = self.n_wrong_classif_made / self.n_classes_seen
            return T.mean(y_decision_res), pred_per_class
        else:
            raise NotImplementedError()