rnn.py 文件源码

def errors(self, y):
        """Return a float representing the number of errors in the sequence
        over the total number of examples in the sequence ; zero one
        loss over the size of the sequence

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
                  correct label
        """
        # check if y has same dimension of y_pred
        if y.ndim != self.y_out.ndim:
            raise TypeError('y should have the same shape as self.y_out',
                ('y', y.type, 'y_out', self.y_out.type))

        if self.output_type in ('binary', 'softmax'):
            # check if y is of the correct datatype
            if y.dtype.startswith('int'):
                # the T.neq operator returns a vector of 0s and 1s, where 1
                # represents a mistake in prediction
                return T.mean(T.neq(self.y_out, y))
            else:
                raise NotImplementedError()