loss.py 文件源码

def loss(self, y_true, y_pred, mean=True):
        scale_factor = self.scale_factor
        eps = self.eps

        with tf.name_scope(self.scope):
            y_true = tf.cast(y_true, tf.float32)
            y_pred = tf.cast(y_pred, tf.float32) * scale_factor

            if self.masking:
                nelem = _nelem(y_true)
                y_true = _nan2zero(y_true)

            # Clip theta
            theta = tf.minimum(self.theta, 1e6)

            t1 = tf.lgamma(theta+eps) + tf.lgamma(y_true+1.0) - tf.lgamma(y_true+theta+eps)
            t2 = (theta+y_true) * tf.log(1.0 + (y_pred/(theta+eps))) + (y_true * (tf.log(theta+eps) - tf.log(y_pred+eps)))

            if self.debug:
                assert_ops = [
                        tf.verify_tensor_all_finite(y_pred, 'y_pred has inf/nans'),
                        tf.verify_tensor_all_finite(t1, 't1 has inf/nans'),
                        tf.verify_tensor_all_finite(t2, 't2 has inf/nans')]

                tf.summary.histogram('t1', t1)
                tf.summary.histogram('t2', t2)

                with tf.control_dependencies(assert_ops):
                    final = t1 + t2

            else:
                final = t1 + t2

            final = _nan2inf(final)

            if mean:
                if self.masking:
                    final = tf.divide(tf.reduce_sum(final), nelem)
                else:
                    final = tf.reduce_mean(final)


        return final