losses.py 文件源码

def mean_squared_error(weights=1.0, name='MeanSquaredError', scope=None, collect=True):
    """Computes Mean Square Loss.

    Args:
        weights: Coefficients for the loss a `scalar`.
        scope: scope to add the op to.
        name: name of the op.
        collect: add to losses collection.

    Returns:
        A scalar `Tensor` representing the loss value.

    Raises:
        ValueError: If `predictions` shape doesn't match `labels` shape, or `weights` is `None`.
    """

    def inner_loss(y_true, y_pred):
        losses = math_ops.square(math_ops.subtract(y_pred, y_true))
        return losses
    return built_loss(inner_loss, weights, name, scope, collect)