Replace 'loss' parameter by 'objective' which has a more interesting interface.

[dnouri]

Adds a more interesting interface for specifying losses. The loss parameter is now deprecated, and one should use objective instead. Here's an example of an 'Objective' implementation from nolearn.lasagne itself.

class LossObjective(object):
    def __init__(self, loss_function):
        self.loss_function = loss_function

    def __call__(self, layers, X, y, deterministic=False):
        output_layer = layers['output']
        return self.loss_function(
            output_layer.get_output(X, deterministic=deterministic), y)

@cancan101 Let me know if this makes sense to you, and if it would fix #14.

[cancan101]

Looks good to me.

[pengpaiSH]

@dnouri , @cancan101, I am still confused why we could introduce e.g. L2 regularization by such LossObject class.

[cancan101]

The current signature for loss is not rich enough to calculate the penalty on the weights.