adver.py 文件源码

def __init__(self, mdl, x):
        self.loss_value = None
        self.grad_values = None
        self.mdl = mdl

        loss = K.variable(0.)
        layer_dict = dict([(layer.name, layer) for layer in mdl.layers])

        inp = layer_dict['face'].output
        out = layer_dict['conf'].output

        loss -= K.sum(out)
        # Might want to add some L2-loss in here, depending on output
        # loss += 0.0005 * K.sum(K.square(inp - x))
        grads = K.gradients(loss, inp)

        outputs = [loss]
        if type(grads) in {list, tuple}:
            outputs += grads
        else:
            outputs.append(grads)

        self.f_outputs = K.function([inp, K.learning_phase()], outputs)