models.py 文件源码

def __init__(self, x, y, args):
        self.params_theta = []
        self.params_lambda = []
        self.params_weight = []
        if args.dataset == 'mnist':
            input_size = (None, 1, 28, 28)
        elif args.dataset == 'cifar10':
            input_size = (None, 3, 32, 32)
        else:
            raise AssertionError
        layers = [ll.InputLayer(input_size)]
        self.penalty = theano.shared(np.array(0.))

        #conv1
        layers.append(Conv2DLayerWithReg(args, layers[-1], 20, 5))
        self.add_params_to_self(args, layers[-1])
        layers.append(ll.MaxPool2DLayer(layers[-1], pool_size=2, stride=2))
        #conv1
        layers.append(Conv2DLayerWithReg(args, layers[-1], 50, 5))
        self.add_params_to_self(args, layers[-1])
        layers.append(ll.MaxPool2DLayer(layers[-1], pool_size=2, stride=2))
        #fc1
        layers.append(DenseLayerWithReg(args, layers[-1], num_units=500))
        self.add_params_to_self(args, layers[-1])
        #softmax
        layers.append(DenseLayerWithReg(args, layers[-1], num_units=10, nonlinearity=nonlinearities.softmax))
        self.add_params_to_self(args, layers[-1])

        self.layers = layers
        self.y = ll.get_output(layers[-1], x, deterministic=False)
        self.prediction = T.argmax(self.y, axis=1)
        # self.penalty = penalty if penalty != 0. else T.constant(0.)
        print(self.params_lambda)
        # time.sleep(20)
        # cost function
        self.loss = T.mean(categorical_crossentropy(self.y, y))
        self.lossWithPenalty = T.add(self.loss, self.penalty)
        print "loss and losswithpenalty", type(self.loss), type(self.lossWithPenalty)