def __init__(self, model='bvlc_alexnet.caffemodel',mean='misc/ilsvrc_2012_mean.npy',labels='misc/labels.txt',optimizer=None):
		super(AlexNet,self).__init__('AlexNet',in_size=227)


		self.func = LoadCaffeModel(model)
		self.labels = np.loadtxt(labels, str, delimiter="\t")

		if 0 >= Deel.gpu:
			cuda.check_cuda_available()


		if Deel.gpu >= 0:
			cuda.get_device(self.gpu).use()
			self.func.to_gpu()

		#ImageNet.mean_image = np.load(mean)
		mean_image = np.load(mean)
		ImageNet.mean_image=mean_image
		
		cropwidth = 256 - self.in_size
		start = cropwidth // 2
		stop = start + self.in_size
		self.mean_image = mean_image[:, start:stop, start:stop].copy()
		#del self.func.layers[15:23] 
		#self.outname = 'pool5'

		self.batchsize = 1
		self.x_batch = np.ndarray((self.batchsize, 3, self.in_size, self.in_size), dtype=np.float32)
dataset/ETHPy150Open uei/deel/deel/network/alexnet.py/AlexNet.__init__/swapped operands of `>=`