Example #1
0
	def __init__(self, layer_structure, net_name):
		# load the cifar-10 images and their corresponding labels
		cifar_data = load_cifar.images_to_volumes("image_data/data_batch_1")
		test_data = load_cifar.images_to_volumes("image_data/data_batch_2")
		self.image_volumes = cifar_data[0]
		self.image_labels = cifar_data[1]

		test_volumes = test_data[0]
		test_labels = test_data[1]
		self.net_name = net_name
		# generate the layers based off of layer definitions from user
		self.layers = []

		if layer_structure == None:
			print "~~ Loading network '%s' from saved_networks directory..." % (net_name)
			self.pretrained = True
			self.layer_structure = self.load_structure()
			self.build_layers(self.layer_structure)
			self.load_params()
			print "~~ Done!"
		else:
			print "~~ Initializing untrained network..."
			self.pretrained = False
			self.layer_structure = layer_structure
			self.build_layers(self.layer_structure)
			self.initialize_params()
			print "~~ Done!"
		print
Example #2
0
	def __init__(self, network, learning_rate):
		self.network = network
		self.learning_rate = learning_rate
		self.weight_decay = 0.0001

		# accumulators for the gradient and parameters (used in adadelta)
		self.grad_accumated = []
		self.update_accumated = []

		# import all the batches
		batch_one = load_cifar.images_to_volumes("image_data/data_batch_1")
		batch_two = load_cifar.images_to_volumes("image_data/data_batch_2")
		batch_three = load_cifar.images_to_volumes("image_data/data_batch_3")
		batch_four = load_cifar.images_to_volumes("image_data/data_batch_4")
		batch_test = load_cifar.images_to_volumes("image_data/data_batch_5")

    # define all the volumes and their labels
		self.vol_one = batch_one[0]
		self.lab_one = batch_one[1]

		self.vol_two = batch_two[0]
		self.lab_two = batch_two[1]

		self.vol_three = batch_three[0]
		self.lab_three = batch_three[1]

		self.vol_four = batch_four[0]
		self.lab_four = batch_four[1]

		self.vol_test = batch_test[0]
		self.lab_test = batch_test[1]