Python NeuralNet.load_from_file方法代码示例

# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import load_from_file [as 别名]
class StageRecognizer():
    def __init__(self, trained_net_path):
        self.net = NeuralNet()
        self.net.load_from_file(trained_net_path)

    def recognize_image(self, img):
        net_return = self.net.apply_over_data(extract_counter_feat(img))
        stage_number = int(round(net_return))
        stage = ''
        precision = 'strong'

        if stage_number == 1:
            stage = 'red'
            if abs(stage_number - 1) > .15:
                precision = 'weak'

        elif stage_number == 2:
            stage = 'yellow'
            if abs(stage_number - 1) > .15:
                precision = 'weak'

        elif stage_number == 3:
            stage = 'green'
            if abs(stage_number - 1) > .15:
                precision = 'weak'

        return stage, precision

# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import load_from_file [as 别名]
def findBin(frame):
	image=cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
	pil_im = Image.fromarray(image)
	#~ img1 = pil_im.resize((basewidth, height), Image.ANTIALIAS)
	pil_im.thumbnail((256, 256), Image.ANTIALIAS)
	img2 = pil_im.convert('1')
	#~ pixels = img2.load()
	pixels1 = np.asarray(img2.getdata(),dtype=np.bool)
	outstr = "outimg" +".bmp"
	img2.save(outstr)
	array01 = []
	count = 0
	Tot = 0
	for item in pixels1:
		Tot  += 1
		if not item:
			array01.append(1)
			count += 1
		else: 
			array01.append(0)
	testitem = []
	testitem.append(Instance(array01, [0]))
# load a stored network configuration
	network = NeuralNet.load_from_file( "plastic122.pkl" )
	arr = network.print_test(testitem)
	print('Value returned by neural network plastic: ' + str(arr[0]))
	
	network2 = NeuralNet.load_from_file( "metal122.pkl" )
	arr2 = network2.print_test(testitem)
	print('Value returned by neural network metal: ' + str(arr2[0]))
	
	network3 = NeuralNet.load_from_file( "paper122.pkl" )
	arr3 = network3.print_test(testitem)
	print('Value returned by neural network paper: ' + str(arr3[0]))
	
	pl = arr[0]
	me = arr2[0]
	pa = arr3[0]
	
	if((pl > pa and pl > me) or pl > 0.5 or (pa < 0.42 and me < 0.09) ):
		return 1 #plastic
	elif((me > pa and me > pl) or me > 0.13):
		return 3 #metal
	else:
		return 2 #paper

# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import load_from_file [as 别名]
                                        #~ # The last pair in you list describes the number of output signals
    #~ 
    #~ # Optional settings
    #~ "weights_low"           : -0.1,     # Lower bound on initial weight range
    #~ "weights_high"          : 0.1,      # Upper bound on initial weight range
    #~ "save_trained_network"  : True,    # Whether to write the trained weights to disk
    #~ 
    #~ "input_layer_dropout"   : 0.0,      # dropout fraction of the input layer
    #~ "hidden_layer_dropout"  : 0.0,      # dropout fraction in all hidden layers
#~ }

# initialize the neural network
#~ network = NeuralNet( settings )

# load a stored network configuration
network = NeuralNet.load_from_file( "network5.pkl" )

# Train the network using backpropagation
#~ backpropagation(
        #~ network,
        #~ training_one,          # specify the training set
        #~ ERROR_LIMIT     = 0.001, # define an acceptable error limit 
        #~ #max_iterations  = 100, # continues until the error limit is reach if this argument is skipped
                    #~ 
        #~ # optional parameters
        #~ learning_rate   = 0.03, # learning rate
        #~ momentum_factor = 0.4, # momentum
         #~ )
#~ 
#~ # Train the network using SciPy
#~ scipyoptimize(

# 需要导入模块: from neuralnet import NeuralNet [as 别名]
# 或者: from neuralnet.NeuralNet import load_from_file [as 别名]
def load_network():
    if os.path.isfile("networks/XOR_Operator/XOR_Operator.obj"):
        global network
        network = NeuralNet.load_from_file("networks/XOR_Operator/XOR_Operator.obj")
    else:
        raise ValueError("networks/XOR_Operator/XOR_Operator.obj")