opencv-python ANN 神经网络训练 minist 数据集，训练后Predict的返回值总是 nan 空矩阵，劳烦大神看看哪里出了问题

qq_42738260 2019-04-09 05:00:38



import os

import struct

import numpy as np

import cv2



def load_mnist(path, kind='train'):

    """Load MNIST data from `path`"""

    labels_path = os.path.join(path,

                               '%s-labels.idx1-ubyte'

                               % kind)

    images_path = os.path.join(path,

                               '%s-images.idx3-ubyte'

                               % kind)

    with open(labels_path, 'rb') as lbpath:

        magic, n = struct.unpack('>II',

                                 lbpath.read(8))

        labels = np.fromfile(lbpath,

                             dtype=np.uint8)



    with open(images_path, 'rb') as imgpath:

        magic, num, rows, cols = struct.unpack('>IIII',

                                               imgpath.read(16))

        images = np.fromfile(imgpath,

                             dtype=np.uint8).reshape(len(labels), 784)



    return images, labels



image, labels = load_mnist(r'C:\Users\Desktop\Python\Image_processing\data_number','train')



charNum = 10

'''将数据集的n维label 格式更新为 n X 10 '''

trainingLabel = []

for i in range(len(labels)):

    labelArraytoAppend = []

    for j in range(charNum):

        if labels[i] == j:

            labelArraytoAppend.append(1)

        else:

            labelArraytoAppend.append(0)

    trainingLabel.append(labelArraytoAppend)



'''训练多层神经网络 MLP'''

ann = cv2.ml.ANN_MLP_create()

ann.setActivationFunction(cv2.ml.ANN_MLP_SIGMOID_SYM)

ann.setTrainMethod(cv2.ml.ANN_MLP_BACKPROP)

ann.setBackpropMomentumScale(0.1)

ann.setBackpropWeightScale(0.001)

ann.setTermCriteria((cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS, 500, 0.05))



ann.setLayerSizes(np.array([np.array(image).shape[1], 128, charNum], dtype=np.uint16))



ann.train(np.array(image, dtype=np.float32), cv2.ml.ROW_SAMPLE, np.array(trainingLabel, dtype=np.float32))

ann.save(r'C:\Users\bj0lt5\Desktop\Python\Image_processing\data_number\characterRecognize.xml')



'''测试神经网络'''

testImage, testLabels = load_mnist(r'C:\Users\Desktop\Python\Image_processing\data_number', 't10k')

#result = np.zeros((testLabels.shape[0], charNum), dtype=np.float32)

retval, result = ann.predict(np.array(image, dtype=np.float32))

print(result[:2])