Question

我已经使用bin下的caffe工具训练了一个模型，现在我正尝试使用python脚本进行测试，我读取了图像并对其进行了预处理（就像我对训练数据集所做的那样），然后将预训练的权重加载到网络，但我几乎总是（99.99％的时间）为每个测试图像收到相同的结果-0-。我确实认为我的模型可能过拟合，但是在训练了一些模型之后，我开始意识到我从预测中获得的标签很可能是原因。我也增加了辍学率，并采取了随机作物的方法来克服过度拟合的问题，我有大约60K的训练资源。数据集也大致平衡。在培训评估阶段，我的准确度在77到87之间（取决于我处理数据的方式，使用的架构等）

对不起，我的超级hacky代码已经有一段时间了，我一直在进行caffe测试，所以我怀疑问题出在我如何将输入数据传递到网络上，但是我无法动手：

import h5py, os
import sys
sys.path.append("/home/X/Desktop/caffe-caffe-0.16/python")
from caffe.io import oversample
from caffe.io import resize_image
import caffe
from random import randint
import numpy as np
import cv2
import matplotlib.pyplot as plt
from collections import Counter as Cnt

meanImg = cv2.imread('/home/caffe/data/Ch/Final_meanImg.png')

model_def = '/home/X/Desktop/caffe-caffe-0.16/models/bvlc_googlenet/deploy.prototxt'
model_weights = '/media/X/DATA/SDet/Google__iter_140000.caffemodel'

# load the model
#caffe.set_mode_gpu()
#caffe.set_device(0)

net = caffe.Net(model_def,      # defines the structure of the model
                model_weights,  # contains the trained weights
                caffe.TEST)     # use test mode (e.g., don't perform dropout)


with open( '/home/caffe/examples/sdet/SDet/test_random.txt', 'r' ) as T, open('/media/X/DATA/SDet/results/testResults.txt','w') as testResultsFile:


    readImgCounter = 0
    runningCorrect = 0
    runningAcc = 0.0

    #testResultsFile.write('filename'+' '+'prediction'+' '+'GT')
    lines = T.readlines()

    for i,l in enumerate(lines):


        sp = l.split(' ')

        video = sp[0].split('_')[0]

        impath =  '/home/caffe/data/Ch/images/'+video+'/'+sp[0] +'.jpg' 

        img = cv2.imread(impath)

        resized_img = resize_image(img, (255,255))

        oversampledImages = oversample([resized_img], (224,224)) #5 crops x 2 mirror flips = return 10 images

        transposed_img = np.zeros( (10, 3, 224, 224), dtype='f4' ) 
        tp =  np.zeros( (1, 3, 224, 224), dtype='f4' ) 

        predictedLabels = []

        for j in range(0,oversampledImages.shape[0]-1):
            transposed_img[j] = oversampledImages[j].transpose((2,0,1))


            tp[0] = transposed_img[j]
            net.blobs['data'].data[0]  = tp

            pred =  net.forward(data=tp)

            predictedLabels.append(pred['prob'].argmax())


        print(predictedLabels)

        prediction,num_most_common = Cnt(predictedLabels).most_common(1)[0]


        print(prediction)

        readImgCounter = readImgCounter + 1

        if (prediction == int(sp[1])):
            runningCorrect = runningCorrect + 1

        runningAcc = runningCorrect / readImgCounter
        print('runningAcc:')
        print(runningAcc)
        print('-----------')
        print('runningCorrect:')
        print(runningCorrect)
        print('-----------')
        print('totalImgRead:')
        print(readImgCounter)
        print('-----------')

        testResultsFile.write(sp[0]+' '+str(prediction)+' '+sp[1])
        testResultsFile.write('\n')

Answer 1

我终于解决了这个问题。我不确定百分百有效，但最有可能在学习时将偏差更改为0。

Caffe总是返回一个标签

1 个答案: