eval-all.py 文件源码

def test_alex(self):

    class_index = 0
    image_index = 0
    total_count = 0.0
    accept_sum = 0
    actual = []
    predict = []

    for filename in filenames:
        #query-feature
        X=self.read_imagelist(filelist_path + filename + extension)
        test_num=np.shape(X)[0]
        out = self.forward_all(data=X)
        predicts=out[self.outputs[0]]
        predicts=np.reshape(predicts,(test_num,10))
        confusion_array = np.zeros((class_size), dtype = np.int)
        for i in range(test_num):
        actual.append(class_index)
        for j in range(class_size):    
           if np.max(predicts[i]) == predicts[i][j]:
            confusion_array[j] += 1 
            predict.append(j)
        image_index += 1
        #print(confusion_array)
        total_count += test_num
        accept_sum += confusion_array[class_index]
        class_index += 1

    print 'total:%d' % (round(total_count))
    print 'accept:%d' % (accept_sum)
    print 'reject:%d' % (round(total_count) - accept_sum)
    print 'accuray:%.4f' % (accept_sum / total_count)

    #conf_mat = confusion_matrix(actual,predict)
    #print(conf_mat)
    #actual = np.array(actual)
    #predict = np.array(predict)
    #y_actual = pd.Series(actual, name='Actual')
    #y_predict = pd.Series(predict, name='Predicted')
    #df_confusion = pd.crosstab(y_actual,y_predict, rownames=['Actual'], colnames=['Predicted'], margins=True)
    #print(df_confusion)
    #plot_confusion_matrix(df_confusion)
    return (accept_sum / total_count)

    #process a text file