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VGG Model 的 Output 在训练后变得恒定，并且损失/准确性没有提高

[英]Output of VGG Model becomes constant after training and Loss/Accuracy are not improving

 原文  2021-05-25 07:41:31  9   1    python/ tensorflow/ keras/ vgg-net/ image-classification

问题描述

I'm trying to implement a slightly smaller version of VGG16 and train it from scratch on a dataset of about 6000 images (5400 for training and 600 for validation).我正在尝试实现一个稍微小一点的 VGG16 版本，并在大约 6000 个图像的数据集上从头开始训练它（5400 个用于训练，600 个用于验证）。 I chose a batch size of 30 so that it can neatly fit within the dataset, otherwise I would get his with IncompatibleShape error during training.我选择了 30 的批量大小，以便它可以整齐地放入数据集中，否则我会在训练期间得到他的 IncompatibleShape 错误。

After going through 15-20 epochs, the EarlyStopping callback kicks in and stops the training.经过 15-20 个 epoch 后，EarlyStopping 回调开始并停止训练。

I'm facing two issues with this model我遇到了这个 model 的两个问题

  1. After this, when I pass in test images into the model, the output seems to remain constant.之后，当我将测试图像传入 model 时，output 似乎保持不变。 The least I expect is that for imageA, predicted output should be different from imageB.我最不希望的是，对于 imageA，预测的 output 应该与 imageB 不同。 I'm unable to figure out why this is the case我无法弄清楚为什么会这样
  2. Loss and Accuracy seem not to change much.损失和准确性似乎没有太大变化。 I was expecting that the accuracy would go up at least to about 50% for the number of epochs, but it does not go above 23%.我期望 go 对于 epoch 的数量至少达到 50% 左右，但 go 不会超过 23%。 I have tried to include steps_per_epoch, ReduceLROnPlateau but they doesn't seem make any dent.我试图包括 steps_per_epoch、ReduceLROnPlateau 但它们似乎没有任何影响。

Training Output:培训 Output：

Epoch 1/50
180/180 [==============================] - 50s 278ms/step - loss: 1.6095 - categorical_accuracy: 0.1987 - val_loss: 1.6109 - val_categorical_accuracy: 0.1267

Epoch 00001: val_loss improved from inf to 1.61094, saving model to vgg16.h5
Epoch 2/50
180/180 [==============================] - 51s 285ms/step - loss: 1.6095 - categorical_accuracy: 0.2044 - val_loss: 1.6107 - val_categorical_accuracy: 0.2133

Epoch 00002: val_loss improved from 1.61094 to 1.61067, saving model to vgg16.h5
Epoch 3/50
180/180 [==============================] - 51s 285ms/step - loss: 1.6098 - categorical_accuracy: 0.1946 - val_loss: 1.6106 - val_categorical_accuracy: 0.1400

Epoch 00003: val_loss improved from 1.61067 to 1.61059, saving model to vgg16.h5
Epoch 4/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6095 - categorical_accuracy: 0.1928 - val_loss: 1.6098 - val_categorical_accuracy: 0.2000

Epoch 00004: val_loss improved from 1.61059 to 1.60983, saving model to vgg16.h5

Epoch 00004: ReduceLROnPlateau reducing learning rate to 2.5000001187436283e-05.
Epoch 5/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6093 - categorical_accuracy: 0.2033 - val_loss: 1.6103 - val_categorical_accuracy: 0.1467

Epoch 00005: val_loss did not improve from 1.60983
Epoch 6/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6094 - categorical_accuracy: 0.1989 - val_loss: 1.6106 - val_categorical_accuracy: 0.1400

Epoch 00006: val_loss did not improve from 1.60983
Epoch 7/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6094 - categorical_accuracy: 0.2069 - val_loss: 1.6098 - val_categorical_accuracy: 0.1733

Epoch 00007: val_loss improved from 1.60983 to 1.60978, saving model to vgg16.h5

Epoch 00007: ReduceLROnPlateau reducing learning rate to 1e-05.
Epoch 8/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6093 - categorical_accuracy: 0.2076 - val_loss: 1.6103 - val_categorical_accuracy: 0.1600

Epoch 00008: val_loss did not improve from 1.60978
Epoch 9/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6095 - categorical_accuracy: 0.2006 - val_loss: 1.6097 - val_categorical_accuracy: 0.2200

Epoch 00009: val_loss improved from 1.60978 to 1.60975, saving model to vgg16.h5
Epoch 10/50
180/180 [==============================] - 52s 287ms/step - loss: 1.6095 - categorical_accuracy: 0.2043 - val_loss: 1.6101 - val_categorical_accuracy: 0.1667

Epoch 00010: val_loss did not improve from 1.60975
Epoch 11/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6094 - categorical_accuracy: 0.2009 - val_loss: 1.6102 - val_categorical_accuracy: 0.1800

Epoch 00011: val_loss did not improve from 1.60975
Epoch 12/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6095 - categorical_accuracy: 0.2041 - val_loss: 1.6115 - val_categorical_accuracy: 0.1600

Epoch 00012: val_loss did not improve from 1.60975
Epoch 13/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6095 - categorical_accuracy: 0.1989 - val_loss: 1.6108 - val_categorical_accuracy: 0.1867

Epoch 00013: val_loss did not improve from 1.60975
Epoch 14/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6094 - categorical_accuracy: 0.2009 - val_loss: 1.6102 - val_categorical_accuracy: 0.1733

Epoch 00014: val_loss did not improve from 1.60975
Epoch 15/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6093 - categorical_accuracy: 0.2074 - val_loss: 1.6113 - val_categorical_accuracy: 0.1467

Epoch 00015: val_loss did not improve from 1.60975
Epoch 16/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6098 - categorical_accuracy: 0.1983 - val_loss: 1.6105 - val_categorical_accuracy: 0.1867

Epoch 00016: val_loss did not improve from 1.60975
Epoch 17/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6095 - categorical_accuracy: 0.2056 - val_loss: 1.6119 - val_categorical_accuracy: 0.1667

Epoch 00017: val_loss did not improve from 1.60975
Epoch 18/50
180/180 [==============================] - 52s 286ms/step - loss: 1.6093 - categorical_accuracy: 0.1994 - val_loss: 1.6110 - val_categorical_accuracy: 0.1800

Epoch 00018: val_loss did not improve from 1.60975
Epoch 19/50
180/180 [==============================] - 51s 286ms/step - loss: 1.6095 - categorical_accuracy: 0.2026 - val_loss: 1.6103 - val_categorical_accuracy: 0.1667

Epoch 00019: val_loss did not improve from 1.60975
Restoring model weights from the end of the best epoch.
Epoch 00019: early stopping

Code used to get Predictions:用于获取预测的代码：

predictions = []
actuals=[]

for i, (images, labels) in enumerate( test_datasource):
  if i > 2:
    break
  pred = model_2(images)
  print(labels.shape, pred.shape)
  for j in range(len(labels)):
    actuals.append( labels[j])
    predictions.append(pred[j])
    print(labels[j].numpy(), "\t", pred[j].numpy())

Output of the above code:上述代码的Output：

(30, 5) (30, 5)
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
(30, 5) (30, 5)
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
(30, 5) (30, 5)
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 0. 1.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[1. 0. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 1. 0. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 0. 1. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]
[0. 0. 1. 0. 0.]     [0.19907779 0.20320047 0.1968051  0.20173152 0.19918515]

Here is the model summary:这是 model 摘要：

Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(30, 224, 224, 3)]       0         
_________________________________________________________________
conv_1_1 (Conv2D)            (30, 224, 224, 32)        896       
_________________________________________________________________
conv_1_2 (Conv2D)            (30, 224, 224, 32)        9248      
_________________________________________________________________
maxpool_1 (MaxPooling2D)     (30, 112, 112, 32)        0         
_________________________________________________________________
conv_2_1 (Conv2D)            (30, 112, 112, 64)        18496     
_________________________________________________________________
conv_2_2 (Conv2D)            (30, 112, 112, 64)        36928     
_________________________________________________________________
maxpool_2 (MaxPooling2D)     (30, 56, 56, 64)          0         
_________________________________________________________________
conv_3_1 (Conv2D)            (30, 56, 56, 128)         73856     
_________________________________________________________________
conv_3_2 (Conv2D)            (30, 56, 56, 128)         147584    
_________________________________________________________________
conv_3_3 (Conv2D)            (30, 56, 56, 128)         147584    
_________________________________________________________________
maxpool_3 (MaxPooling2D)     (30, 28, 28, 128)         0         
_________________________________________________________________
conv_4_1 (Conv2D)            (30, 28, 28, 256)         295168    
_________________________________________________________________
conv_4_2 (Conv2D)            (30, 28, 28, 256)         590080    
_________________________________________________________________
conv_4_3 (Conv2D)            (30, 28, 28, 256)         590080    
_________________________________________________________________
maxpool_4 (MaxPooling2D)     (30, 14, 14, 256)         0         
_________________________________________________________________
conv_5_1 (Conv2D)            (30, 14, 14, 256)         590080    
_________________________________________________________________
conv_5_2 (Conv2D)            (30, 14, 14, 256)         590080    
_________________________________________________________________
conv_5_3 (Conv2D)            (30, 14, 14, 256)         590080    
_________________________________________________________________
maxpool_5 (MaxPooling2D)     (30, 7, 7, 256)           0         
_________________________________________________________________
flatten (Flatten)            (30, 12544)               0         
_________________________________________________________________
fc_1 (Dense)                 (30, 4096)                51384320  
_________________________________________________________________
dropout_1 (Dropout)          (30, 4096)                0         
_________________________________________________________________
fc_2 (Dense)                 (30, 4096)                16781312  
_________________________________________________________________
dropout_2 (Dropout)          (30, 4096)                0         
_________________________________________________________________
output (Dense)               (30, 5)                   20485     
=================================================================
Total params: 71,866,277
Trainable params: 71,866,277
Non-trainable params: 0

The code is here in Google Colab: https://colab.research.google.com/drive/1AWe87Zb3MvF90j3RS7sv3OiSgR86q4j_代码在 Google Colab 中： https://colab.research.google.com/drive/1AWe87Zb3MvF90j3RS7sv3OiSgR86q4j_

I tried two versions of VGG-16, one with half the depth of filters than the original and the second with quarter of the depth of filters.我尝试了两种版本的 VGG-16，一种是滤镜深度是原来的一半，第二个是滤镜深度的四分之一。

1 个解决方案

解决方案1
 0  2021-05-25 13:20:39

I was under the impression that the issue was that the loss and accuracy were unchanging and that is why, when using the model for predictions the outputs were fixed and unchanging irrespective of the inputs being fed to it.我的印象是，问题在于损失和准确性是不变的，这就是为什么当使用 model 进行预测时，无论输入什么输入，输出都是固定不变的。

When I reinitialized the model by calling the Model(inputs...,outputs...) function, and passed inputs to it without training, the outputs were at least changing.当我通过调用模型（输入...，输出...） function 重新初始化 model 并在没有训练的情况下将输入传递给它时，输出至少发生了变化。

I tried with multiple learning rates and optimizers with no change in behavior of the model.我尝试了多种学习率和优化器，model 的行为没有改变。

After some more google searching I chanced upon these articles:经过更多的谷歌搜索后，我偶然发现了这些文章：

  1. https://www.quora.com/Why-does-my-convolutional-neural-network-always-produce-the-same-outputs https://www.quora.com/Why-does-my-convolutional-neural-network-always-produce-the-same-outputs
  2. https://www.quora.com/Why-does-my-own-neural-network-give-me-the-same-output-for-different-input-sets https://www.quora.com/Why-does-my-own-neural-network-give-me-the-same-output-for-different-input-sets
  3. https://datascience.stackexchange.com/questions/5706/what-is-the-dying-relu-problem-in-neural-networks https://datascience.stackexchange.com/questions/5706/what-is-the-dying-relu-problem-in-neural-networks

I made two changes in the code to get it to finally work.我对代码进行了两次更改以使其最终正常工作。

  1. I was initially only dividing the image array by 255 to get the values to fall between 0 and 1 followed by subtracting 0.5 from the result to cause the values to fall between -0.5 to 0.5.我最初只是将图像数组除以 255 以使值介于 0 和 1 之间，然后从结果中减去 0.5 以使值介于 -0.5 到 0.5 之间。 This was changed to the use of tf.image.per_image_standardization(images-127) and dividing the result by the maximum value in each image.这已更改为使用 tf.image.per_image_standardization(images-127) 并将结果除以每个图像中的最大值。 As a result the image values fell between -1 and +1结果，图像值介于 -1 和 +1 之间
  2. Another major cause of fixed outputs is the model's relu units dying (or saturating) during training.固定输出的另一个主要原因是模型的 relu 单元在训练期间死亡（或饱和）。 relu activation function inherently has this problem where once a variable's weight goes to 0, it does not recover from it. relu 激活 function 天生就有这个问题，一旦变量的权重变为 0，它就不会从中恢复。 Although high learning rate is said to cause this problem, I was unable to find a learning rate that alleviated from this issue.尽管据说高学习率会导致此问题，但我无法找到缓解此问题的学习率。 Another solution is to change the activation function to leaky relu or elu (Exponential relu) which have an inherent mechanism to recover from this issue另一种解决方案是将激活 function 更改为leaky relu 或 elu (Exponential relu)，它们具有从这个问题中恢复的固有机制

With these changes, the model's loss dropped to < 1 and training-accuracy <0.90 although validation numbers were not this good, but certainly better than before随着这些变化，模型的损失下降到 < 1 和训练准确率 <0.90，虽然验证数字不是那么好，但肯定比以前更好

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