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Val_accuracy 没有增加

[英]Val_accuracy not increasing

 原文  2021-12-14 10:19:14  6   1    python/ deep-learning/ tf.keras

问题描述

I'm new to this technology, so I was trying to build a model on image dataset.我是这项技术的新手，所以我试图在图像数据集上构建一个 model。 I have used this architecture -我使用过这种架构 -

model = keras.Sequential()

model.add(layers.Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(32,32,1)))
model.add(layers.AveragePooling2D())

model.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(layers.AveragePooling2D())

model.add(layers.Flatten())

model.add(layers.Dense(units=120, activation='relu'))

model.add(layers.Dense(units=84, activation='relu'))

model.add(layers.Dense(units=1, activation = 'sigmoid'))

The accuracy and loss seems pretty good but not the validation accuracy -准确度和损失似乎相当不错，但验证准确度却不是 -

Epoch 1/50
10/10 [==============================] - 17s 2s/step - loss: 20.8554 - accuracy: 0.5170 - 
val_loss: 0.8757 - val_accuracy: 0.5946
Epoch 2/50
10/10 [==============================] - 14s 1s/step - loss: 1.5565 - accuracy: 0.5612 - 
val_loss: 0.8725 - val_accuracy: 0.5811
Epoch 3/50
10/10 [==============================] - 14s 1s/step - loss: 0.8374 - accuracy: 0.6293 - 
val_loss: 0.8483 - val_accuracy: 0.5405
Epoch 4/50
10/10 [==============================] - 14s 1s/step - loss: 1.0340 - accuracy: 0.5748 - 
val_loss: 1.6252 - val_accuracy: 0.5135
Epoch 5/50
10/10 [==============================] - 14s 1s/step - loss: 1.1054 - accuracy: 0.5816 - 
val_loss: 0.7324 - val_accuracy: 0.6486
Epoch 6/50
10/10 [==============================] - 15s 1s/step - loss: 0.5942 - accuracy: 0.7041 - 
val_loss: 0.7412 - val_accuracy: 0.6351
Epoch 7/50
10/10 [==============================] - 15s 2s/step - loss: 0.6041 - accuracy: 0.6939 - 
val_loss: 0.6918 - val_accuracy: 0.6622
Epoch 8/50
10/10 [==============================] - 14s 1s/step - loss: 0.4944 - accuracy: 0.7687 - 
val_loss: 0.7083 - val_accuracy: 0.6216
Epoch 9/50
10/10 [==============================] - 14s 1s/step - loss: 0.5231 - accuracy: 0.7007 - 
val_loss: 1.0332 - val_accuracy: 0.5270
Epoch 10/50
10/10 [==============================] - 14s 1s/step - loss: 0.5133 - accuracy: 0.7313 - 
val_loss: 0.6859 - val_accuracy: 0.5811
Epoch 11/50
10/10 [==============================] - 14s 1s/step - loss: 0.6177 - accuracy: 0.6735 - 
val_loss: 1.0781 - val_accuracy: 0.5135
Epoch 12/50
10/10 [==============================] - 14s 1s/step - loss: 0.9852 - accuracy: 0.6701 - 
val_loss: 3.0853 - val_accuracy: 0.4865
Epoch 13/50
10/10 [==============================] - 13s 1s/step - loss: 1.0099 - accuracy: 0.6259 - 
val_loss: 1.8193 - val_accuracy: 0.5000
Epoch 14/50
10/10 [==============================] - 13s 1s/step - loss: 0.7179 - accuracy: 0.7041 - 
val_loss: 1.5659 - val_accuracy: 0.5135
Epoch 15/50
10/10 [==============================] - 14s 1s/step - loss: 0.4575 - accuracy: 0.7857 - 
val_loss: 0.6865 - val_accuracy: 0.5946
Epoch 16/50
10/10 [==============================] - 14s 1s/step - loss: 0.6540 - accuracy: 0.7177 - 
val_loss: 1.7108 - val_accuracy: 0.5405
Epoch 17/50
10/10 [==============================] - 13s 1s/step - loss: 1.3617 - accuracy: 0.6156 - 
val_loss: 1.1215 - val_accuracy: 0.5811
Epoch 18/50
10/10 [==============================] - 14s 1s/step - loss: 0.6983 - accuracy: 0.7245 - 
val_loss: 2.1121 - val_accuracy: 0.5135
Epoch 19/50
10/10 [==============================] - 15s 1s/step - loss: 0.6669 - accuracy: 0.7415 - 
val_loss: 0.8061 - val_accuracy: 0.6216
Epoch 20/50
10/10 [==============================] - 14s 1s/step - loss: 0.3853 - accuracy: 0.8129 - 
val_loss: 0.7368 - val_accuracy: 0.6757
Epoch 21/50
10/10 [==============================] - 13s 1s/step - loss: 0.5672 - accuracy: 0.7347 - 
val_loss: 1.4207 - val_accuracy: 0.5270
Epoch 22/50
10/10 [==============================] - 14s 1s/step - loss: 0.4770 - accuracy: 0.7551 - 
val_loss: 1.6060 - val_accuracy: 0.5135
Epoch 23/50
10/10 [==============================] - 14s 1s/step - loss: 0.7212 - accuracy: 0.7041 - 
val_loss: 1.1835 - val_accuracy: 0.5811
Epoch 24/50
10/10 [==============================] - 14s 1s/step - loss: 0.5231 - accuracy: 0.7483 - 
val_loss: 0.6802 - val_accuracy: 0.7027
Epoch 25/50
10/10 [==============================] - 13s 1s/step - loss: 0.3185 - accuracy: 0.8367 - 
val_loss: 0.6644 - val_accuracy: 0.7027
Epoch 26/50
10/10 [==============================] - 14s 1s/step - loss: 0.2500 - accuracy: 0.8912 - 
val_loss: 0.8569 - val_accuracy: 0.6486
Epoch 27/50
10/10 [==============================] - 14s 1s/step - loss: 0.2279 - accuracy: 0.9082 - 
val_loss: 0.7515 - val_accuracy: 0.7162
Epoch 28/50
10/10 [==============================] - 14s 1s/step - loss: 0.2349 - accuracy: 0.9082 - 
val_loss: 0.9439 - val_accuracy: 0.5811
Epoch 29/50
10/10 [==============================] - 13s 1s/step - loss: 0.2051 - accuracy: 0.9184 - 
val_loss: 0.7895 - val_accuracy: 0.7027
Epoch 30/50
10/10 [==============================] - 14s 1s/step - loss: 0.1236 - accuracy: 0.9592 - 
val_loss: 0.7387 - val_accuracy: 0.7297
Epoch 31/50
10/10 [==============================] - 14s 1s/step - loss: 0.1370 - accuracy: 0.9524 - 
val_loss: 0.7387 - val_accuracy: 0.7297
Epoch 32/50
10/10 [==============================] - 14s 1s/step - loss: 0.0980 - accuracy: 0.9796 - 
val_loss: 0.6901 - val_accuracy: 0.7162
Epoch 33/50
10/10 [==============================] - 14s 1s/step - loss: 0.0989 - accuracy: 0.9762 - 
val_loss: 0.7754 - val_accuracy: 0.7162
Epoch 34/50
10/10 [==============================] - 14s 1s/step - loss: 0.1195 - accuracy: 0.9592 - 
val_loss: 0.6639 - val_accuracy: 0.6622
Epoch 35/50
10/10 [==============================] - 14s 1s/step - loss: 0.0805 - accuracy: 0.9898 - 
val_loss: 0.7666 - val_accuracy: 0.7162
Epoch 36/50
10/10 [==============================] - 14s 1s/step - loss: 0.0649 - accuracy: 0.9966 - 
val_loss: 0.7543 - val_accuracy: 0.7162
Epoch 37/50
10/10 [==============================] - 14s 1s/step - loss: 0.0604 - accuracy: 0.9898 - 
val_loss: 0.7472 - val_accuracy: 0.7297
Epoch 38/50
10/10 [==============================] - 14s 1s/step - loss: 0.0538 - accuracy: 1.0000 - 
val_loss: 0.7287 - val_accuracy: 0.7432
Epoch 39/50
10/10 [==============================] - 13s 1s/step - loss: 0.0430 - accuracy: 0.9966 - 
val_loss: 0.8989 - val_accuracy: 0.6622
Epoch 40/50
10/10 [==============================] - 14s 1s/step - loss: 0.0386 - accuracy: 1.0000 - 
val_loss: 0.6951 - val_accuracy: 0.6892
Epoch 41/50
10/10 [==============================] - 13s 1s/step - loss: 0.0379 - accuracy: 1.0000 - 
val_loss: 0.8485 - val_accuracy: 0.6892
Epoch 42/50
10/10 [==============================] - 14s 1s/step - loss: 0.0276 - accuracy: 1.0000 - 
val_loss: 0.9726 - val_accuracy: 0.6486
Epoch 43/50
10/10 [==============================] - 13s 1s/step - loss: 0.0329 - accuracy: 1.0000 - 
val_loss: 0.7336 - val_accuracy: 0.7568
Epoch 44/50
10/10 [==============================] - 14s 1s/step - loss: 0.0226 - accuracy: 1.0000 - 
val_loss: 0.8846 - val_accuracy: 0.6892
Epoch 45/50
10/10 [==============================] - 13s 1s/step - loss: 0.0249 - accuracy: 1.0000 - 
val_loss: 0.9542 - val_accuracy: 0.6892
Epoch 46/50
10/10 [==============================] - 14s 1s/step - loss: 0.0171 - accuracy: 1.0000 - 
val_loss: 0.8792 - val_accuracy: 0.6892
Epoch 47/50
10/10 [==============================] - 15s 1s/step - loss: 0.0122 - accuracy: 1.0000 - 
val_loss: 0.8564 - val_accuracy: 0.7162
Epoch 48/50
10/10 [==============================] - 13s 1s/step - loss: 0.0114 - accuracy: 1.0000 - 
val_loss: 0.8900 - val_accuracy: 0.7027
Epoch 49/50
10/10 [==============================] - 13s 1s/step - loss: 0.0084 - accuracy: 1.0000 - 
val_loss: 0.8981 - val_accuracy: 0.7027

I tried changing the parameters too yet no result.我也尝试更改参数但没有结果。 Would be helpful if I can get to know what's wrong with the val_accuracy.如果我能了解 val_accuracy 出了什么问题，那将会很有帮助。 Thanks in advance.提前致谢。

1 个解决方案

解决方案1
 0  2021-12-31 17:55:51

You are using less dataset specially test dataset for validation.您正在使用较少的数据集专门测试数据集进行验证。 Try adding some more data to train the model and for validation, then you can see the difference in val_accuracy.尝试添加更多数据来训练 model 并进行验证，然后您可以看到 val_accuracy 的差异。 You can also try by adding more layers to the model.您也可以尝试向 model 添加更多层。

There are some other methods available like, data augmentation, dropout, regularizers to increase the accuracy of the model by avoiding overfitting problem.还有一些其他可用的方法，如data augmentation, dropout, regularizers器，通过避免overfitting问题来提高 model 的准确性。

Please follow this reference to overcome the overfitting problem and to best train your model.请遵循此参考以克服overfitting问题并最好地训练您的 model。

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