Val_accuracy不增加

Question

我是这个技术的新手，所以我试着在图像数据集上建立一个模型。我用过这种建筑-

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'))

准确性和损失似乎相当不错，但验证的准确性却不高-

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

我也尝试过改变参数，但没有结果。如果我能知道val_accuracy有什么问题，那将是有帮助的。提前谢谢。

Answer 1

您正在使用较少的数据集，特别是用于验证的测试数据集。尝试添加更多的数据来训练模型和验证，然后您可以看到val_accuracy中的不同之处。您也可以通过向模型添加更多的层来尝试。

还有其他一些方法，例如，data augmentation, dropout, regularizers，通过避免overfitting问题来提高模型的精度。

请遵循这参考，以克服overfitting问题，并最好地培训您的模型。