MNIST的Keras VGG模型:训练和验证准确性之间的差异

Question

我用Keras创建了以下模型.数据集是MNIST.

'''
    conv - relu - conv- relu - pool -
    conv - relu - conv- relu - pool -
    conv - relu - conv- relu - pool -
    affine - relu - dropout - affine - dropout - softmax
'''

model = Sequential()
model.add(Conv2D(16, kernel_size=(3, 3),
                 padding='same',
                 input_shape=input_shape)) 
model.add(Activation('relu'))
model.add(Conv2D(16, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2))) 
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(50, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Dropout(0.5))
model.add(Activation('softmax'))

结果如下:

60000/60000 [==============================] - 10s - loss: 1.2707 - acc: 0.5059 - val_loss: 0.0881 - val_acc: 0.9785                    
Epoch 2/20                                                                                                                              
60000/60000 [==============================] - 9s - loss: 0.9694 - acc: 0.5787 - val_loss: 0.0449 - val_acc: 0.9873                                    

...        

Epoch 19/20                                                         
60000/60000 [==============================] - 9s - loss: 0.8530 - acc: 0.6004 - val_loss: 0.0282 - val_acc: 0.9937                     
Epoch 20/20                       
60000/60000 [==============================] - 9s - loss: 0.8564 - acc: 0.5982 - val_loss: 0.0383 - val_acc: 0.9910                     
Test loss: 0.0382921607383        
Test accuracy: 0.991                    

为什么培训准确性如此之低,而验证的准确性如此之高？

Answer 1

最后一个Dense图层上的丢失会随机删除你的10个神经元中的一半.你的最后一层只能准确地减半,因为一般来说有一半的神经元缺失了.

尝试删除它,我假设你得到均值.