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'))
以下是结果：
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？
答案 0 :(得分：6)
您最后一个Dense图层上的丢失会随机删除您的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，我假设你得到偶数值。