通过加深网络把CIFAR10的训练精度提升到80%

这次继续，在原来网络的基础上，加深了卷积层的数量，从原来的3层卷积，加深到了6层。核心代码如下：

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3),padding='same'))
model.add(layers.Conv2D(32, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))

model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation='softmax'))

可以看到验证精度比之前的略好一些。但是程度非常有限，刚刚有接近80%的影子。下面我尝试增加Dropout层：

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3),padding='same'))
model.add(layers.Conv2D(32, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.2))

model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.3))

model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.Flatten())
model.add(layers.Dropout(0.4))


model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation='softmax'))

在原来的基础上增加了两层Dropout，结果点进一步得到改善：

已经达到80%的水平了。

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到这之后，我又发现一个自己代码的问题，之前修改代码的时候，去掉了最后卷积层之后的池化层，肯定是不妥的。我现在给加回来：

from keras import layers
from keras import models


model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3),padding='same'))
model.add(layers.Conv2D(32, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.2))

model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.3))

model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.4))

model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(10, activation='softmax'))

重新训练结果如下：

可以看到验证精度最后都能超过80%。只能说怪我了，卷积层后面不跟池化层的话数据就太大了，用池化层压缩一下，训练效果往往会更好。

下面我又尝试把Flatten之后的全连接层给去掉，得到了更好的数据：

训练在12轮之后，验证精度就稳步在80%以上了。
最终代码如下：

from keras import layers
from keras import models


model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3),padding='same'))
model.add(layers.Conv2D(32, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.2))

model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(64, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.3))

model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.Conv2D(128, (3, 3), activation='relu',padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.4))

model.add(layers.Flatten())

model.add(layers.Dense(10, activation='softmax'))

model.summary()

model.compile(optimizer='Adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])


history = model.fit(train_images, train_labels, epochs=50, batch_size=64,validation_data=(test_images, test_labels))