数据挖掘 - CNN 过拟合的问题 - 吾爱随笔录

我正在使用 CNN 进行图像分类，但在构建不会过度拟合的网络时遇到了麻烦。我的训练集中有 4 个类别的 2000 张图像，而在我的测试集中，我有 3038 个相同的 4 个类别的图像。我的CNN如下：

def Network(input_shape, num_classes, regl2 = 0.0001, lr=0.0001):

model = Sequential()

# C1 Convolutional Layer 
model.add(Conv2D(filters=32, input_shape=input_shape, kernel_size=(3,3),\
                 strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation before passing it to the next layer
model.add(BatchNormalization())

# C2 Convolutional Layer
model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Batch Normalisation
model.add(BatchNormalization())

# C3 Convolutional Layer
model.add(Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Batch Normalisation
model.add(BatchNormalization())

# C4 Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
#Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# C5 Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

 # C6 Convolutional Layer
model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

 # C7 Convolutional Layer
model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Conv2D(filters=512, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# Flatten
model.add(Flatten())

flatten_shape = (input_shape[0]*input_shape[1]*input_shape[2],)

# D1 Dense Layer
model.add(Dense(4096, input_shape=flatten_shape, kernel_regularizer=regularizers.l2(regl2)))
model.add(Activation('relu'))
# Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# D2 Dense Layer
model.add(Dense(4096, kernel_regularizer=regularizers.l2(regl2)))
model.add(Activation('relu'))
# Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# D3 Dense Layer
model.add(Dense(1000,kernel_regularizer=regularizers.l2(regl2)))
model.add(Activation('relu'))
# Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# Output Layer
model.add(Dense(num_classes))
model.add(Activation('softmax'))

# Compile

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

return model

每次我训练和测试时，我都明显过度拟合，因为如果我测试模型，我得到的准确率很低，大约为 45%，如果我绘制它们，测试和训练的准确率曲线相距甚远。

我怎样才能以不会过拟合的方式改进我的网络？

提前致谢。