鉴于预定义的Keras模型，我尝试首先加载预先训练的权重，然后删除一到三个模型内部（非最后几个）图层，然后将其替换为另一个图层。
我似乎无法在keras.io找到任何关于做这样事情的文档或者从预定义模型中删除图层。
我使用的模型是一个很好的ole VGG-16网络，它在一个函数中实例化，如下所示：
def model(self, output_shape):
# Prepare image for input to model
img_input = Input(shape=self._input_shape)
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
x = Dense(output_shape, activation='softmax', name='predictions')(x)
inputs = img_input
# Create model.
model = Model(inputs, x, name=self._name)
return model
举个例子，在将原始权重加载到所有其他图层之后，我想在块1中取两个Conv图层并用一个Conv图层替换它们。
有什么想法吗？
答案 0 :(得分：7)
假设您有一个模型vgg16_model，可以通过上面的函数或keras.applications.VGG16(weights='imagenet')进行初始化。现在，您需要在中间插入一个新图层，以便保存其他图层的权重。
我们的想法是将整个网络拆分为单独的层，然后再将它组装回来。以下是专门针对您的任务的代码：
vgg_model = applications.VGG16(include_top=True, weights='imagenet')
# Disassemble layers
layers = [l for l in vgg_model.layers]
# Defining new convolutional layer.
# Important: the number of filters should be the same!
# Note: the receiptive field of two 3x3 convolutions is 5x5.
new_conv = Conv2D(filters=64,
kernel_size=(5, 5),
name='new_conv',
padding='same')(layers[0].output)
# Now stack everything back
# Note: If you are going to fine tune the model, do not forget to
# mark other layers as un-trainable
x = new_conv
for i in range(3, len(layers)):
layers[i].trainable = False
x = layers[i](x)
# Final touch
result_model = Model(input=layer[0].input, output=x)
result_model.summary()
以上代码的输出是：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_50 (InputLayer) (None, 224, 224, 3) 0
_________________________________________________________________
new_conv (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1 (Dense) (None, 4096) 102764544
_________________________________________________________________
fc2 (Dense) (None, 4096) 16781312
_________________________________________________________________
predictions (Dense) (None, 1000) 4097000
=================================================================
Total params: 138,320,616
Trainable params: 1,792
Non-trainable params: 138,318,824
_________________________________________________________________
答案 1 :(得分：1)
另一种方法是建立一个顺序模型。 请参阅以下示例，其中我将ReLU层交换为PReLU。 您只需要简单地不添加不需要的图层，而添加一个新图层。
def convert_model_relu(model):
from keras.layers.advanced_activations import PReLU
from keras.activations import linear as linear_activation
from keras.models import Sequential
new_model = Sequential()
# Go through all layers, if it has a ReLU activation, replace it with PrELU
for layer in tuple(model.layers):
layer_type = type(layer).__name__
if hasattr(layer, 'activation') and layer.activation.__name__ == 'relu':
# Set activation to linear, add PReLU
prelu_name = layer.name + "_prelu"
prelu = PReLU(shared_axes=(1, 2), name=prelu_name) \
if layer_type == "Conv2D" else PReLU(name=prelu_name)
layer.activation = linear_activation
new_model.add(layer)
new_model.add(prelu)
else:
new_model.add(layer)
return new_model