给定预定义的Keras模型,我试图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)层,然后用另一个层替换它 .
我似乎无法在keras.io上找到任何关于做这样的事情或从预定义模型中删除图层的文档 .
我使用的模型是一个良好的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层之后,我想在块1中取两个Conv层并用一个Conv层替换它们 .
有任何想法吗?
2 回答
假设您有一个模型
vgg16_model
,由上面的函数或keras.applications.VGG16(weights='imagenet')
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,然后再组装 . 以下是专门针对您的任务的代码:
以上代码的输出是:
另一种方法是构建一个Sequential模型 . 请参阅以下示例,其中我为PReLU交换ReLU层 . 您只需要添加不需要的图层,然后添加新图层即可 .