是否可能/如何将新的卷积层添加到预训练的模型中进行转移学习？

Question

我尝试重复使用预先训练的模型并添加一些新的卷积层。还要替换预训练的分类器。

1。预训练的模型看起来像这样：

input_shape = X_train.shape[1:] #(224, 224, 3)
num_classes = 500
model = Sequential()
model.add(Conv2D(32, kernel_size = (3, 3), input_shape=input_shape, activation='relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(Conv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(Conv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))

model.add(Conv2D(256, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(Conv2D(256, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(Conv2D(256, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

2。尝试执行转移学习如下：

# mark loaded layers as not trainable
for layer in base_model.layers:
    layer.trainable = False

x = base_model.layers[-3].output

# A`enter code here`dd new layers : 
[line 47] x = tf.keras.layers.Conv2D(256, kernel_size = (3, 3), activation = 'relu', padding = 'same',kernel_initializer='glorot_normal')(x)
x = tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)s
x = tf.keras.layers.Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)
x = tf.keras.layers.Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)
x = tf.keras.layers.Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)
x = tf.keras.layers.xPool2D(pool_size=(2, 2), strides=(2, 2))(x)
x = tf.keras.layers.Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)
x = tf.keras.layers.Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)
x = tf.keras.layers.Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same')(x)
x = tf.keras.layers.xPool2D(pool_size=(2, 2), strides=(2, 2))(x)

x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
output = Dense(class_number, activation='softmax')(x)

# define new model
new_model = Model(inputs=base_model.inputs, outputs=output)
new_model.summary()

遇到此错误：

ValueError: Input 0 of layer "conv2d_11" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 256) [at line 47]

是否可能/如何将新的卷积层添加到用于转移学习的预训练模型中？

更新： 灵感来自（ - 训练模型，转移学习，TensorFlow（LOAD_MODEL））

以下代码适合我的目的。

inputshape = (224, 224, 3)
# num_classes = y_train.shape[1]
num_classes = 2000
# Load Model
path = r'D:\00_twm_cnn_model\02_prt\02_outputs' 
os.chdir(path)
ModelName = r'Model_CNN_VGG-16_chi_prt_100_2022-06-15_1.h5' 


base_model = load_model(ModelName)
base_model.summary()
  
model = tf.keras.Sequential()
for layer in base_model.layers[0:-6]: 
    model.add(layer)

model.add(Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same',input_shape=input_shape,name='conv2d_10'))
model.add(Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same',input_shape=input_shape,name='conv2d_11'))
model.add(Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same',input_shape=input_shape,name='conv2d_12'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2),name='max_pooling2d_4'))

model.add(Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same',input_shape=input_shape,name='conv2d_13'))
model.add(Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same',input_shape=input_shape,name='conv2d_14'))
model.add(Conv2D(512, kernel_size = (3, 3), activation = 'relu', padding = 'same',input_shape=input_shape,name='conv2d_15'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2),name='max_pooling2d_5'))

model.add(Flatten())

model.add(Dense(128, activation='relu')) 
model.add(Dropout(0.1))
model.add(Dense(256, activation='relu')) 
model.add(Dropout(0.1))
#model.add(BatchNormalization())
model.add(Dense(num_classes, activation='softmax'))

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