向 RESNET50 添加层以构建 JOIN CNN 模型

Question

这是我的代码，用于将 resnet50 模型与此模型（我想在我的数据集上训练）结合起来。我想在代码中冻结 resnet50 模型的层（参见 Trainable=false）。我在这里导入 resnet 50 模型

`` 
import tensorflow.keras
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
resnet50_imagnet_model = tensorflow.keras.applications.resnet.ResNet50(weights = "imagenet", 
                           include_top=False, 
                           input_shape = (150, 150, 3),
                           pooling='max')
  ``

在这里我创建我的模型

 ```
# freeze feature layers and rebuild model
for l in resnet50_imagnet_model.layers:
    l.trainable = False

#construction du model
model5 = [
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(12, activation='softmax')
]

#Jointure des deux modeles
model_using_pre_trained_resnet50 = tf.keras.Sequential(resnet50_imagnet_model.layers + model5 )
 ```

最后一行不工作，我有这个错误： 层 conv2_block1_3_conv 的输入 0 与层不兼容：预期输入形状的轴 -1 的值为 64 但收到的输入形状为 [None、38、38、256

感谢您的帮助。

Answer 1

您也可以使用 keras 的 functional API，如下所示

    from tensorflow.keras.applications.resnet50 import ResNet50
    import tensorflow as tf

    resnet50_imagenet_model = ResNet50(include_top=False, weights='imagenet', input_shape=(150, 150, 3))

    #Flatten output layer of Resnet
    flattened = tf.keras.layers.Flatten()(resnet50_imagenet_model.output)

    #Fully connected layer 1
    fc1 = tf.keras.layers.Dense(128, activation='relu', name="AddedDense1")(flattened)

    #Fully connected layer, output layer
    fc2 = tf.keras.layers.Dense(12, activation='softmax', name="AddedDense2")(fc1)

    model = tf.keras.models.Model(inputs=resnet50_imagenet_model.input, outputs=fc2)

另请参阅。

向 RESNET50 添加层以构建 JOIN CNN 模型

Adding layers to RESNET50 in order to build a JOIN CNN Model

python

tensorflow

keras

deep-learning

resnet