将预处理层添加到模型

Question

我使用的是预训练的 ResNet 模型，我正在使用我的数据集训练模型的几个层，但我想将 ResNet 的预处理作为模型的一个层。

如何操作？

Answer 1

在Input层之后添加预处理层与在ResNet50模型之前添加预处理层相同，

resnet = tf.keras.applications.ResNet50( 
    include_top=False ,
    weights='imagenet' ,
    input_shape=( 256 , 256  , 3) ,
    pooling='avg' ,
    classes=13
)
for layer in resnet.layers:
    layer.trainable = False

# Some preprocessing layer here ...
preprocessing_layer = tf.keras.layers.Normalization( mean=0 , variance=1 , input_shape=( 256 , 256 , 3 ) )

model = tf.keras.models.Sequential( [
     preprocessing_layer ,
     resnet,
     tf.keras.layers.Flatten(),
     tf.keras.layers.Dense(22, activation='softmax',name='output') 
])
model.compile( loss='mse' , optimizer='adam' )
model.summary()

输出，

_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 normalization (Normalizatio  (None, 256, 256, 3)      0         
 n)                                                              
                                                                 
 resnet50 (Functional)       (None, 2048)              23587712  
                                                                 
 flatten (Flatten)           (None, 2048)              0         
                                                                 
 output (Dense)              (None, 22)                45078     
                                                                 
=================================================================
Total params: 23,632,790
Trainable params: 45,078
Non-trainable params: 23,587,712
_________________________________________________________________

输入现在将首先通过 preprocessing_layer，然后进入 resnet 模型。

Answer 2

如果你想使用tf.keras.applications.resnet50.preprocess_input()，试试：

import tensorflow as tf

resnet = tf.keras.applications.ResNet50( 
    include_top=False ,
    weights='imagenet' ,
    input_shape=( 256 , 256  , 3) ,
    pooling='avg' ,
    classes=13
)
for layer in resnet.layers:
    layer.trainable = False

model = tf.keras.Sequential()
model.add(tf.keras.layers.Lambda(tf.keras.applications.resnet50.preprocess_input, input_shape=(256, 256, 3)))
model.add(resnet)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(22, activation='softmax',name='output') )

model.compile( loss='mse' , optimizer='adam' )
print(model(tf.random.normal((1, 256, 256, 3))))

tf.Tensor(
[[0.12659772 0.02955576 0.13070999 0.0258545  0.0186768  0.01459627
  0.07854564 0.010685   0.01598095 0.04758708 0.05001146 0.20679766
  0.00975605 0.01047837 0.00401289 0.01095579 0.06127766 0.0313729
  0.00884041 0.04098257 0.01187507 0.05484949]], shape=(1, 22), dtype=float32)

将预处理层添加到模型

Add preprocess layer to a model

deep-learning