微调 keras 中的 resnet 解冻层

Question

i am working with resnet to train my data. I have frozen most of the layers and only working training with the last 4 layers. I want to change these last four layer dimension so that it matches my input dimension and channels. As i am new to this i dont know how to do it. I tried googling it but cannot find the solution

base_model = tf.keras.applications.ResNet50(
    include_top=False,
    weights="imagenet",
    input_tensor=3,
    input_shape=(150,150),
    pooling=None,  
)
for layer in base_model.layers[:46]:
    layer.trainable = False

Answer 1

If you want to change last layers architecture, you should get output of the desired intermediate layer and connect it to yours.

I assume that you want to change the architecture after the 46th layer.

First define pre-trained model:

base_model = tf.keras.applications.ResNet50(
    include_top=False,
    weights="imagenet",
    input_shape=(150,150,3), 
)
for layer in base_model.layers[:46]:
    layer.trainable = False

Then, get the name of intermediate layer you want (in this case 46th layer):

print(base_model.layers[46].name)

For me, The output is conv3_block1_3_conv

Then get the output of this layer and connect to your own layers:

last_layer = base_model.get_layer('conv3_block1_3_conv') #get the layer
last_output = last_layer.output                          #get the layer output
x = tf.keras.layers.Flatten()(last_output)               #flatten the output
x = tf.keras.layers.Dense(1024, activation='relu')(x)    #add your own layers
x = tf.keras.layers.Dense(1, activation='sigmoid')(x)    #add your own output
model = tf.keras.Model(base_model.input, x)              #create the new model