如何使用功能 keras API 在预训练的非序列 model 中的激活层之后插入丢失层？

Question

I am working on a modified resnet, and want to insert dropout after activation layers. I have tried the following but due to the model not being sequential, it did not work:

def add_dropouts(model, probability = 0.5):
    print("Adding Dropouts")
    
    updated_model = tf.keras.models.Sequential()
    for layer in model.layers:
        print("layer = ", layer)
        updated_model.add(layer)
        if isinstance(layer, tf.keras.layers.Activation):
            updated_model.add(tf.keras.layers.Dropout(probability))

    print("updated model Summary = ", updated_model.summary)
    print("model Summary = ", model.summary)

    model = updated_model

    return model


base_model = tf.keras.applications.ResNet50V2(include_top=False, input_shape=input_img_shape, pooling='avg')

base_model = add_dropouts(base_model, probability = 0.5)

Then i tried my own version using the functional API, but this method doesn't work and returns a value error say Tensor doesn't have output.

    prev_layer = base_model.layers[0]
    for layer in base_model.layers:
        next_layer = layer(prev_layer.output)
        if isinstance(layer, tf.keras.layers.Activation):
            next_layer = Dropout(0.5)(next_layer.output)
        prev_layer = next_layer

Does anyone know how someone would add dropout layers into resnet or any other pretrained network?

Answer 1

So eventually i figured out how to do it; but its very hacky. Go to:

C:\ProgramData\Anaconda3\envs*your env name*\Lib\site-packages\tensorflow\python\keras\applications

Go to resnet.py. This will also change resnetv2 instances because it is based on the original resnet. Just Cntrl+F for activation,and where you see an activation layer(which is usually in the format x = Layer(x) building the model a layer at a time) then just add: x = Dropout(prob)(x) Here is an example:

  if not preact:
    x = layers.BatchNormalization(
        axis=bn_axis, epsilon=1.001e-5, name='conv1_bn')(x)
    x = layers.Activation('relu', name='conv1_relu')(x)#insert layer after each of these
    x = layers.Dropout(prob)(x) # added dropout

Do this for all similar search results for 'activation'.

Then you will see the dropout added in your model summary.