Adding Dropout layer after every activation layer in a pre-trained resnet model in tensorflow-2

Question

I am using a pre-trained Resnet model for a classification problem and the model is over-fitting on the data. I want to try adding dropouts after each activation function layer of the pre-trained Resnet Architecture.

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(weights=weights, include_top=False, input_shape=input_img_shape, pooling='avg')

base_model = add_dropouts(base_model, probability = 0.5)

But I am getting the following error:

ValueError: Input 0 of layer conv2_block1_3_conv is incompatible with the layer: expected axis -1 of input shape to have value 64 but received input with shape [None, 128, 128, 256]

This is preventing me from copying the layers individually. How can I solve this? If anyone has a better idea to add Dropouts after every activation layer, please share.