Concatenate Custom Pretrained Model with new Model Keras

I converted Sports_1M caffe model to Keras and using it as an pretrained model into my new Keras Model.I also loaded the pretrained weights.

I removed the top layer of Pretrained model and finally concatenated with the New Model. I don't want to train the loaded pretrained model again (just wanted to use the embedding of pretrained model and use it to train my new Keras model).

The code looks like this:

from keras.models import model_from_json
from keras import backend as K
K.set_image_dim_ordering('th')

model = model_from_json(open('/content/sports_1M/sports1M_model_new.json', 'r').read())
model.load_weights('/content/sports_1M/sports1M_weights.h5')

My questions are:

1. Should I compile the pretrained model then concatenate it?

    model.compile(loss='mean_squared_error', optimizer='adam')
   

2. How do I know that the pretrained model is not training it again (which I don't want)?

3. How do I train the whole (concatenated) architecture?

    model2 = Model(model.get_input_at(0),model.get_layer(layer_name).output)
   
    input_shape = (3, 16, 112, 112)
   
    encoded_l = model2(left_input)
   
    prediction = Dense(1,activation='sigmoid')(encoded_l)
   
    Model([left_input,right_input] , prediction)
   

4. When we use Inbuild pretrained models like VGG , we generally use VGG(include_top = False , weights = 'imagenet')

I am thinking like this for my case