Given VGG16 model named org_model, construct a similar CNN named base_model using weights from org_model

create_base_model.py

# hyp: org_model is VGG16
def create_base_model( org_model, input_shape ):

    base_model = Sequential()
    
    l = org_model.get_layer('block1_conv1')
    base_model.add( Conv2D( l.filters, l.kernel_size, strides=l.strides, padding=l.padding,
                            use_bias=l.use_bias, data_format=l.data_format, activation=l.activation, 
                            name='my_block1_conv1',
                            input_shape=input_shape ) )
    base_model.layers[-1].set_weights( l.get_weights() )
    
    l = org_model.get_layer('block1_conv2')
    base_model.add( Conv2D( l.filters, l.kernel_size, strides=l.strides, padding=l.padding,
                            use_bias=l.use_bias, data_format=l.data_format, activation=l.activation, 
                            name='my_block1_conv2' ) )
    base_model.layers[-1].set_weights( l.get_weights() )

    l = org_model.get_layer('block1_pool')
    base_model.add( MaxPooling2D(pool_size=l.pool_size, padding=l.padding, strides=l.strides,
                                 name='my_block1_pool' ) )

    l = org_model.get_layer('block2_conv1')

    ...
    
    for l in base_model.layers:
        l.trainable = False

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

    return base_model