Fine tuning model delete previous added layers - machine-learning

Question

I use Keras 2.2.4. I train a model that I want to fine-tune every 30 epochs with new data content (image classification).
Everyday I add more image to classes to feed the model. Every 30 epochs the model is re-trained.
I use 2 conditions, first one if no previous model already trained and second condition when a model is already trained then I want to fine-tune it with new content/classes.
model_base = keras.applications.vgg19.VGG19(include_top=False, input_shape=(*IMG_SIZE, 3), weights='imagenet')
output = GlobalAveragePooling2D()(model_base.output)
# If we resume a pretrained model load it
if os.path.isfile(os.path.join(MODEL_PATH, 'weights.h5')):
print('Using existing weights...')
base_lr = 0.0001
model = load_model(os.path.join(MODEL_PATH, 'weights.h5'))
output = Dense(len(all_character_names), activation='softmax', name='d2')(output)
model = Model(model_base.input, output)
for layer in model_base.layers[:-2]:
layer.trainable = False
else:
base_lr = 0.001
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(2048, activation='relu', name='d1')(output)
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(len(all_character_names), activation='softmax', name='d2')(output)
model = Model(model_base.input, output)
for layer in model_base.layers[:-5]:
layer.trainable = False
opt = optimizers.Adam(lr=base_lr, decay=base_lr / epochs)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
Model summary first time:
...
_________________________________________________________________
block5_conv4 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 512) 2048
_________________________________________________________________
dropout_1 (Dropout) (None, 512) 0
_________________________________________________________________
d1 (Dense) (None, 2048) 1050624
_________________________________________________________________
batch_normalization_2 (Batch (None, 2048) 8192
_________________________________________________________________
dropout_2 (Dropout) (None, 2048) 0
_________________________________________________________________
d2 (Dense) (None, 19) 38931
=================================================================
Total params: 21,124,179
Trainable params: 10,533,907
Non-trainable params: 10,590,272
Model summary second time:
...
_________________________________________________________________
block5_conv4 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
global_average_pooling2d_1 ( (None, 512) 0
_________________________________________________________________
d2 (Dense) (None, 19) 9747
=================================================================
Total params: 20,034,131
Trainable params: 2,369,555
Non-trainable params: 17,664,576
Problem: When a model exist and is loaded for fine-tune it seems to have loose all additionals layers added the first time (Dense 2048, Dropout, etc)
Do I need to add these layers again ? It seems to have no sense as it would loose the training information made at the first pass.
Note: I may need to not set the base_lr as saving a model should save also the learning rate at the state where it stopped before, but I will check this later.

Answer 1

Please note that once you load the model:
model = load_model(os.path.join(MODEL_PATH, 'weights.h5'))
You don't use it. You just overwrite it again
model = Model(model_base.input, output)
Where output is also defined as an operation on the base_model.
It seems to me that you just want to delete the lines after load_model.