I start to training until ckpt-7 then I stopped training. Then again I started training but befor I changed pipline config in fine tune chekpoint on my model. I wrote latest check point and I changed its directory . My loss function approximetly 0.899 before stopped to the training.
When I continue to train but its start to steps 100 and my loss fuction 15.009.
How can I contiune the model before stopped? What should I do?
I am using centernet model with Colab.
Please explain I am new on that topic.
I could understand your question that you could not resume the training where it stopped.
Actually with the updates in TF2, we need not change the finetune checkpoint parameter in the pipeline.config. Re-run the same training script pointing to the same model_dir where your checkpoints are stored.
TF2 will automatically understand and resume from where the training stopped with the help of checkpoints created in the model_dir.
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I've trained SSD mobilent v2 320x320 model with around 4k steps which produced quite a few checkpoints that are saved in my training folder.The issue I am experiencing now is that this only evaluates the latest checkpoint, but I'd like to evaluate all of them at once.
Ideally I would like to see the results in TensorBoard which shows the validation accuracy (mAP) of the different checkpoints as graph - which it does already, but just for the one checkpoint.
I have tried to run my evaluation code to generate a graph for my mAP but it shows my mAP with a simple dot.
Each checkpoint refers to a previous state of your model during training. The graph you see on TensorBoard for mAP, at some points, is the same as the dots that are produced when you run the evaluation once on checkpoint because the checkpoints are not actually different models but your model at different times during training. So the graph of the last model is what you need.
I'm training a CNN on google colab pro, and unfortunately thought about adding the ModelCheckpoint callback too late. Despite being on google pro, the model very simple model has been training for 10 hours now.
If I interrupt the model.fit cell (I stop it running), and add the ModelCheckpoint callback to the callbacks in the model.fit function, will the model re-train from scratch?
Brief answer: No.
A longer answer: You can actually try the following: take your model and look at the initial loss for example
As you can see, at the end of the first epoch the training loss is 0.2499. Now I modify the parameters in the fit method adding a callback.
And at the beginning of the first epoch, we have the training starting with lower loss.
In order to restart the training you have to recompile the model.
I am new to deep learning, I have a yolov3 model that I have been training on my custom data. Every time I train, the training seems to start from scratch. How do I make the model continue its training from where it stopped last time?
The setup I have is the same as this repo.
You can use model.load_weights(path_to_checkpoint) just after the model is defined at line 41 in train.py and continue training where you left off
I'm using Attention Mechanism for Image Captioning, and i saved weights of all layers manually , but when i restart my pc and load the saved weight then model's loss is increased too much, it seems that weights are not properly saved.
but i didn't find out any un-saved weight. Any one can help ?
Colab Link
saving the weights is fine as long as you are running your program. Saving the weights does not preserve other needed information that must be restored once you exit execution for example the state of the optimizer. So in your program have a statement that saves the entire model using model.save before you end execution. Then when you restart your program reload the entire model using model.load. Documentation is here.
I am using Tensorflow Object Detection API. I started training a model for a couple of hours and I want to add more images to the dataset and start training again from epoch 1. What would be the best way to do this? I know that if a run now python train.py is it going to continue from the last checkpoint, something that I don't want because I want to retrain over again and with more data. I am thinking to delete the files in the training folder which is where checkpoints and other files related are. I just not sure about what files to delete from that folder or if that is the proper way.
UPDATE: Indeed, I just need to delete all the files (checkpoints) from the training directory and run again python train.py. That would start the training from the step 1