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
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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 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.
Its my first time deploying a model. I've created a cnn model using tensorflow, keras, Xception and saved model is about 80 mb. When I load it from a function and do a prediction, it takes about 4-5 seconds. Is there a way to reduce this time? Does the model has to be loaded for every prediction?
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The model load only once in your program. for each prediction, you use the loaded model. it might take time to predict. TensorFlow doesn't load the model on prediction. the better way is to only save weights after training and for inference create model architecture and then load the saved weights.
I am working on training some deep learning mode. It takes several hours to train such model in Google Colaboratory. I need to remain online full hours to train the model successively. Is there any solution to make Google colab train the model and if any internet interruption occurs, make no hamper to the training. Otherwise I need to train from the start.
I am very new to Neural network and tensorflow, just starting on the retrain image tutorial. I have successfully completed the flower_photos training and i have 2 questions.
1.) Is it a good/bad idea to keep building upon a retrained model many times over and over? Or would it be a lot better to train a model fresh everytime? That leads to my second question
2.) If it is ok to retrain a model over and over, for the retrain model tutorial in Tensorflow (Image_retraining), in the retrain.py would i simply replace the classify_image_graph_def.pb and imagenet_synset_to_human_label_map.txt with the one outputted from my retraining? But i see that there is also a imagenet_2012_challenge_label_map_proto.pbtxt, would i have to replace that one with something else?
Thanks for your time