I am training my Deep neural network on Keras (TF backend). I just want to print the first loss while training DNN. I just want to make sure that my initialization is correct and so I need the initial loss calculated by the DNN after making the first forward pass.
Keras callback allows us to determine loss after every epoch. I want it after first training step.
You can create a custom callback using on_batch_end
https://keras.io/callbacks/
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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.
How can I understand which layers are frozen fine-tuning a detection model from Tensorflow Model Zoo 2?
I have already set with success the Path for fine_tune_checkpoint and fine_tune_checkpoint_type: detection and in the file proto I have already read that "detection" means
// 2. "detection": Restores the entire feature extractor.
The only parts of the full detection model that are not restored are the box and class prediction heads.
This option is typically used when you want to use a pre-trained detection model
and train on a new dataset or task which requires different box and class prediction heads.
I didn't really understand what does that means. Restored means Frozen in this context?
As I understand it, currently the Tensorflow 2 Object detection does not freeze any layers when training from a fine tune checkpoint. There is a issue reported here to support specifying which layers to freeze in the pipeline config. If you look at the training step function, you can see that all trainable variables are used when applying gradients during training.
Restored here means that the model weights are copied from the checkpoint to be used as a starting point for training. Frozen would mean that the weights are not changed (i.e. no gradient is applied) during training.
I'm struggling with understanding how keras model works.
When we train model, we give metrics(like ['accuracy']) and loss function(like cross-entropy) as arguments.
What I want to know is which is the goal for model to optimize.
After fitting, leant model maximize accuracy? or minimize loss?
The model optimizes the loss; Metrics are only there for your information and reporting results.
https://en.wikipedia.org/wiki/Loss_function
Note that metrics are optional, but you must provide a loss function to do training.
You can also evaluate a model on metrics that were not added during training.
Keras models work by minimizing the loss by adjusting trainable model parameters via back propagation. The metrics such as training accuracy, validation accuracy etc are provided as information but can also be used to improve your model's performance through the use of Keras callbacks. Documentation for that is located here. For example the callback ReduceLROnPlateau (documentation is here) can be used to monitor a metric like validation loss and adjust the model's learning rate if the loss fails to decrease after a certain number (patience parameter) of consecutive epochs.
I am looking to train a large face identification network. Resnet or VGG-16/19. TensorFlow 1.14
My question is - if I run out of GPU memory - is it valid strategy to train sets of layers one by one?
For example train 2 cnn and maxpooling layer as one set, then "freeze the weights" somehow and train next set etc..
I know I can train on multi-gpu in tensorflow but what if I want to stick to just one GPU..
The usual approach is to use transfer learning: use a pretrained model and fine-tune it for the task.
For fine-tuning in computer vision, a known approach is re-training only the last couple of layers. See for example:
https://www.learnopencv.com/keras-tutorial-fine-tuning-using-pre-trained-models/
I may be wrong but, even if you freeze your weights, they still need to be loaded into the memory (you need to do whole forward pass in order to compute the loss).
Comments on this are appreciated.
I am loading the inceptionV3 keras net with a tensorflow backend. After loading saved weights and setting the trainable flag of all the layers to false I try to fit the model and expect to see everything stable. But the validation loss increase (and accuracy decrease) with each epoch, while the training loss and accuracy are indeed stable as expected.
Can someone explain this strange behavior ? I susupext it is related to the batch normalization layers.
I had the same problem and looks like I found the solution. Check it out here