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.
Related
I have trained a autoencoder with Early stopping using different epochs but for a certain epoch using early stopping I am getting a minimum loss. How to access that model from that specific address and save it. Below is the picture for reference-
I want to access the model from the specified address and then save it.
Thanks in advance!!!
I am assuming you want to save the model at the time the training early stops.
Refer to:
https://keras.io/api/models/model_saving_apis/
https://keras.io/api/callbacks/model_checkpoint/
A simple model.save() should work (you can do so after the model.fit()) or if you want to have checkpoints every time the loss reaches a minimum then use the modelCheckpoint callback.
I'm training a TensorFlow Keras Model using Model.fit(). I'm also using callbacks to log my training accuracy metrics after every batch using TensorFlow's on_train_batch_end() syntax. In addition, I'm using another callback to run Model.evaluate() every 1,000 batches to compute validation set accuracy and update the logs dict passed around the callbacks during Model.fit().
Looking at the logged metrics vs. batch number shows very perplexing results. After the Model.evaluate() run, the training accuracy experiences a significant 'jolt', initially triggering a rapid increase in the logged training accuracy and subsequently triggering a significant drop training accuracy followed by a slower recovery (see attached images).
My guess is that it's something to do with the Model.evaluate()'s call to reset_metrics(), which loops through and calls the reset_states() method on each metric. I can't work out what reset_states() is doing and if this is relevant to the behaviour I'm observing. It seems to relate to the Mean parent class of CategoricalAccuracy. I haven't been able to find anything helpful in the TensorFlow docs yet.
Are the metrics shown during Model.fit() actually some form of moving averages rather than the batch-wise metric? In that case, the reset_states() method would be resetting the moving average, possibly producing the jolting behaviour.
Can anyone with a better grasp of TensorFlow's inner workings help?
Here is a picture of the gradient of a conv2d layer (the kernel). It has a zigsag pattern which I would like to understand. What I understand is that the gradient changes from mini-batch to mini-batch. But why does it increase after each epoch?
I am using the Keras Adam optimizer with default settings. I dont think that is the reason. Dropout and Batch-Norm. should also not be the reason. I am using image augmentation but that does not change its behavior from batch to batch.
Does anybody have an idear?
I've seen this before with keras metrics.
In that case the problem was that the metrics maintain a running average across each epoch, and it's that "average so far" that they report to TensorBoard.
How are these grads getting to TensorBoard? Are you passing them to a tf.keras.metrics.Mean? If so you probably want to call "reset_states" on it. Maybe in an custom callback's on_batch_end.
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/
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