I am working on a project where Keras ModelCheckpoint is used. This callback class seems to cover my needs except for one small detail.
I have not found a way to pass a counter to the epoch numbering so as to deal with resume model training cases. I often train for some epochs and then resume training afterwards. I would like to have a consistent model saving pattern like:
model.{epoch:03d}-{loss:.2f}.hdf5
but with numbering beginning from the epoch the previous training stopped and not from the beginning.
The current command I use is this:
ckp_saver = ModelCheckpoint(checkpoint_dir + "/model.{epoch:03d}-{loss:.2f}.hdf5", monitor='loss', verbose=0,
save_best_only=False, save_weights_only=True, mode='auto', period=1)
Is there any way to pass this information to ModelCheckpoint? The solution I found is to edit the Keras code and add a default argument containing the actual pretrained epochs (defaulting in 0 if not passed) so as not to break any other code but I would prefer to avoid this if it's not necessary. Any other ideas?
The original code was taken from this file here.
Related
I am training a ResNet50 model. I wrote a callback to save the best models according to their val_loss.
The code is like:
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_loss',
verbose=1,
best = float(resume_loss),
save_best_only=True,
mode='min')
To set up the previous val_loss value I use best. In my example, I set it to 13.0880. However, during the fitting, it still takes it as inf.
Here is the image showing that the best attribute is not changing according to user input, but its default value is being used.
This is not a bug, it is a feature.
During training, the loss from the last epoch is compared with the current loss and then it is saved conditionally. [as per your defined checkpoint]
But when you start training over again, the loss from the last epoch is considered as 'inf', and the current loss as it is and then it is saved.
Some Keras callbacks, like ModelCheckpoint or ReduceLROnPlateau, rely on counting the number of epochs that some condition is met until some action is taken.
For certain purposes I need to train a Keras model in several fitting sessions, so something like
for epoch in range(num_epochs):
model.fit(data, epochs=1)
rather than
model.fit(data, epochs=num_epochs)
I was wondering if Keras callbacks work even if I use them across several fitting sessions.
Each time model.fit(...) is called callbacks.History is reset. So no, it will not work like that. While you could log yourself as #kacpo1 mentioned and save each, you may benefit from the train_on_batch(...) method. This performs a single update and you can set reset_metrics=False in the method call to retain your metrics.
https://keras.io/api/models/model_training_apis/#trainonbatch-method
I was wondering if Keras callbacks work even if I use them across several fitting sessions.
The answer is no. If you doing things this way (loop with fitting one epoch at time) you can do those things like saving weights and learning rate decay by yourself without using callbacks.
for epoch in range(num_epochs):
model.fit(data, epochs=1)
if epoch % 5 == 0:
model.save_weights(...)
I use keras to train a deep model. Because of my situation I must stop the training fuzee and start it again. I use callbacks to save the last and best checkpoint in my model. this is how I define them:
# best model check point
checkpoint_path_best = f"best.hdf5"
modelcheckpoint_best = ModelCheckpoint(checkpoint_path_best,
monitor='val_loss',
save_best_only=True,
mode='min')
# last model check point
checkpoint_path_last = f"last.hdf5"
modelcheckpoint_last = ModelCheckpoint(checkpoint_path_last,
save_best_only=False)
In the next run I load last checkpoint and resume the train. The problem is that it when saving the best.
I don't know how must pass the saved best to it that assume it in job.
Also in new run it start epoch from zero, that affect in learning rete. How can control this?
Because I have storage problem I cant save all the best.
I am training a GPT2 text generation model in TensorFlow and am performing a single epoch across my text corpus. My question is, how can I save my model every, say, 10 steps or so? My model abruptly stopped training on the 100th step with only another 20 to go....oooof.
I'm aware of the Model_Checkpoint() callback, but it doesn't appear as though I can replace steps for epoch in the save_freq parameter.
tf.keras.callbacks.ModelCheckpoint(
filepath, monitor='val_loss', verbose=0, save_best_only=False,
save_weights_only=False, mode='auto', save_freq='epoch', **kwargs)
https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint
Set save_freq = 1. This should save every step. I would not recommend this because it will spend much time on the i/o of the save and slow your training down.
If not, why not? Sometimes I will have an epoch that gets 95ish % and then finish with an epoch that has 10% or so less accuracy. I just never can tell whether it reverts back to that best epoch.
if you are using Keras,then in ModelCheckpoint callback, set save_best_only=True. If this option is enabled, then it saves the model which shows the best results based on the metric you set, either loss or accuracy which you mention for monitor attribute.
Read more about it here - https://keras.io/callbacks/#modelcheckpoint
keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1)