问题
I am using pre-trained VGG-16 model for image classification. I am adding custom last layer as the number of my classification classes are 10. I am training the model for 200 epochs.
My question is: is there any way if I randomly stop (by closing python window) the training at some epoch, let's say epoch no. 50 and resume from there? I have read about saving and reloading model but my understanding is that works for our custom models only instead of pre-trained models like VGG-16.
回答1:
You can use ModelCheckpoint callback to save your model regularly. To use it, pass a callbacks
parameter to the fit
method:
from keras.callbacks import ModelCheckpoint
checkpointer = ModelCheckpoint(filepath='model-{epoch:02d}.hdf5', ...)
model.fit(..., callbacks=[checkpointer])
Then, later you can load the last saved model. For more customization of this callback take a look at the documentation.
回答2:
Here is a customised version of ModelCheckpoint that I use to resume training from a given epoch, gist. It will save the epoch and other logs to a corresponding JSON file, it will also check whether to resume the training or not when starting. You need to call get_last_epoch
and set initial_epoch
in model.fit
in order to resume from that epoch.
import json
class StatefulCheckpoint(ModelCheckpoint):
"""Save extra checkpoint data to resume training."""
def __init__(self, weight_file, state_file=None, **kwargs):
"""Save the state (epoch etc.) along side weights."""
super().__init__(weight_file, **kwargs)
self.state_f = state_file
self.state = dict()
if self.state_f:
# Load the last state if any
try:
with open(self.state_f, 'r') as f:
self.state = json.load(f)
self.best = self.state['best']
except Exception as e: # pylint: disable=broad-except
print("Skipping last state:", e)
def on_train_begin(self, logs=None):
prefix = "Resuming" if self.state else "Starting"
print("{} training...".format(prefix))
def on_epoch_end(self, epoch, logs=None):
"""Saves training state as well as weights."""
super().on_epoch_end(epoch, logs)
if self.state_f:
state = {'epoch': epoch+1, 'best': self.best}
state.update(logs)
state.update(self.params)
with open(self.state_f, 'w') as f:
json.dump(state, f)
def get_last_epoch(self, initial_epoch=0):
"""Return last saved epoch if any, or return default argument."""
return self.state.get('epoch', initial_epoch)
来源:https://stackoverflow.com/questions/52008748/stop-and-restart-training-on-vgg-16