How to resume training of a model? I have not GPU support so it often happens that my model takes hours to train. Can I train my model in batches , for example if I want to have 100 epochs for my model,but due to power cut my training stops(at 50th epoch) but when I retrain my model I want to train it from where it was left (from 50th epoch).

It would be much appreciated if anyone can explain it by some example.
With tensorflow, currently the most straightforward and easy way to get persistence for your model is to use a tf.train.MonitoredTrainingSession. You just need to use it instead the normal tf.Session() that is frequently used. This an illustrative Python snippet:



with tf.train.MonitoredTrainingSession(checkpoint_dir='/tmp/mymodel',
                                       save_summaries_secs=600) as sess:
   _ = sess.run(train_op, feed_dict={...})


With this, your model is automagically saved every 600 secs in /tmp/mymodel and restored the next time you restart the program.