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New-comer to the field here with a few questions. Do I understand correctly that a checkpoint is just a snapshot of the model at a point in time? i.e. "Here a
by jangerhofer 9y ago
New-comer to the field here with a few questions.
Do I understand correctly that a checkpoint is just a snapshot of the model at a point in time? i.e. "Here are the probabilities of each outcome given the characteristics I have observed already."
Also, what does "fully converged" signify? Are there points in the course of training the model at which it is more appropriate to "save" progress than at other times?
- minimaxir 9y ago> Also, what does "fully converged" signify? In machine learning/deep learning, the decrease in training loss has major diminishing returns as training continues. Eventually, training the model hits a point where the loss barely improves each epoch/iteration. (fun visualization from one of my projects: http://minimaxir.com/img/char-embeddings/epoch-losses.png http://minimaxir.com/img/char-embeddings/epoch-losses.png) In some cases, the loss can stop improving entirely, or increase.
- carbocation 9y agoAny recs on choosing TensorFlow vs Keras-on-TensorFlow?
- minimaxir 9y agoKeras does not add much overhead, if any, and yes, it is as easy as everyone claims.
- jacquesm 9y agoStart with Keras, if you run into something you want to do that is not supported by Keras drop into TensorFlow, they are not mutually exclusive and all of TensorFlow is availble.
- carbocation 9y agoFantastic. Thanks to you and minimaxir for the guidance.