Saving and Loading Models (Coding TensorFlow)

Key Takeaways

[Music] [Applause] hi there everybody what's up my name is Magnus and you're watching cohdon intensify the show where you learn how to code intensive flow alright in this episode we'll talk about saving and loading models so why do we want to talk about this well first of all whenever you train a model of any significant complexity the training can take a long time most of the models in this getting started series will just take a minute or so to train but real-life models can take days or even weeks to train so if you were to hit ctrl C on your training job after it's been running for a day or so all your model weights values will be lost and you would have to restart training from the beginning and be a very sad camper but if you save your model every so often you can always resume training from that point making you a happy camper another benefit is that you can take your model and transfer it to another computer where you can continue training but I'm pretty sure you already guessed that I was going to bring that up that's enough talking for now check out the links below to locate the code because that's what we're gonna do now check out the cold Oh finally we get to check out the code that's awesome let's go and check out the code all right let's start by checking out the awesome licenses here at the top then install packages for hdf5 and general support and here we do some imports and print the tensorflow version it's totally okay if you have a later version than me here we used to M this dataset to demonstrate model loading and saving then reshape the images to batches of 28 by 28 arrays which is the pixel size of M NIST images and normalize all pixel values to be between 0 & 1 next is the model definition which is defined in the create underscore model function this is a very basic model which is totally ok because in this screencast we're interested learning how to load and save models not creating the best model for the emne Stata set and here we finally get to see how a model can be saved checkpoint underscore path will be the path of the save model a model checkpoint callback object is created with this path we also specify that only the weights of the model should be saved and that we want debug output when saving is performed finally we perform the model training by calling the fit method and providing this callback as you can see this will cost the model to be saved once every epoch has been completed and if we look at the check points directory we can now see three files the CP dot ck apt dot data file contains all the weight values this file has a range sequence because multiple partitions could potentially be used if we have a lot of weights the CP dot c k PT dot index file specifies which partition file contains which weights and finally the checkpoint file is a text file that points to the latest model in our case we only have one data file but shortly we'll see an example where we have saved multiple versions of the model all right so now when we have our saved model let's try out loading it first let's just create a model from scratch and try it out since it hasn't been trained you can see that the accuracy really sucks and now for the magic if we call the method load underscore weights with our checkpoint path our model gets initialized with the previous training state and has much better accuracy ok so that's the basics to save and load models let's look at some more options we have one option is to provide the period parameter when creating the model checkpoint object in this case we used a value 5 which as you can see saves a new model every 5 epochs observe in this case we also use a parameterised file name based on the epoch this means a unique file is saved every time that's also why we can see multiple files from looking at the checkpoint directory we can also use a function called TF train dot latest underscore checkpoint that will return the latest model which was saved in our case the one with index 50 this function actually looks into the file with the name checkpoint to find the latest checkpoint remember that the checkpoint file is a text file so you can actually check the file content yourself and now we can load the model using the load underscore weights function like we did before providing the value returned by TF the train that latest underscore checkpoint another way of saving models is to call the save method on the model this will create an hdf5 formatted file remember that we specified safe underscore weights underscore only to true last time we saved a model in addition to only saving variables the save method says additional data like the models configuration and even the state of the optimizer a model that was saved using the save method can be loaded with the function Kira's thought models that load underscore model and as you can see we have the accuracy of a trained model in addition to everything we've looked at tensorflow also has a very important file format called saved model this is a file format that allows to exchange models between many different parts of tensorflow like tens of your Python ten Sephirot Jess and also tells the full light we are currently building out first hand support for saved modeling Kira's and you can check out the links below to read more about it and that's it for this episode of the coding tensorflow make sure to subscribe to the channel to get more videos like this now it's your turn to go out there and create some great Manos don't forget to tell us all about it [Music]