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  • [MUSIC PLAYING]

  • MAGNUS HYTTSTEN: Hi there, everybody.

  • What's up?

  • My name is Magnus, and you're watching Cording TensorFlow--

  • the show where you learn how to code in TensorFlow.

  • [MUSIC PLAYING]

  • All right.

  • 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,

  • where real-life models can take days or even weeks to train.

  • So if you were to hit Control-C on your training job

  • after it's been running for a day or so,

  • all your model weights and values will be lost,

  • and you would have to restart training from the beginning

  • and be a very sad camper.

  • But if you saved 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 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 going to do now.

  • Check out the code!

  • 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 JAML support.

  • And here we do some imports, and print the TensorFlow version.

  • It's totally OK if you have a later version than me here.

  • We use the MNIST data set to demonstrate

  • model loading and saving.

  • Then reshape the images to batches of 28

  • by 28 arrays, which is the pixel size of MNIST images,

  • and normalize all pixel values to be between 0 and 1.

  • Next is the model definition, which

  • is defined in the create_model function.

  • This is a very basic model, which

  • is totally OK, because in this screencast

  • we're interested in learning how to load and save models,

  • not creating the best model for the MNIST dataset.

  • And here, we finally get to see how a model can be saved.

  • checkpoint_path will be the path of the saved 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 the saving is performed.

  • Finally, we perform the model training

  • by calling the fit method and providing this callback.

  • As you can see, this will cause a model

  • to be saved once every epoch has been completed.

  • And if we look at the checkpoints directory,

  • we can now see three files.

  • The cp.ckpt.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.ckpt.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_weights

  • with our checkpoint path, our model

  • gets initialized with the previous training state,

  • and has much better accuracy.

  • OK.

  • 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 use the value 5, which as you can see

  • saves a new model every five epochs.

  • Observe in this case, we also use a parameterized filing

  • based on the epoch.

  • This means a unique file is saved every time.

  • That's also why we can see multiple files when looking

  • at the checkpoint directory.

  • We can also use a function called

  • tf.train.latest_checkpoint that will return the latest model,

  • which was saved--

  • in our case, the one with index 50.

  • This function 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_weights function

  • like we did before, providing the value returned

  • by tf.train.latest_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 save_weights_only

  • to true last time we saved a model.

  • In addition to only saving variables,

  • the save method saves additional data,

  • like the model's configuration and even the state

  • of the optimizer.

  • A model that was saved using the save method can be loaded with

  • the function keras.models.load_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 SavedModel.

  • This is a file format that allows

  • to exchange models between many different parts of TensorFlow,

  • like TensorFlow Python, TensorFlow.js,.

  • And also TensorFlow Lite.

  • We are currently building out first-hand support

  • for SavedModel in Keras, and you can check out the links below

  • to read more about it.

  • And that's it for this episode of 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 models.

  • And don't forget to tell us all about it.

  • [MUSIC PLAYING]

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保存和加載模型(TensorFlow編碼) (Saving and Loading Models (Coding TensorFlow))

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    林宜悉 發佈於 2021 年 01 月 14 日
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