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Model progress can be saved during—and after—training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share:
Sharing this data helps others understand how the model works and try it themselves with new data.
Caution: Be careful with untrusted code—TensorFlow models are code. See Using TensorFlow Securely for details.
There are different ways to save TensorFlow models—depending on the API you're using. This guide uses tf.keras, a high-level API to build and train models in TensorFlow. For other approaches, see the TensorFlow Save and Restore guide or Saving in eager.
Install and import TensorFlow and dependencies:
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!pip install pyyaml h5py # Required to save models in HDF5 format
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import os
import tensorflow as tf
from tensorflow import keras
print(tf.version.VERSION)
To demonstrate how to save and load weights, you'll use the MNIST dataset. To speed up these runs, use the first 1000 examples:
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(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
train_labels = train_labels[:1000]
test_labels = test_labels[:1000]
train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0
Start by building a simple sequential model:
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# Define a simple sequential model
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Dense(512, activation='relu', input_shape=(784,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
return model
# Create a basic model instance
model = create_model()
# Display the model's architecture
model.summary()
You can use a trained model without having to retrain it, or pick-up training where you left off—in case the training process was interrupted. The tf.keras.callbacks.ModelCheckpoint callback allows to continually save the model both during and at the end of training.
Create a tf.keras.callbacks.ModelCheckpoint callback that saves weights only during training:
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checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create a callback that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
save_weights_only=True,
verbose=1)
# Train the model with the new callback
model.fit(train_images,
train_labels,
epochs=10,
validation_data=(test_images,test_labels),
callbacks=[cp_callback]) # Pass callback to training
# This may generate warnings related to saving the state of the optimizer.
# These warnings (and similar warnings throughout this notebook)
# are in place to discourage outdated usage, and can be ignored.
This creates a single collection of TensorFlow checkpoint files that are updated at the end of each epoch:
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!ls {checkpoint_dir}
Create a new, untrained model. When restoring a model from weights-only, you must have a model with the same architecture as the original model. Since it's the same model architecture, you can share weights despite that it's a different instance of the model.
Now rebuild a fresh, untrained model, and evaluate it on the test set. An untrained model will perform at chance levels (~10% accuracy):
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# Create a basic model instance
model = create_model()
# Evaluate the model
loss, acc = model.evaluate(test_images, test_labels, verbose=2)
print("Untrained model, accuracy: {:5.2f}%".format(100*acc))
Then load the weights from the checkpoint and re-evaluate:
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# Loads the weights
model.load_weights(checkpoint_path)
# Re-evaluate the model
loss,acc = model.evaluate(test_images, test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
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# Include the epoch in the file name (uses `str.format`)
checkpoint_path = "training_2/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create a callback that saves the model's weights every 5 epochs
cp_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_path,
verbose=1,
save_weights_only=True,
period=5)
# Create a new model instance
model = create_model()
# Save the weights using the `checkpoint_path` format
model.save_weights(checkpoint_path.format(epoch=0))
# Train the model with the new callback
model.fit(train_images,
train_labels,
epochs=50,
callbacks=[cp_callback],
validation_data=(test_images,test_labels),
verbose=0)
Now, look at the resulting checkpoints and choose the latest one:
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!ls {checkpoint_dir}
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latest = tf.train.latest_checkpoint(checkpoint_dir)
latest
Note: the default tensorflow format only saves the 5 most recent checkpoints.
To test, reset the model and load the latest checkpoint:
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# Create a new model instance
model = create_model()
# Load the previously saved weights
model.load_weights(latest)
# Re-evaluate the model
loss, acc = model.evaluate(test_images, test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
The above code stores the weights to a collection of checkpoint-formatted files that contain only the trained weights in a binary format. Checkpoints contain:
If you are only training a model on a single machine, you'll have one shard with the suffix: .data-00000-of-00001
You saw how to load the weights into a model. Manually saving them is just as simple with the Model.save_weights method. By default, tf.keras—and save_weights in particular—uses the TensorFlow checkpoint format with a .ckpt extension (saving in HDF5 with a .h5 extension is covered in the Save and serialize models guide):
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# Save the weights
model.save_weights('./checkpoints/my_checkpoint')
# Create a new model instance
model = create_model()
# Restore the weights
model.load_weights('./checkpoints/my_checkpoint')
# Evaluate the model
loss,acc = model.evaluate(test_images, test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
Call model.save to save a model's architecture, weights, and training configuration in a single file/folder. This allows you to export a model so it can be used without access to the original Python code*. Since the optimizer-state is recovered, you can resume training from exactly where you left off.
Entire model can be saved in two different file formats (SavedModel and HDF5). It is to be noted that TensorFlow SavedModel format is the default file format in TF2.x. However, model can be saved in HDF5 format. More details on saving entire model in the two file formats is described below.
Saving a fully-functional model is very useful—you can load them in TensorFlow.js (Saved Model, HDF5) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (Saved Model, HDF5)
*Custom objects (e.g. subclassed models or layers) require special attention when saving and loading. See the Saving custom objects section below
The SavedModel format is another way to serialize models. Models saved in this format can be restored using tf.keras.models.load_model and are compatible with TensorFlow Serving. The SavedModel guide goes into detail about how to serve/inspect the SavedModel. The section below illustrates the steps to saving and restoring the model.
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# Create and train a new model instance.
model = create_model()
model.fit(train_images, train_labels, epochs=5)
# Save the entire model as a SavedModel.
!mkdir -p saved_model
model.save('saved_model/my_model')
The SavedModel format is a directory containing a protobuf binary and a Tensorflow checkpoint. Inspect the saved model directory:
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# my_model directory
!ls saved_model
# Contains an assets folder, saved_model.pb, and variables folder.
!ls saved_model/my_model
Reload a fresh Keras model from the saved model:
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new_model = tf.keras.models.load_model('saved_model/my_model')
# Check its architecture
new_model.summary()
The restored model is compiled with the same arguments as the original model. Try running evaluate and predict with the loaded model:
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# Evaluate the restored model
loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)
print('Restored model, accuracy: {:5.2f}%'.format(100*acc))
print(new_model.predict(test_images).shape)
Keras provides a basic save format using the HDF5 standard.
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# Create and train a new model instance.
model = create_model()
model.fit(train_images, train_labels, epochs=5)
# Save the entire model to a HDF5 file.
# The '.h5' extension indicates that the model should be saved to HDF5.
model.save('my_model.h5')
Now, recreate the model from that file:
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# Recreate the exact same model, including its weights and the optimizer
new_model = tf.keras.models.load_model('my_model.h5')
# Show the model architecture
new_model.summary()
Check its accuracy:
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loss, acc = new_model.evaluate(test_images, test_labels, verbose=2)
print('Restored model, accuracy: {:5.2f}%'.format(100*acc))
Keras saves models by inspecting the architecture. This technique saves everything:
Keras is not able to save the v1.x optimizers (from tf.compat.v1.train) since they aren't compatible with checkpoints. For v1.x optimizers, you need to re-compile the model after loading—losing the state of the optimizer.
If you are using the SavedModel format, you can skip this section. The key difference between HDF5 and SavedModel is that HDF5 uses object configs to save the model architecture, while SavedModel saves the execution graph. Thus, SavedModels are able to save custom objects like subclassed models and custom layers without requiring the orginal code.
To save custom objects to HDF5, you must do the following:
get_config method in your object, and optionally a from_config classmethod.get_config(self) returns a JSON-serializable dictionary of parameters needed to recreate the object.from_config(cls, config) uses the returned config from get_config to create a new object. By default, this function will use the config as initialization kwargs (return cls(**config)).custom_objects argument when loading the model. The argument must be a dictionary mapping the string class name to the Python class. E.g. tf.keras.models.load_model(path, custom_objects={'CustomLayer': CustomLayer})See the Writing layers and models from scratch tutorial for examples of custom objects and get_config.