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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:

  • code to create the model, and
  • the trained weights, or parameters, for the model

Sharing this data helps others understand how the model works and try it themselves with new data.

Options

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.

Setup
Installs and imports

Install and import TensorFlow and dependencies:
pip install -q h5py pyyaml

Get an example dataset

We’ll use the MNIST dataset to train our model to demonstrate saving weights. To speed up these demonstration runs, only use the first 1000 examples:
from __future__ import absolute_import, division, print_function

import os

import tensorflow as tf
from tensorflow import keras

(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

Define a model

Let’s build a simple model we’ll use to demonstrate saving and loading weights.
# Returns a short sequential model
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])

return model

# Create a basic model instance
model = create_model()
model.summary()

Save checkpoints during training

The primary use case is to automatically save checkpoints during and at the end of training. This way you can use a trained model without having to retrain it, or pick-up training where you left of—in case the training process was interrupted.

tf.keras.callbacks.ModelCheckpoint is a callback that performs this task. The callback takes a couple of arguments to configure checkpointing.
Checkpoint callback usage

Train the model and pass it the ModelCheckpoint callback:
checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

# Create checkpoint callback
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weights_only=True,
verbose=1)

model = create_model()

model.fit(train_images, train_labels, epochs = 10,
validation_data = (test_images,test_labels),
callbacks = [cp_callback]) # pass callback to training

This creates a single collection of TensorFlow checkpoint files that are updated at the end of each epoch:
!ls {checkpoint_dir}
Create a new, untrained model. When restoring a model from only weights, you must have a model with the same architecture as the original model. Since it’s the same model architecture, we 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):
model = create_model()

loss, acc = model.evaluate(test_images, test_labels)
print("Untrained model, accuracy: {:5.2f}%".format(100*acc))

Then load the weights from the checkpoint, and re-evaluate:
model.load_weights(checkpoint_path)
loss,acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

Checkpoint callback options

The callback provides several options to give the resulting checkpoints unique names, and adjust the checkpointing frequency.

Train a new model, and save uniquely named checkpoints once every 5-epochs:
# 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)

cp_callback = tf.keras.callbacks.ModelCheckpoint(
checkpoint_path, verbose=1, save_weights_only=True,
# Save weights, every 5-epochs.
period=5)

model = create_model()
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:
! ls {checkpoint_dir}
latest = tf.train.latest_checkpoint(checkpoint_dir)
latest

To test, reset the model and load the latest checkpoint:
model = create_model()
model.load_weights(latest)
loss, acc = model.evaluate(test_images, test_labels)
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: * One or more shards that contain your model’s weights. * An index file that indicates which weights are stored in a which shard.

If you are only training a model on a single machine, you’ll have one shard with the suffix: .data-00000-of-00001

Manually save weights

Above you saw how to load the weights into a model.
Manually saving the weights is just as simple, use the Model.save_weights method.
# Save the weights
model.save_weights('./checkpoints/my_checkpoint')

# Restore the weights
model = create_model()
model.load_weights('./checkpoints/my_checkpoint')

loss,acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

Save the entire model

The entire model can be saved to a file that contains the weight values, the model’s configuration, and even the optimizer’s configuration. This allows you to checkpoint a model and resume training later—from the exact same state—without access to the original code.

Saving a fully-functional model in Keras is very useful—you can load them in TensorFlow.js and then train and run them in web browsers.

Keras provides a basic save format using the HDF5 standard. For our purposes, the saved model can be treated as a single binary blob.
model = create_model()

model.fit(train_images, train_labels, epochs=5)

# Save entire model to a HDF5 file
model.save('my_model.h5')

Now recreate the model from that file:
# Recreate the exact same model, including weights and optimizer.
new_model = keras.models.load_model('my_model.h5')
new_model.summary()

Check its accuracy:
loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

This technique saves everything:

  • The weight values
  • The model’s configuration(architecture)
  • The optimizer configuration

Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from tf.train). When using those you will need to re-compile the model after loading, and you will loose the state of the optimizer.

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