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Save and restore models

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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
!pip install -q tf_nightly

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, unicode_literals

import os

import tensorflow as tf
from tensorflow import keras

tf.__version__
'1.14.1-dev20190625'
(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.keras.activations.relu, input_shape=(784,)),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation=tf.keras.activations.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()
WARNING: Logging before flag parsing goes to stderr.
W0625 16:20:01.280914 140498918131456 deprecation.py:506] From /tmpfs/src/tf_docs_env/lib/python3.5/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1624: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 512)               401920    
_________________________________________________________________
dropout (Dropout)            (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                5130      
=================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
_________________________________________________________________

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 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.
W0625 16:20:01.748211 140498918131456 deprecation.py:323] From /tmpfs/src/tf_docs_env/lib/python3.5/site-packages/tensorflow_core/python/keras/optimizer_v2/optimizer_v2.py:460: BaseResourceVariable.constraint (from tensorflow.python.ops.resource_variable_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Apply a constraint manually following the optimizer update step.

Train on 1000 samples, validate on 1000 samples
Epoch 1/10
 512/1000 [==============>...............] - ETA: 0s - loss: 1.5379 - acc: 0.5684
Epoch 00001: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 411us/sample - loss: 1.1356 - acc: 0.6880 - val_loss: 0.7020 - val_acc: 0.7920
Epoch 2/10
 544/1000 [===============>..............] - ETA: 0s - loss: 0.4638 - acc: 0.8787
Epoch 00002: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 167us/sample - loss: 0.4262 - acc: 0.8870 - val_loss: 0.5081 - val_acc: 0.8430
Epoch 3/10
 544/1000 [===============>..............] - ETA: 0s - loss: 0.3168 - acc: 0.9099
Epoch 00003: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 159us/sample - loss: 0.2988 - acc: 0.9150 - val_loss: 0.5249 - val_acc: 0.8250
Epoch 4/10
 576/1000 [================>.............] - ETA: 0s - loss: 0.2104 - acc: 0.9358
Epoch 00004: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 157us/sample - loss: 0.2119 - acc: 0.9430 - val_loss: 0.4470 - val_acc: 0.8560
Epoch 5/10
 576/1000 [================>.............] - ETA: 0s - loss: 0.1537 - acc: 0.9670
Epoch 00005: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 161us/sample - loss: 0.1540 - acc: 0.9690 - val_loss: 0.4208 - val_acc: 0.8650
Epoch 6/10
 576/1000 [================>.............] - ETA: 0s - loss: 0.1134 - acc: 0.9792
Epoch 00006: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 161us/sample - loss: 0.1195 - acc: 0.9770 - val_loss: 0.4184 - val_acc: 0.8700
Epoch 7/10
 576/1000 [================>.............] - ETA: 0s - loss: 0.0846 - acc: 0.9896
Epoch 00007: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 159us/sample - loss: 0.0893 - acc: 0.9890 - val_loss: 0.3985 - val_acc: 0.8740
Epoch 8/10
 544/1000 [===============>..............] - ETA: 0s - loss: 0.0631 - acc: 0.9926
Epoch 00008: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 161us/sample - loss: 0.0672 - acc: 0.9900 - val_loss: 0.3892 - val_acc: 0.8710
Epoch 9/10
 576/1000 [================>.............] - ETA: 0s - loss: 0.0574 - acc: 0.9965
Epoch 00009: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 160us/sample - loss: 0.0555 - acc: 0.9950 - val_loss: 0.4202 - val_acc: 0.8640
Epoch 10/10
 576/1000 [================>.............] - ETA: 0s - loss: 0.0369 - acc: 0.9983
Epoch 00010: saving model to training_1/cp.ckpt
1000/1000 [==============================] - 0s 161us/sample - loss: 0.0397 - acc: 0.9980 - val_loss: 0.4034 - val_acc: 0.8750

<tensorflow.python.keras.callbacks.History at 0x7fc81ae8dc18>

This creates a single collection of TensorFlow checkpoint files that are updated at the end of each epoch:

!ls {checkpoint_dir}
checkpoint           cp.ckpt.data-00001-of-00002
cp.ckpt.data-00000-of-00002  cp.ckpt.index

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))
1000/1000 [==============================] - 0s 81us/sample - loss: 2.4326 - acc: 0.0830
Untrained model, accuracy:  8.30%

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))
1000/1000 [==============================] - 0s 49us/sample - loss: 0.4034 - acc: 0.8750
Restored model, accuracy: 87.50%

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.save_weights(checkpoint_path.format(epoch=0))
model.fit(train_images, train_labels,
          epochs = 50, callbacks = [cp_callback],
          validation_data = (test_images,test_labels),
          verbose=0)
W0625 16:20:04.665833 140498918131456 callbacks.py:861] `period` argument is deprecated. Please use `save_freq` to specify the frequency in number of samples seen.


Epoch 00005: saving model to training_2/cp-0005.ckpt

Epoch 00010: saving model to training_2/cp-0010.ckpt

Epoch 00015: saving model to training_2/cp-0015.ckpt

Epoch 00020: saving model to training_2/cp-0020.ckpt

Epoch 00025: saving model to training_2/cp-0025.ckpt

Epoch 00030: saving model to training_2/cp-0030.ckpt

Epoch 00035: saving model to training_2/cp-0035.ckpt

Epoch 00040: saving model to training_2/cp-0040.ckpt

Epoch 00045: saving model to training_2/cp-0045.ckpt

Epoch 00050: saving model to training_2/cp-0050.ckpt

<tensorflow.python.keras.callbacks.History at 0x7fc84fdf9898>

Now, look at the resulting checkpoints and choose the latest one:

! ls {checkpoint_dir}
checkpoint            cp-0025.ckpt.data-00001-of-00002
cp-0000.ckpt.data-00000-of-00002  cp-0025.ckpt.index
cp-0000.ckpt.data-00001-of-00002  cp-0030.ckpt.data-00000-of-00002
cp-0000.ckpt.index        cp-0030.ckpt.data-00001-of-00002
cp-0005.ckpt.data-00000-of-00002  cp-0030.ckpt.index
cp-0005.ckpt.data-00001-of-00002  cp-0035.ckpt.data-00000-of-00002
cp-0005.ckpt.index        cp-0035.ckpt.data-00001-of-00002
cp-0010.ckpt.data-00000-of-00002  cp-0035.ckpt.index
cp-0010.ckpt.data-00001-of-00002  cp-0040.ckpt.data-00000-of-00002
cp-0010.ckpt.index        cp-0040.ckpt.data-00001-of-00002
cp-0015.ckpt.data-00000-of-00002  cp-0040.ckpt.index
cp-0015.ckpt.data-00001-of-00002  cp-0045.ckpt.data-00000-of-00002
cp-0015.ckpt.index        cp-0045.ckpt.data-00001-of-00002
cp-0020.ckpt.data-00000-of-00002  cp-0045.ckpt.index
cp-0020.ckpt.data-00001-of-00002  cp-0050.ckpt.data-00000-of-00002
cp-0020.ckpt.index        cp-0050.ckpt.data-00001-of-00002
cp-0025.ckpt.data-00000-of-00002  cp-0050.ckpt.index
latest = tf.train.latest_checkpoint(checkpoint_dir)
latest
'training_2/cp-0050.ckpt'

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))
1000/1000 [==============================] - 0s 84us/sample - loss: 0.4801 - acc: 0.8780
Restored model, accuracy: 87.80%

What are these files?

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))
1000/1000 [==============================] - 0s 86us/sample - loss: 0.4801 - acc: 0.8780
Restored model, accuracy: 87.80%

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 (depends on set up). 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 is very useful—you can load them in TensorFlow.js (HDF5, Saved Model) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (HDF5, Saved Model)

As an HDF5 file

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')
Train on 1000 samples
Epoch 1/5
1000/1000 [==============================] - 0s 193us/sample - loss: 1.1379 - acc: 0.6760
Epoch 2/5
1000/1000 [==============================] - 0s 99us/sample - loss: 0.4106 - acc: 0.8860
Epoch 3/5
1000/1000 [==============================] - 0s 98us/sample - loss: 0.2821 - acc: 0.9290
Epoch 4/5
1000/1000 [==============================] - 0s 95us/sample - loss: 0.2006 - acc: 0.9510
Epoch 5/5
1000/1000 [==============================] - 0s 92us/sample - loss: 0.1530 - acc: 0.9720

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()
W0625 16:20:15.173478 140498918131456 deprecation.py:506] From /tmpfs/src/tf_docs_env/lib/python3.5/site-packages/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0625 16:20:15.175293 140498918131456 deprecation.py:506] From /tmpfs/src/tf_docs_env/lib/python3.5/site-packages/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0625 16:20:15.615709 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.iter
W0625 16:20:15.616829 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.beta_1
W0625 16:20:15.617507 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.beta_2
W0625 16:20:15.618822 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.decay
W0625 16:20:15.620003 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.learning_rate
W0625 16:20:15.620734 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-0.kernel
W0625 16:20:15.621699 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-0.bias
W0625 16:20:15.623017 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.kernel
W0625 16:20:15.624008 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.bias
W0625 16:20:15.624606 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-0.kernel
W0625 16:20:15.625117 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-0.bias
W0625 16:20:15.625815 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.kernel
W0625 16:20:15.626943 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.bias
W0625 16:20:15.627474 140498918131456 util.py:260] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.
W0625 16:20:15.629154 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.iter
W0625 16:20:15.629958 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.beta_1
W0625 16:20:15.630483 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.beta_2
W0625 16:20:15.631611 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.decay
W0625 16:20:15.632149 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer.learning_rate
W0625 16:20:15.632982 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-0.kernel
W0625 16:20:15.633531 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-0.bias
W0625 16:20:15.634231 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.kernel
W0625 16:20:15.634750 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.bias
W0625 16:20:15.636236 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-0.kernel
W0625 16:20:15.637084 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-0.bias
W0625 16:20:15.637722 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.kernel
W0625 16:20:15.638738 140498918131456 util.py:252] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.bias
W0625 16:20:15.639858 140498918131456 util.py:260] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.

Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_12 (Dense)             (None, 512)               401920    
_________________________________________________________________
dropout_6 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_13 (Dense)             (None, 10)                5130      
=================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
_________________________________________________________________

Check its accuracy:

loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
1000/1000 [==============================] - 0s 103us/sample - loss: 0.4288 - acc: 0.8630
Restored model, accuracy: 86.30%

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 lose the state of the optimizer.

As a saved_model

Build a fresh model:

model = create_model()

model.fit(train_images, train_labels, epochs=5)
Train on 1000 samples
Epoch 1/5
1000/1000 [==============================] - 0s 191us/sample - loss: 1.1219 - acc: 0.6720
Epoch 2/5
1000/1000 [==============================] - 0s 96us/sample - loss: 0.4190 - acc: 0.8820
Epoch 3/5
1000/1000 [==============================] - 0s 96us/sample - loss: 0.2786 - acc: 0.9240
Epoch 4/5
1000/1000 [==============================] - 0s 100us/sample - loss: 0.2121 - acc: 0.9430
Epoch 5/5
1000/1000 [==============================] - 0s 94us/sample - loss: 0.1491 - acc: 0.9670

<tensorflow.python.keras.callbacks.History at 0x7fc84f3b00f0>

Create a saved_model:

import time

saved_model_path = "./saved_models/"+str(int(time.time()))
tf.contrib.saved_model.save_keras_model(model, saved_model_path)
W0625 16:20:17.680878 140498918131456 lazy_loader.py:50] 
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

W0625 16:20:18.605420 140498918131456 deprecation.py:323] From <ipython-input-20-998dfe524d77>:4: export_saved_model (from tensorflow.python.keras.saving.saved_model_experimental) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `model.save(..., save_format="tf")` or `tf.keras.models.save_model(..., save_format="tf")`.
W0625 16:20:19.454333 140498918131456 deprecation.py:323] From /tmpfs/src/tf_docs_env/lib/python3.5/site-packages/tensorflow_core/python/saved_model/signature_def_utils_impl.py:253: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
W0625 16:20:19.455988 140498918131456 export_utils.py:182] Export includes no default signature!
W0625 16:20:19.701409 140498918131456 export_utils.py:182] Export includes no default signature!

Have a look in the directory:

!ls {saved_model_path}
assets  saved_model.pb  variables

Reload a fresh keras model from the saved model.

new_model = tf.contrib.saved_model.load_keras_model(saved_model_path)
new_model.summary()
W0625 16:20:20.011014 140498918131456 deprecation.py:323] From <ipython-input-22-f5ba023fc0b4>:1: load_from_saved_model (from tensorflow.python.keras.saving.saved_model_experimental) is deprecated and will be removed in a future version.
Instructions for updating:
The experimental save and load functions have been  deprecated. Please switch to `tf.keras.models.load_model`.

Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_14 (Dense)             (None, 512)               401920    
_________________________________________________________________
dropout_7 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_15 (Dense)             (None, 10)                5130      
=================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
_________________________________________________________________

Run the restored model.

# The model has to be compiled before evaluating.
# This step is not required if the saved model is only being deployed.

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

# Evaluate the restored model.
loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
1000/1000 [==============================] - 0s 113us/sample - loss: 0.4722 - acc: 0.8570
Restored model, accuracy: 85.70%

What's Next

That was a quick guide to saving and loading in with tf.keras.

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