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Chapter 2
If you want to save a model together with its weights and the optimization
parameters, then you simply use:
model.save('my_model.h5') # save
model = tf.keras.models.load_model('my_model.h5') #restore
Training from tf.data.datasets
Another benefit of using TensorFlow 2.x is the introduction of TensorFlow datasets
as a principled mechanism to deal with heterogeneous (large) datasets in different
categories such as audio, image, video, text, and translation. Let's first use pip to
install tensorflow-datasets:
pip install tensorflow-datasets
As of September 2019 there are already 85+ commonly used
datasets available, but more are planned to be added in https://
www.tensorflow.org/datasets/datasets.
Then, we can list all the datasets available, and load MNIST together with metadata:
import tensorflow as tf
import tensorflow_datasets as tfds
# See all registered datasets
builders = tfds.list_builders()
print(builders)
# Load a given dataset by name, along with the DatasetInfo metadata
data, info = tfds.load("mnist", with_info=True)
train_data, test_data = data['train'], data['test']
print(info)
Then, we get the following lists of datasets:
['bair_robot_pushing_small', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq',
'cifar10', 'cifar100', 'coco2014', 'diabetic_retinopathy_detection',
'dummy_dataset_shared_generator', 'dummy_mnist', 'fashion_mnist', 'image_
label_folder', 'imagenet2012', 'imdb_reviews', 'lm1b', 'lsun', 'mnist',
'moving_mnist', 'nsynth', 'omniglot', 'open_images_v4', 'quickdraw_
bitmap', 'squad', 'starcraft_video', 'svhn_cropped', 'tf_flowers', 'wmt_
translate_ende', 'wmt_translate_enfr']
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