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TensorFlow 1.x and 2.x
Dataset uses TFRecord, a representation of the data (in any format) that can be easily
ported across multiple systems and is independent of the particular model used for
training. In short, dataset allows more flexibility than what we had in TensorFlow 1.0
with feed-dict.
tf.keras or Estimators?
In addition to the direct graph computation and to the tf.keras higher-level APIs,
TensorFlow 1.x and 2.x have an additional set of higher-level APIs called Estimators.
With Estimators, you do not need to worry about creating computational graphs or
handling sessions, since Estimators deal with this on your behalf, in a similar way
to tf.keras.
But what are Estimators? Put simply, they are another way to build or to use prebuilt
bricks. A longer answer is that they are highly efficient learning models for largescale
production-ready environments, which can be trained on single machines
or on distributed multi-servers, and they can run on CPUs, GPUs, or TPUs without
recoding your model. These models include Linear Classifiers, Deep Learning
Classifiers, Gradient Boosted Trees, and many more, which will be discussed in
the upcoming chapters.
Let's see an example of an Estimator used for building a classifier with 2 dense
hidden layers, each with 10 neurons, and with 3 output classes:
# Build a DNN with 2 hidden layers and 10 nodes in each hidden layer.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
The feature_columns=my_feature_columns is a list of feature columns each
describing a single feature you want the model to use. For example, a typical use
would be something like:
# Fetch the data
(train_x, train_y), (test_x, test_y) = load_data()
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
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