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Chapter 2
There, tf.feature_column.numeric_column() represents real valued or numerical
features (https://www.tensorflow.org/api_docs/python/tf/feature_column/
numeric_column). Efficiency Estimators should be trained using tf.Datasets
as input. Here is an example where MNIST is loaded, scaled, shuffled, and batched:
import tensorflow as tf
import tensorflow_datasets as tfds
BUFFER_SIZE = 10000
BATCH_SIZE = 64
def input_fn(mode):
datasets, info = tfds.load(name='mnist',
with_info=True,
as_supervised=True)
mnist_dataset = (datasets['train'] if mode == tf.estimator.ModeKeys.
TRAIN else datasets['test'])
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
return mnist_dataset.map(scale).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
test = input_fn('test')
train = input_fn(tf.estimator.ModeKeys.TRAIN)
print(test)
print(train)
Then, the Estimator can be trained and evaluated by using tf.estimator.train_
and_evaluate() and passing the input_fn which will iterate on the data:
tf.estimator.train_and_evaluate(
classifier,
train_spec=tf.estimator.TrainSpec(input_fn=input_fn),
eval_spec=tf.estimator.EvalSpec(input_fn=input_fn)
)
TensorFlow contains Estimators for both regression and classification, which are
largely adopted by the community and they will continue to be supported through
at least the lifetime of TensorFlow 2.x.
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