def default_input_fn(unused_estimator, examples):
return layers.parse_feature_columns_from_examples(
examples, self._feature_columns)
def default_input_fn(unused_estimator, examples):
return layers.parse_feature_columns_from_examples(
examples, self._feature_columns)
def parse_feature_columns_from_examples_test():
"""Construct examples by tf.train.Example.
Then, parse feature columns from examples.
Finally, get input from feature columns.
Returns:
The input tensor transformed from examples in defined feature columns
format.
"""
language_column = layers.sparse_column_with_hash_bucket(
"language", hash_bucket_size=20)
feature_columns = [
layers.embedding_column(language_column, dimension=3),
layers.real_valued_column("age", dtype=tf.int64)
]
example1 = tf.train.Example(features=tf.train.Features(
feature={
"age":
tf.train.Feature(int64_list=tf.train.Int64List(value=[18])),
"language":
tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"en"]))
}))
example2 = tf.train.Example(features=tf.train.Features(
feature={
"age":
tf.train.Feature(int64_list=tf.train.Int64List(value=[20])),
"language":
tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"fr"]))
}))
example3 = tf.train.Example(features=tf.train.Features(
feature={
"age":
tf.train.Feature(int64_list=tf.train.Int64List(value=[25])),
"language":
tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"en"]))
}))
examples = [
example1.SerializeToString(),
example2.SerializeToString(),
example3.SerializeToString()
]
print(examples)
# feature_lists = tf.train.FeatureLists(
# feature_list={
# "age": tf.train.FeatureList(
# feature=[
# tf.train.Feature(int64_list=tf.train.Int64List(value=[18])),
# tf.train.Feature(int64_list=tf.train.Int64List(value=[20])),
# tf.train.Feature(int64_list=tf.train.Int64List(value=[25])),
# ]
# ),
# "language": tf.train.FeatureList(
# feature=[
# tf.train.Feature(bytes_list=tf.train.BytesList(value=[
# b"en"])),
# tf.train.Feature(bytes_list=tf.train.BytesList(value=[
# b"fr"])),
# tf.train.Feature(bytes_list=tf.train.BytesList(value=[
# b"zh"]))
# ]
# )
# }
# )
# print(feature_lists)
# serialized = feature_lists.SerializeToString()
columns_to_tensor = layers.parse_feature_columns_from_examples(
serialized=examples, feature_columns=feature_columns)
input_layer = layers.input_from_feature_columns(
columns_to_tensors=columns_to_tensor, feature_columns=feature_columns)
print("input_layer:\n", str(input_layer))
sess = tf.InteractiveSession()
tf.initialize_all_variables().run(session=sess)
print(input_layer.eval(session=sess))
