-
Notifications
You must be signed in to change notification settings - Fork 0
/
day016.py
37 lines (30 loc) · 1.29 KB
/
day016.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
# Numeric columns.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Bucketized column.
numeric_feature_column = tf.feature_column.numeric_column("Year")
bucketized_feature_column = tf.feature_column.bucketized_column(
source_column = numeric_feature_column,
boundaries = [1960, 1980, 2000])
# Categorical column with identify
identity_feature_column = tf.feature_column.categorical_column_with_identity(
key='my_feature_b',
num_buckets=4) # Values [0, 4)
# Categorical column with vocabulary list
vocabulary_feature_column =
tf.feature_column.categorical_column_with_vocabulary_list(
key=feature_name_from_input_fn,
vocabulary_list=["kitchenware", "electronics", "sports"])
# Hached bucket column
hashed_feature_column =
tf.feature_column.categorical_column_with_hash_bucket(
key = "some_feature",
hash_buckets_size = 100) # The number of categories
# Categorical column as an indicator column.
indicator_column = tf.feature_column.indicator_column(categorical_column)
# Embedding column
categorical_column = ... # Create any categorical column
embedding_column = tf.feature_column.embedding_column(
categorical_column=categorical_column,
dimension=embedding_dimensions)