特征列

默认数据类型 (tf.float32)

indicator_column = tf.feature_column.indicator_column(categorical_column)

embedding_column = tf.feature_column.embedding_column( <br> categorical_column=categorical_column, <br> dimension=dimension_of_embedding_vector)

identity_feature_column = tf.feature_column.categorical_column_with_identity( <br> key='my_feature_b', <br> num_buckets=4)

vocabulary_feature_column = <br> tf.feature_column.categorical_column_with_vocabulary_list( <br> key="a feature returned by input_fn()", <br> vocabulary_list=["kitchenware", "electronics", "sports"])

ocabulary_feature_column = <br> tf.feature_column.categorical_column_with_vocabulary_file( <br> key="a feature returned by input_fn()", <br> vocabulary_file="product_class.txt", <br> vocabulary_size=3)

hashed_feature_column = <br> tf.feature_column.categorical_column_with_hash_bucket( <br> key = "some_feature", <br> hash_buckets_size = 100) # The number of categories

crossed_lat_lon_fc = tf.feature_column.crossed_column( <br> [latitude_bucket_fc, longitude_bucket_fc], 5000)