TensorFlow广度和深度学习的教程

"Never-married", "Separated", "Married-AF-spouse", "Widowed"

])

relationship = tf.feature_column.categorical_column_with_vocabulary_list(

"relationship", [

"Husband", "Not-in-family", "Wife", "Own-child", "Unmarried",

"Other-relative"

])

workclass = tf.feature_column.categorical_column_with_vocabulary_list(

"workclass", [

"Self-emp-not-inc", "Private", "State-gov", "Federal-gov",

"Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked"

])

# 展示一个哈希的例子：

occupation = tf.feature_column.categorical_column_with_hash_bucket(

"occupation", hash_bucket_size=1000)

native_country = tf.feature_column.categorical_column_with_hash_bucket(

"native_country", hash_bucket_size=1000)

# 持续基列

age = tf.feature_column.numeric_column("age")

education_num = tf.feature_column.numeric_column("education_num")

capital_gain = tf.feature_column.numeric_column("capital_gain")

capital_loss = tf.feature_column.numeric_column("capital_loss")

hours_per_week = tf.feature_column.numeric_column("hours_per_week")

# 转换

age_buckets = tf.feature_column.bucketized_column(

age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])

广度模型：具有交叉特点列的线性模型

广度模型是一个具有稀少和交叉特点列的线性模型：

base_columns = [ 
 gender, native_country, education, occupation, workclass, relationship, 
 age_buckets, 
] 
 
crossed_columns = [ 
 tf.feature_column.crossed_column( 
 ["education", "occupation"], hash_bucket_size=1000), 
 tf.feature_column.crossed_column( 
 [age_buckets, "education", "occupation"	
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