feature_column、fc.input_layer以及各种类型的column如何转化

1，数字类型的

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')

my_numeric_columns = [age,education_num, capital_gain, capital_loss, hours_per_week]

fc.input_layer(feature_batch, my_numeric_columns).numpy()

2，Categorical columns vocabulary

relationship = fc.categorical_column_with_vocabulary_list(
'relationship',
['Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried', 'Other-relative'])

fc.input_layer(feature_batch, [age, fc.indicator_column(relationship)])

3，If we don't know the set of possible values in advance

occupation = tf.feature_column.categorical_column_with_hash_bucket(
'occupation', hash_bucket_size=1000)

for item in feature_batch['occupation'].numpy():
print(item.decode())

occupation_result = fc.input_layer(feature_batch, [fc.indicator_column(occupation)])

occupation_result.numpy().shape

4，综合

my_categorical_columns = [relationship, occupation, education, marital_status, workclass]

classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns+my_categorical_columns)
classifier.train(train_inpf)
result = classifier.evaluate(test_inpf)

clear_output()

for key,value in sorted(result.items()):
print('%s: %s' % (key, value))

classifier = tf.estimator.LinearClassifier(feature_columns=my_numeric_columns+my_categorical_columns)

classifier.train(train_inpf)
result = classifier.evaluate(test_inpf)

clear_output()

for key,value in sorted(result.items()):
print('%s: %s' % (key, value))

5，连续数值的分列桶化

age_buckets = tf.feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])

fc.input_layer(feature_batch, [age, age_buckets]).numpy()

6，联合多个列

education_x_occupation = tf.feature_column.crossed_column(
['education', 'occupation'], hash_bucket_size=1000)

age_buckets_x_education_x_occupation = tf.feature_column.crossed_column(
[age_buckets, 'education', 'occupation'], hash_bucket_size=1000)

7，总之，有这些类型

• CategoricalColumn
• NumericColumn
• BucketizedColumn
• CrossedColumn

import tempfile

base_columns = [
education, marital_status, relationship, workclass, occupation,
age_buckets,
]

crossed_columns = [
tf.feature_column.crossed_column(
['education', 'occupation'], hash_bucket_size=1000),
tf.feature_column.crossed_column(
[age_buckets, 'education', 'occupation'], hash_bucket_size=1000),
]

model = tf.estimator.LinearClassifier(
model_dir=tempfile.mkdtemp(),
feature_columns=base_columns + crossed_columns,
optimizer=tf.train.FtrlOptimizer(learning_rate=0.1))