def run_numeric_feature_column(normalizer_fn=None):
price = {'price': [[1., 1., 1., 1., 1., 1.], [2., 2., 2., 2., 2., 2.], [3., 3., 3., 3., 3., 3.], [4., 4., 4., 4., 4., 4.]]}
print(price['price'])
price_column = feature_column.numeric_column('price', shape=[6], normalizer_fn=normalizer_fn)
price_transformed_tensor = feature_column.input_layer(price, [price_column])
with tf.Session() as session:
print('Result:')
print(session.run([price_transformed_tensor]))
def run_embedding_feature_column():
example = {'example': [['A'], ['B'], ['C'], ['D'], ['E'], ['F'], ['G'], ['H'], ['I'], ['J']]}
print(example['example'])
example_column = feature_column.categorical_column_with_hash_bucket('example', hash_bucket_size=15)
example_column_embedding = feature_column.embedding_column(example_column, dimension=4)
example_transformed_tensor = feature_column.input_layer(example, [example_column_embedding])
with tf.Session() as session:
session.run(tf.global_variables_initializer())
print('Result:')
print(session.run([example_transformed_tensor]))
def run_categorical_hash_bucket_feature_column():
example = {'example': [['A'], ['B'], ['C'], ['D']]}
print(example['example'])
example_column = feature_column.categorical_column_with_hash_bucket(
'example', hash_bucket_size=7)
example_column_identy = feature_column.indicator_column(example_column)
example_transformed_tensor = feature_column.input_layer(
example, [example_column_identy])
with tf.Session() as session:
print('Result:')
print(session.run([example_transformed_tensor]))
def run_categorical_identity_feature_column():
example = {'example': [[1], [2], [8], [4]]}
print(example['example'])
example_column = feature_column.categorical_column_with_identity(
'example', num_buckets=10)
example_column_identity = feature_column.indicator_column(example_column)
example_transformed_tensor = feature_column.input_layer(
example, [example_column_identity])
with tf.Session() as session:
# session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
print('Result:')
print(session.run([example_transformed_tensor]))
def run_categorical_vocabulary_list_feature_column():
example = {'example': [['A'], ['B'], ['C'], ['D']]}
print(example['example'])
example_column = feature_column.categorical_column_with_vocabulary_list(
'example', vocabulary_list=['A', 'D'])
example_column_identy = feature_column.indicator_column(example_column)
example_transformed_tensor = feature_column.input_layer(
example, [example_column_identy])
with tf.Session() as session:
# session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
print('Result:')
print(session.run([example_transformed_tensor]))
def run_bucketized_feature_column():
price = {
'price': [[1., 1., 1., 1., 1., 1.], [2., 2., 2., 2., 2., 2.],
[3., 3., 3., 3., 3., 3.], [4., 4., 4., 4., 4., 4.]]
}
print(price['price'])
price_column = feature_column.numeric_column('price', shape=[6])
bucket_price = feature_column.bucketized_column(price_column,
[0, 2, 3.5, 5])
price_transformed_tensor = feature_column.input_layer(
price, [bucket_price])
with tf.Session() as session:
print('Result:')
print(session.run([price_transformed_tensor]))