1，

model_l1 = tf.estimator.LinearClassifier(

feature_columns=base_columns + crossed_columns,

optimizer=tf.train.FtrlOptimizer(

learning_rate=0.1,

l1_regularization_strength=10.0,

l2_regularization_strength=0.0))

model_l1.train(train_inpf)

results = model_l1.evaluate(test_inpf)

clear_output()

for key in sorted(results):

print('%s: %0.2f' % (key, results[key]))

 

2，

model_l2 = tf.estimator.LinearClassifier(

feature_columns=base_columns + crossed_columns,

optimizer=tf.train.FtrlOptimizer(

learning_rate=0.1,

l1_regularization_strength=0.0,

l2_regularization_strength=10.0))

model_l2.train(train_inpf)

results = model_l2.evaluate(test_inpf)

clear_output()

for key in sorted(results):

print('%s: %0.2f' % (key, results[key]))