學習目標：
- 計算模型大小
- 通過應用 L1 正則化來增加稀疏性，以減小模型大小
降低複雜性的一種方法是使用正則化函數，它會使權重正好爲零。對於線性模型（例如線性迴歸），權重爲零就相當於完全沒有使用相應特徵。除了可避免過擬合之外，生成的模型還會更加有效。
L1 正則化是一種增加稀疏性的好方法。
from __future__ import print_function
import math
from IPython import display
from matplotlib import cm
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset
tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
california_housing_dataframe = pd.read_csv("https://download.mlcc.google.cn/mledu-datasets/california_housing_train.csv", sep=",")
california_housing_dataframe = california_housing_dataframe.reindex(
np.random.permutation(california_housing_dataframe.index))
def preprocess_features(california_housing_dataframe):
selected_features = california_housing_dataframe[
["latitude",
"longitude",
"housing_median_age",
"total_rooms",
"total_bedrooms",
"population",
"households",
"median_income"]]
processed_features = selected_features.copy()
# Create a synthetic feature.
processed_features["rooms_per_person"] = (
california_housing_dataframe["total_rooms"] /
california_housing_dataframe["population"])
return processed_features
def preprocess_targets(california_housing_dataframe):
output_targets = pd.DataFrame()
# Create a boolean categorical feature representing whether the
# median_house_value is above a set threshold.
output_targets["median_house_value_is_high"] = (
california_housing_dataframe["median_house_value"] > 265000).astype(float)
return output_targets
training_examples = preprocess_features(california_housing_dataframe.head(12000))
training_targets = preprocess_targets(california_housing_dataframe.head(12000))
# Choose the last 5000 (out of 17000) examples for validation.
validation_examples = preprocess_features(california_housing_dataframe.tail(5000))
validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))
# Double-check that we've done the right thing.
print("Training examples summary:")
display.display(training_examples.describe())
print("Validation examples summary:")
display.display(validation_examples.describe())
print("Training targets summary:")
display.display(training_targets.describe())
print("Validation targets summary:")
display.display(validation_targets.describe())
def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
# Convert pandas data into a dict of np arrays.
features = {key:np.array(value) for key,value in dict(features).items()}
# Construct a dataset, and configure batching/repeating.
ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
ds = ds.batch(batch_size).repeat(num_epochs)
# Shuffle the data, if specified.
if shuffle:
ds = ds.shuffle(10000)
# Return the next batch of data.
features, labels = ds.make_one_shot_iterator().get_next()
return features, labels
def get_quantile_based_buckets(feature_values, num_buckets):
quantiles = feature_values.quantile(
[(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])
return [quantiles[q] for q in quantiles.keys()]
def construct_feature_columns():
bucketized_households = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("households"),
boundaries=get_quantile_based_buckets(training_examples["households"], 10))
bucketized_longitude = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("longitude"),
boundaries=get_quantile_based_buckets(training_examples["longitude"], 50))
bucketized_latitude = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("latitude"),
boundaries=get_quantile_based_buckets(training_examples["latitude"], 50))
bucketized_housing_median_age = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("housing_median_age"),
boundaries=get_quantile_based_buckets(
training_examples["housing_median_age"], 10))
bucketized_total_rooms = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("total_rooms"),
boundaries=get_quantile_based_buckets(training_examples["total_rooms"], 10))
bucketized_total_bedrooms = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("total_bedrooms"),
boundaries=get_quantile_based_buckets(training_examples["total_bedrooms"], 10))
bucketized_population = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("population"),
boundaries=get_quantile_based_buckets(training_examples["population"], 10))
bucketized_median_income = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("median_income"),
boundaries=get_quantile_based_buckets(training_examples["median_income"], 10))
bucketized_rooms_per_person = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("rooms_per_person"),
boundaries=get_quantile_based_buckets(
training_examples["rooms_per_person"], 10))
long_x_lat = tf.feature_column.crossed_column(
set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)
feature_columns = set([
long_x_lat,
bucketized_longitude,
bucketized_latitude,
bucketized_housing_median_age,
bucketized_total_rooms,
bucketized_total_bedrooms,
bucketized_population,
bucketized_households,
bucketized_median_income,
bucketized_rooms_per_person])
return feature_columns
def model_size(estimator):
variables=estimator.get_variable_names()
size=0
for variable in variables:
if not any(x in variable
for x in ['global_step',
'centered_bias_weight',
'bias_weight',
'Ftrl']):
size+=np.count_nonzero(estimator.get_variable_names(variable))
return size
def train_linear_classifier_model(
learning_rate,
regularization_strength,
steps,
batch_size,
feature_columns,
training_examples,
training_targets,
validation_examples,
validation_targets):
periods = 7
steps_per_period = steps / periods
# Create a linear classifier object.
my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)
my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
linear_classifier = tf.estimator.LinearClassifier(
feature_columns=feature_columns,
optimizer=my_optimizer
)
# Create input functions.
training_input_fn = lambda: my_input_fn(training_examples,
training_targets["median_house_value_is_high"],
batch_size=batch_size)
predict_training_input_fn = lambda: my_input_fn(training_examples,
training_targets["median_house_value_is_high"],
num_epochs=1,
shuffle=False)
predict_validation_input_fn = lambda: my_input_fn(validation_examples,
validation_targets["median_house_value_is_high"],
num_epochs=1,
shuffle=False)
# Train the model, but do so inside a loop so that we can periodically assess
# loss metrics.
print("Training model...")
print("LogLoss (on validation data):")
training_log_losses = []
validation_log_losses = []
for period in range (0, periods):
# Train the model, starting from the prior state.
linear_classifier.train(
input_fn=training_input_fn,
steps=steps_per_period
)
# Take a break and compute predictions.
training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)
training_probabilities = np.array([item['probabilities'] for item in training_probabilities])
validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)
validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])
# Compute training and validation loss.
training_log_loss = metrics.log_loss(training_targets, training_probabilities)
validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)
# Occasionally print the current loss.
print(" period %02d : %0.2f" % (period, validation_log_loss))
# Add the loss metrics from this period to our list.
training_log_losses.append(training_log_loss)
validation_log_losses.append(validation_log_loss)
print("Model training finished.")
# Output a graph of loss metrics over periods.
plt.ylabel("LogLoss")
plt.xlabel("Periods")
plt.title("LogLoss vs. Periods")
plt.tight_layout()
plt.plot(training_log_losses, label="training")
plt.plot(validation_log_losses, label="validation")
plt.legend()
return linear_classifier
linear_classifier = train_linear_classifier_model(
learning_rate=0.1,
# TWEAK THE REGULARIZATION VALUE BELOW
regularization_strength=1.0,
steps=300,
batch_size=100,
feature_columns=construct_feature_columns(),
training_examples=training_examples,
training_targets=training_targets,
validation_examples=validation_examples,
validation_targets=validation_targets)
print("Model size:", model_size(linear_classifier))
