model_dir=None, # No save directory specified
learner_config=learner_config,
n_classes=num_classes,
examples_per_layer=examples_per_layer,
num_trees=num_trees,
center_bias=False,
config=run_config)
# Display TF info logs
tf.logging.set_verbosity(tf.logging.INFO)
# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(x={'images': mnist.train.images},
y=mnist.train.labels,
batch_size=batch_size,
num_epochs=None,
shuffle=True)
# Train the Model
gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)
# Evaluate the Model
# Define the input function for evaluating
input_fn = tf.estimator.inputs.numpy_input_fn(x={'images': mnist.test.images},
y=mnist.test.labels,
batch_size=batch_size,
shuffle=False)
# Use the Estimator 'evaluate' method
e = gbdt_model.evaluate(input_fn=input_fn)
print("Testing Accuracy:", e['accuracy'])
def test2():
import tensorflow as tf
from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier
from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner
# Ignore all GPUs (current TF GBDT does not support GPU).
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# 设置日志级别
tf.logging.set_verbosity(tf.logging.ERROR)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(
"MNIST_data",
one_hot=False,
source_url='http://yann.lecun.com/exdb/mnist/')
# 参数
batch_size = 4096 # 批次大小
num_classes = 10 # 标签数
num_features = 784 # 特征数
max_steps = 10000 # 最大步数
# GBDT Parameters
learning_rate = 0.1
l1_regul = 0.
l2_regul = 1.
examples_per_layer = 1000
num_trees = 10
max_depth = 16
# 设置参数
learner_config = gbdt_learner.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate
learner_config.regularization.l1 = l1_regul
learner_config.regularization.l2 = l2_regul / examples_per_layer
learner_config.constraints.max_tree_depth = max_depth
growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER
learner_config.growing_mode = growing_mode
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
learner_config.multi_class_strategy = (
gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)
# 创建模型
gbdt_model = GradientBoostedDecisionTreeClassifier(
model_dir=None, # No save directory specified
learner_config=learner_config,
n_classes=num_classes,
examples_per_layer=examples_per_layer,
num_trees=num_trees,
center_bias=False,
config=run_config)
# Display TF info logs
tf.logging.set_verbosity(tf.logging.INFO)
# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.train.images},
y=mnist.train.labels,
batch_size=batch_size,
num_epochs=None,
shuffle=True)
# 训练
gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)
# 预测
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.test.images},
y=mnist.test.labels,
batch_size=batch_size,
shuffle=False)
e = gbdt_model.evaluate(input_fn=input_fn)
print("Testing Accuracy:", e['accuracy'])
# Create a TensorFlor GBDT Estimator
gbdt_model = GradientBoostedDecisionTreeClassifier(
model_dir=None, # No save directory specified
learner_config=learner_config,
n_classes=num_classes,
examples_per_layer=examples_per_layer,
num_trees=num_trees,
center_bias=False,
config=run_config)
# Display TF info logs
tf.logging.set_verbosity(tf.logging.INFO)
# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.train.images}, y=mnist.train.labels,
batch_size=batch_size, num_epochs=None, shuffle=True)
# Train the Model
gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)
# Evaluate the Model
# Define the input function for evaluating
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.test.images}, y=mnist.test.labels,
batch_size=batch_size, shuffle=False)
# Use the Estimator 'evaluate' method
e = gbdt_model.evaluate(input_fn=input_fn)
print("Testing Accuracy:", e['accuracy'])
# Create a TensorFlor GBDT Estimator
gbdt_model = GradientBoostedDecisionTreeClassifier(
model_dir=None, # No save directory specified
learner_config=learner_config,
n_classes=num_classes,
examples_per_layer=examples_per_layer,
num_trees=num_trees,
center_bias=False,
config=run_config)
# Display TF info logs
tf.logging.set_verbosity(tf.logging.INFO)
# Define the input function for training
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.train.images}, y=mnist.train.labels,
batch_size=batch_size, num_epochs=None, shuffle=True)
# Train the Model
gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)
# Evaluate the Model
# Define the input function for evaluating
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.test.images}, y=mnist.test.labels,
batch_size=batch_size, shuffle=False)
# Use the Estimator 'evaluate' method
e = gbdt_model.evaluate(input_fn=test_input_fn)
print("Testing Accuracy:", e['accuracy'])
def run():
# 忽略所有的 GPU, 当前的 TF GBDT 不支持 GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# 设置 Tensorflow 的 log 只显示 error
tf.logging.set_verbosity(tf.logging.ERROR)
# 导入 MNIST 数据集
mnist = input_data.read_data_sets(
"/tmp/data/",
one_hot=False,
source_url='http://yann.lecun.com/exdb/mnist/')
# 超参数设置
batch_size = 4096 # 每批用来训练的数据的大小
num_classes = 10 # 最终结果一共有基类
num_features = 784 # 输入数据的特征数——每张图片的大小是 28 * 28 像素
max_steps = 10000
# GBDT 超参数
learning_rate = 0.1 # 学习率
l1_regul = 0. # L1 正则化
l2_regul = 1. # L2 正则化
examples_per_layer = 1000
num_trees = 10 # 树的数量
max_depth = 16 # 最大深度
# 设置 GBDT 的超参数
learner_config = gbdt_learner.LearnerConfig()
learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate
learner_config.regularization.l1 = l1_regul
learner_config.regularization.l2 = l2_regul / examples_per_layer
learner_config.constraints.max_tree_depth = max_depth
growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER
learner_config.growing_mode = growing_mode
run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)
learner_config.multi_class_strategy = (
gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)
# 创建TensorFlor GBDT 模型
gbdt_model = GradientBoostedDecisionTreeClassifier(
model_dir=None, # 不指定保存位置
learner_config=learner_config,
n_classes=num_classes,
examples_per_layer=examples_per_layer,
num_trees=num_trees,
center_bias=False,
config=run_config)
# 展示 log 的 info 信息
tf.logging.set_verbosity(tf.logging.INFO)
# 定义用来训练的输入数据
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.train.images},
y=mnist.train.labels,
batch_size=batch_size,
num_epochs=None,
shuffle=True)
# 拟合模型
gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)
# 评估拟合后的模型
# 定义用于评估的输入
input_fn = tf.estimator.inputs.numpy_input_fn(
x={'images': mnist.test.images},
y=mnist.test.labels,
batch_size=batch_size,
shuffle=False)
# 使用 评估 方法来进行评估
e = gbdt_model.evaluate(input_fn=input_fn)
print("测试准确度为:", e['accuracy'])
return