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