Question

我试图在TensorForestEstimator模型中使用表示7个特征和7个标签的数值浮点数据。也就是说，features和labels的形状都是(484876, 7)。我在num_classes=7中适当地设置了num_features=7和ForestHParams。数据格式如下：

f1       f2     f3    f4      f5    f6    f7   l1       l2       l3       l4       l5       l6       l7
39000.0  120.0  65.0  1000.0  25.0  0.69  3.94 39000.0  39959.0  42099.0  46153.0  49969.0  54127.0  55911.0
32000.0  185.0  65.0  1000.0  75.0  0.46  2.19 32000.0  37813.0  43074.0  48528.0  54273.0  60885.0  63810.0 
30000.0  185.0  65.0  1000.0  25.0  0.41  1.80 30000.0  32481.0  35409.0  39145.0  42750.0  46678.0  48595.0

当使用以下消息调用fit() Python崩溃时：

Python在使用_pywrap_tensorflow_internal.so插件时意外退出。

以下是启用tf.logging.set_verbosity('INFO')时的输出：

INFO:tensorflow:training graph for tree: 0
INFO:tensorflow:training graph for tree: 1
... 
INFO:tensorflow:training graph for tree: 9998
INFO:tensorflow:training graph for tree: 9999
INFO:tensorflow:Create CheckpointSaverHook.
2017-07-26 10:25:30.908894: F tensorflow/contrib/tensor_forest/kernels/count_extremely_random_stats_op.cc:404] 
Check failed: column < num_classes_ (39001 vs. 8)

Process finished with exit code 134 (interrupted by signal 6: SIGABRT)

我不确定这个错误意味着什么，它从num_classes=7开始没有意义，而不是8，因为功能和标签的形状是(484876, 7)，我不知道＃39;不知道39001的来源。

以下是重现的代码：

import numpy as np
import pandas as pd
import os

def get_training_data():
    training_file = "data.txt"
    data = pd.read_csv(training_file, sep='\t')

    X = np.array(data.drop('Result', axis=1), dtype=np.float32)

    y = []
    for e in data.ResultStr:
        y.append(list(np.array(str(e).replace('[', '').replace(']', '').split(','))))

    y = np.array(y, dtype=np.float32)

    features = tf.constant(X)
    labels = tf.constant(y)

    return features, labels

hyperparameters = ForestHParams(
    num_trees=100,
    max_nodes=10000,
    bagging_fraction=1.0,
    num_splits_to_consider=0,
    feature_bagging_fraction=1.0,
    max_fertile_nodes=0,
    split_after_samples=250,
    min_split_samples=5,
    valid_leaf_threshold=1,
    dominate_method='bootstrap',
    dominate_fraction=0.99,
    # All parameters above are default
    num_classes=7,
    num_features=7
)

estimator = TensorForestEstimator(
    params=hyperparameters,
    # All parameters below are default
    device_assigner=None,
    model_dir=None,
    graph_builder_class=RandomForestGraphs,
    config=None,
    weights_name=None,
    keys_name=None,
    feature_engineering_fn=None,
    early_stopping_rounds=100,
    num_trainers=1,
    trainer_id=0,
    report_feature_importances=False,
    local_eval=False
)

estimator.fit(
    input_fn=lambda: get_training_data(),
    max_steps=100,
    monitors=[
        TensorForestLossHook(
            early_stopping_rounds=30
        )
    ]
)

如果我用SKCompat包装它也不起作用，会发生同样的错误。造成这次事故的原因是什么？

Answer 1

regression=True需要在ForestHParams中指定，因为TensorForestEstimator默认情况下假定它用于解决分类问题，该问题只能输出一个值。

在估计器初始化时创建了隐式num_outputs变量，如果未指定1，则设置为regression。如果指定了regression，则num_outputs = num_classes和检查点会正常保存。

TensorFlow在安装TensorForestEstimator时崩溃

1 个答案: