Tensorflow -如何操作保护程序

Question

我正在使用tensorflow的波士顿住房数据教程，但我正在插入我自己的数据集：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)

COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
       "dis", "tax", "ptratio", "medv"]
FEATURES = ["crim", "zn", "indus", "nox", "rm",
        "age", "dis", "tax", "ptratio"]
LABEL = "medv"


def input_fn(data_set):
    feature_cols = {k: tf.constant(data_set[k].values) for k in FEATURES}
    labels = tf.constant(data_set[LABEL].values)
    return feature_cols, labels

def main(unused_argv):
    # Load datasets
    training_set = pd.read_csv("boston_train.csv", skipinitialspace=True,
                         skiprows=1, names=COLUMNS)
    test_set = pd.read_csv("boston_test.csv", skipinitialspace=True,
                     skiprows=1, names=COLUMNS)

    # Set of 6 examples for which to predict median house values
    prediction_set = pd.read_csv("boston_predict.csv",             skipinitialspace=True,
                           skiprows=1, names=COLUMNS)

    # Feature cols
    feature_cols = [tf.contrib.layers.real_valued_column(k)
              for k in FEATURES]

    # Build 2 layer fully connected DNN with 10, 10 units respectively.
    regressor = tf.contrib.learn.DNNRegressor(
    feature_columns=feature_cols, hidden_units=[10, 10])

    # Fit
    regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)

    # Score accuracy
    ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
    loss_score = ev["loss"]
    print("Loss: {0:f}".format(loss_score))

    # Print out predictions
    y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
    print("Predictions: {}".format(str(y)))

if __name__ == "__main__":
    tf.app.run()

我遇到的问题是数据集太大了，所以通过tf.train.Saver()保存检查点文件就填满了我所有的磁盘空间。

是否有一种方法可以禁用检查点文件的保存，或者减少上面脚本中保存的检查点的数量？

谢谢

Answer 1

tf.contrib.learn.DNNRegressor初始化程序接受一个tf.contrib.learn.RunConfig对象，该对象可用于控制内部创建的保护程序的行为。例如，您可以执行以下操作，只保留一个检查点：

config = tf.contrib.learn.RunConfig(keep_checkpoint_max=1)
regressor = tf.contrib.learn.DNNRegressor(
    feature_columns=feature_cols, hidden_units=[10, 10], config=config)