Delete test.py

tensnsorflow/test.py

-#coding=utf-8
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import pandas as pd
-import sys
-import os
-import glob
-
-import tensorflow as tf
-import numpy as np
-import re
-from multiprocessing import Pool as ThreadPool
-
-flags = tf.app.flags
-FLAGS = flags.FLAGS
-LOG = tf.logging
-
-tf.app.flags.DEFINE_string("input_dir", "./", "input dir")
-tf.app.flags.DEFINE_string("output_dir", "./", "output dir")
-tf.app.flags.DEFINE_integer("threads", 16, "threads num")
-
-def gen_tfrecords(in_file):
-    basename = os.path.basename(in_file) + ".tfrecord"
-    out_file = os.path.join(FLAGS.output_dir, basename)
-    tfrecord_out = tf.python_io.TFRecordWriter(out_file)
-    df = pd.read_csv(in_file)
-
-    for i in range(df.shape[0]):
-        feats = ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
-                "channel", "top", "l1", "time", "stat_date","l2"]
-        id = np.array([])
-        for j in feats:
-            id = np.append(id,df[j][i])
-        features = tf.train.Features(feature={
-            "y": tf.train.Feature(float_list=tf.train.FloatList(value=[df["y"][i]])),
-            "z": tf.train.Feature(float_list=tf.train.FloatList(value=[df["z"][i]])),
-            "ids": tf.train.Feature(int64_list=tf.train.Int64List(value=id.astype(np.int)))
-        })
-
-        example = tf.train.Example(features = features)
-        serialized = example.SerializeToString()
-        tfrecord_out.write(serialized)
-    tfrecord_out.close()
-
-def main(_):
-    if not os.path.exists(FLAGS.output_dir):
-        os.mkdir(FLAGS.output_dir)
-    file_list = glob.glob(os.path.join(FLAGS.input_dir, "*.csv"))
-    print("total files: %d" % len(file_list))
-
-    pool = ThreadPool(FLAGS.threads) # Sets the pool size
-    pool.map(gen_tfrecords, file_list)
-    pool.close()
-    pool.join()
-
-
-if __name__ == "__main__":
-    tf.logging.set_verbosity(tf.logging.INFO)
-    tf.app.run()
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