TensorFlow實戰除錯中所遇問題及解決方法2

程式原始碼：

# -*- encoding=utf-8 -*-

from numpy import genfromtxt
import numpy as np
import random
import sys
import csv
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn import preprocessing
reload(sys)
sys.setdefaultencoding("utf-8")
datapath = r"./bb.csv"
num_epochs1 = 10
rows = 100
turn = 2000
def read_data(file_queue):
#定義reader
reader = tf.textlinereader(skip_header_lines=1)
key, value = reader.read(file_queue)
#定義decoder
#defaults用於指定矩陣格式以及資料型別，csv檔案中的矩陣是m*n的，則此處為1*n的矩陣，比如矩陣中如果有小數，則為float,[1]應該變為[1.0]
defaults = [[''], ['null'], [''], [0.], [0.], [0.], [0.], [0], [""], [0], ['null'], [""]]
#矩陣中有幾列，有幾列需要寫幾個
city, origin, destination, origin_lat, origin_lng, destination_lat, destination_lng, \
distance, weature, duration, week_time, create_time = tf.decode_csv(records=value, record_defaults=defaults)
#return tf.stack([sepallengthcm, sepalwidthcm, petallengthcm, petalwidthcm]), preprocess_op
return distance, duration
def batch_input(filename, num_epochs):
#生成乙個先入先出佇列和乙個queuerunner
file_queue = tf.train.string_input_producer(string_tensor=[filename], num_epochs=10)
example, label = read_data(file_queue)
min_after_dequeue = 100
batch_size = 10
capacity = min_after_dequeue+3*batch_size
example_batch, label_batch = tf.train.shuffle_batch([example, label], batch_size=batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch
#examplebatch1, labelbatch1 = batch_input(datapath, num_epochs=100)
with tf.session() as sess:
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
examplebatch1, labelbatch1 = batch_input(datapath, num_epochs=100)
coord = tf.train.coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
example_batch, label_batch = sess.run([examplebatch1, labelbatch1])
print("example_batch is:")
print(example_batch)
except tf.errors.outofrangeerror: 
print('done training -- epoch limit reached') 
finally:
coord.request_stop()
coord.join(threads)

以上程式完成的功能是批量獲得csv檔案中的資料，但在執行時出現以下錯誤：

file "better_nonlinear_one_input_batch1.py", line 64, in

coord.join(threads)

file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/coordinator.py", line 389, in join

six.reraise(*self._exc_info_to_raise)

file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/queue_runner_impl.py", line 238, in _run

enqueue_callable()

file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1231, in _single_operation_run

target_list_as_strings, status, none)

file "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__

c_api.tf_getcode(self.status.status))

tensorflow.python.framework.errors_impl.failedpreconditionerror: attempting to use uninitialized value input_producer/limit_epochs/epochs

[[node: input_producer/limit_epochs/countupto = countupto[t=dt_int64, _class=["loc:@input_producer/limit_epochs/epochs"], limit=10, _device="/job:localhost/replica:0/task:0/device:cpu:0"](input_producer/limit_epochs/epochs)]]

上網查詢類似錯誤，得到的結果都說要加入初始化，即以下兩行：

init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

sess.run(init_op)

但實際上**中已經加入了這2行**，問題依然存在。一時一頭霧水，懷疑難道是例程都存在問題？決定一點一點查詢原因，在這裡要感謝

examplebatch1, labelbatch1 = batch_input(datapath, num_epochs=100)，

其位置很關鍵，將上邊貼的源程式中

examplebatch1, labelbatch1 = batch_input(datapath, num_epochs=100)這句話放到

with tf.session() as sess:

之上，就不會報錯（參見上述源**中兩個examplebatch1, labelbatch1 = batch_input(datapath, num_epochs=100)的位置），我最初是放到了裡邊，這樣就產生了所遇到的詭異問題。

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