Python tensorflow tf.train.string\u输入\u生产者和洗牌\u批量发行

没有num_纪元的代码运行良好

但添加num_epoch时会出错

OutOfRangeError (see above for traceback): RandomShuffleQueue '_1_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 2, current size 0)
[[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]

我一直在关注Tensorflow官方教程，无法完成num_时代

我想做的是，当它通过epoch_num时生成一个错误，这样我就不必通过计算我整个培训文件的实例来跟踪当前批次和最大批次，因为它非常大

知道为什么吗？我想我做错了什么

""" Some people tried to use TextLineReader for the assignment 1
but seem to have problems getting it work, so here is a short 
script demonstrating the use of CSV reader on the heart dataset.
Note that the heart dataset is originally in txt so I first
converted it to csv to take advantage of the already laid out columns.
You can download heart.csv in the data folder.
Author: Chip Huyen
Prepared for the class CS 20SI: "TensorFlow for Deep Learning Research"
cs20si.stanford.edu
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import sys
sys.path.append('..')

import tensorflow as tf

DATA_PATH = './heart.csv'
BATCH_SIZE = 2
N_FEATURES = 9

def batch_generator(filenames):
    """ filenames is the list of files you want to read from. 
    In this case, it contains only heart.csv
    """
    filename_queue = tf.train.string_input_producer(filenames, num_epochs=3)
    reader = tf.TextLineReader(skip_header_lines=1) # skip the first line in the file
    _, value = reader.read(filename_queue)

    # record_defaults are the default values in case some of our columns are empty
    # This is also to tell tensorflow the format of our data (the type of the decode result)
    # for this dataset, out of 9 feature columns, 
    # 8 of them are floats (some are integers, but to make our features homogenous, 
    # we consider them floats), and 1 is string (at position 5)
    # the last column corresponds to the label is an integer

    record_defaults = [[1.0] for _ in range(N_FEATURES)]
    record_defaults[4] = ['']
    record_defaults.append([1])

    # read in the 10 rows of data
    content = tf.decode_csv(value, record_defaults=record_defaults) 

    # convert the 5th column (present/absent) to the binary value 0 and 1
    content[4] = tf.cond(tf.equal(content[4], tf.constant('Present')), lambda: tf.constant(1.0), lambda: tf.constant(0.0))

    # pack all 9 features into a tensor
    features = tf.stack(content[:N_FEATURES])

    # assign the last column to label
    label = content[-1]

    # minimum number elements in the queue after a dequeue, used to ensure 
    # that the samples are sufficiently mixed
    # I think 10 times the BATCH_SIZE is sufficient
    min_after_dequeue = 10 * BATCH_SIZE

    # the maximum number of elements in the queue
    capacity = 20 * BATCH_SIZE

    # shuffle the data to generate BATCH_SIZE sample pairs
    data_batch, label_batch = tf.train.shuffle_batch([features, label], batch_size=BATCH_SIZE, 
                                        capacity=capacity, min_after_dequeue=min_after_dequeue)

    return data_batch, label_batch

def generate_batches(data_batch, label_batch):
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        for _ in range(400): # generate 400 batches
            features, labels = sess.run([data_batch, label_batch])
            print(features)
        coord.request_stop()
        coord.join(threads)

def main():
    data_batch, label_batch = batch_generator([DATA_PATH])
    generate_batches(data_batch, label_batch)

if __name__ == '__main__':
    main()

---

tf.train.string\u input\u producer（）

在其实现中使用“局部变量”，因此需要添加

sess.run(tf.local_variables_initializer()) 

…在启动队列运行程序之前

出于这样的可用性原因，我们现在鼓励TensorFlow用户使用API来构建输入管道。您的代码可以重写如下：

# Start with a dataset of filenames.
dataset = tf.data.Dataset.from_tensor_slices(filenames)

# Repeat the filenames for three epochs.
dataset = dataset.repeat(3)

# Use Dataset.flat_map() and tf.data.TextLineDataset to convert the
# filenames into a dataset of lines.
dataset = dataset.flat_map(
    lambda filename: tf.data.TextLineDataset(filename).skip(1))

# Wrap the per-line parsing logic in a function, and map it over the dataset.
def parse_line(value):
    record_defaults = [[1.0] for _ in range(N_FEATURES)]
    record_defaults[4] = ['']
    record_defaults.append([1])

    # read in the 10 rows of data
    content = tf.decode_csv(value, record_defaults=record_defaults) 

    # convert the 5th column (present/absent) to the binary value 0 and 1
    content[4] = tf.cond(tf.equal(content[4], tf.constant('Present')), lambda: tf.constant(1.0), lambda: tf.constant(0.0))

    # pack all 9 features into a tensor
    features = tf.stack(content[:N_FEATURES])

    # assign the last column to label
    label = content[-1]

    return features, label

dataset = dataset.map(parse_line)

# Shuffle the dataset.
dataset = dataset.shuffle(20 * BATCH_SIZE)

# Combine consecutive elements into batches.
dataset = dataset.batch(BATCH_SIZE)

# Create an iterator to get elements from the dataset.
iterator = dataset.make_one_shot_iterator()

# Get tensors that represent the next element of the iterator.
data_batch, label_batch = iterator.get_next()

# Finally, create a session to iterate over the batches.
with tf.Session() as sess:
    try:
        while True:
            features, labels = sess.run([data_batch, label_batch])
            print(features)
    except tf.errors.OutOfRangeError:
        # Raised when there are no more batches to produce.
        pass

---

num\u epoch是tf.train.string\u input\u producer（）中的参数，它解决了这个问题。顺便说一句，tensorflow新版本中添加了tf.data模块，所以使用旧版本的用户应该执行本地变量初始化