FIFOQueue'_1_batch / fifo_queue'已关闭且元素不足（请求20，当前大小为0）

时间：2017-10-12 04:03:22

标签： python python-3.x tensorflow

我的代码在这里。我正在使用python 3.6.2。每个文件夹都有100个图像，例如negativa_peaton_1，negativa_peaton_2，无文件夹情况下为negativa_peaton_100。

import tensorflow as tf
import numpy
import numpy as np
import math
from PIL import Image
from six.moves import xrange


# config
learning_rate = 0.01
training_epochs = 100
num_examples = 1000
num_train = int(0.8*num_examples)
num_test = int(0.2*num_examples)

IMAGE_WIDTH  = 40
IMAGE_HEIGHT = 80
IMAGE_DEPTH  = 1
IMAGE_PIXELS = IMAGE_WIDTH * IMAGE_HEIGHT
NUM_CLASSES  = 2
BATCH_SIZE    = 20

# function to read image names
def read_my_list( minId, maxId, folder ):


    filenames = []
    labels    = []
    for num in range( minId, maxId+1 ):

        filenames.append( "/Users/RetailAdmin/Documents/Inteligencia Artificial/Python/Ejemplo" + folder + "/si/" + name_si( num ) + ".jpg" )
        labels.append( int( 1 ) )

        filenames.append( "/Users/RetailAdmin/Documents/Inteligencia Artificial/Python/Ejemplo" + folder + "/no/" + name_no( num ) + ".jpg" )
        labels.append( int( 0 ) )

        print( num_name(num) )

    # return list with all filenames
    print( "number of labels: " + str( len( labels ) ) )
    print( "number of images: " + str( len( filenames ) ) )
    return filenames, labels

def num_name( id ):



    ret = str( id )
    while ( len( ret ) < 5 ):
        ret = "0" + ret;

    return ret;

def name_si( id ):

    ret = str( id )
    ret = "peaton_" + ret;

    return ret;

def name_no( id ):

    ret = str( id )
    ret = "negativa_peaton_" + ret;

    return ret;

# read and prepare images
def read_images_from_disk(input_queue):

    label = input_queue[1]
    print( "read file "  )
    file_contents = tf.read_file(input_queue[0])
    example = tf.image.decode_jpeg( file_contents, channels = 1 )
    print(example)
    example = tf.image.resize_images(example,[IMAGE_HEIGHT, IMAGE_WIDTH])
    print(example)
    example = tf.reshape( example, [ IMAGE_PIXELS ] )
    print(example)
    example.set_shape( [ IMAGE_PIXELS ] )
    print(example)

    example = tf.cast( example, tf.float32 )
    example = tf.cast( example, tf.float32 ) * ( 1. / 255 ) - 0.5

    label = tf.cast( label, tf.int64 )

    label = tf.one_hot( label, 2, 0, 1 )
    label = tf.cast( label, tf.float32 )

    print( "file read " )
    return  example, label

def fill_feed_dict(image_batch, label_batch, imgs, lbls):
  feed_dict = {
      imgs: image_batch,
      lbls: label_batch,
  }
  return feed_dict

# input images
# None -> batch size can be any size, IMAGE_PIXELS -> image size
x = tf.placeholder(tf.float32, shape=[None, IMAGE_PIXELS], name="x-input")
# target 2 output classes
y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES], name="y-input")

# model parameters will change during training so we use tf.Variable
W = tf.Variable(tf.zeros([IMAGE_PIXELS, NUM_CLASSES]))

# bias
b = tf.Variable(tf.zeros([NUM_CLASSES]))

# implement model
# y is our prediction
y = tf.nn.softmax(tf.matmul(x,W) + b)

# specify cost function
# this is our cost --> y is the net output, y_ is the target
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

# Accuracy --> y is the net output, y_ is the target
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# specify optimizer
# optimizer is an "operation" which we can execute in a session
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

# DATA FOR TRAINING
# get filelist and labels for training (num_train/2 examples of each class)
image_list, label_list = read_my_list( 1, int(num_train/2), "train" )

# create queue for training
input_queue = tf.train.slice_input_producer( [ image_list, label_list ])

# read files for training
image, label = read_images_from_disk( input_queue )

# `image_batch` and `label_batch` represent the "next" batch
# read from the input queue.
image_batch, label_batch = tf.train.batch( [ image, label ], batch_size = BATCH_SIZE )

# DATA FOR TESTING
# get filelist and labels for tESTING
image_list_test, label_list_test = read_my_list( int(num_train/2)+1, int(num_examples/2), "train" )

# create queue for training
input_queue_test = tf.train.slice_input_producer( [ image_list_test, label_list_test ])

# read files for training
image_test, label_test = read_images_from_disk( input_queue_test )

# read from the input queue.
image_batch_test, label_batch_test = tf.train.batch( [ image_test, label_test ], batch_size = num_test )

with tf.Session() as sess:
    # variables need to be initialized before we can use them
    sess.run(tf.local_variables_initializer())

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    # perform training cycles
    for epoch in range(training_epochs):

        # number of batches in one epoch
        batch_count = int(num_train/BATCH_SIZE)

        for i in range(batch_count):

            imgs, lbls = sess.run([image_batch, label_batch])

            sess.run([train_op], feed_dict={x:imgs, y_:lbls})

        print("Epoch: ", epoch)
        imgs_test, lbls_test = sess.run([image_batch_test, label_batch_test])
        print ("Accuracy: ", accuracy.eval(feed_dict={x: imgs_test , y_: lbls_test}))
    print ("done")
    coord.request_stop()
    coord.join(threads)

我得到了这个问题

2017-10-12 00：25：19.457738：WC：\ tf_jenkins \ home \ workspace \ rel-win \ M \ windows \ PY \ 36 \ tensorflow \ core \ platform \ cpu_feature_guard.cc：45] TensorFlow库未编译为使用AVX指令，但这些指令可在您的计算机上使用，并可加速CPU计算。 2017-10-12 00：25：19.457845：WC：\ tf_jenkins \ home \ workspace \ rel-win \ M \ windows \ PY \ 36 \ tensorflow \ core \ platform \ cpu_feature_guard.cc：45] TensorFlow库不是编译为使用AVX2指令，但这些指令可在您的机器上使用，并可加速CPU计算。 8 2017-10-12 00：25：19.806878：WC：\ tf_jenkins \ home \ workspace \ rel-win \ M \ windows \ PY \ 36 \ tensorflow \ core \ kernels \ queue_base.cc：295] _3_batch_1 / fifo_queue：跳过取消队列未关闭的队列排队 2017-10-12 00：25：19.807235：WC：\ tf_jenkins \ home \ workspace \ rel-win \ M \ windows \ PY \ 36 \ tensorflow \ core \ kernels \ queue_base.cc：295] _2_input_producer_1 / input_producer：跳过已取消队列未关闭的队列排队 2017-10-12 00：25：19.811144：WC：\ tf_jenkins \ home \ workspace \ rel-win \ M \ windows \ PY \ 36 \ tensorflow \ core \ kernels \ queue_base.cc：295] _0_input_producer / input_producer：跳过已取消队列未关闭的队列排队 Traceback（最近一次调用最后一次）：   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”，第1327行，在_do_call中     return fn（* args）   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”，第1306行，在_run_fn中     status，run_metadata）   文件“C：\ Program Files \ Python36 \ lib \ contextlib.py”，第88行，退出     下一个（self.gen）   在raise_exception_on_not_ok_status中输入文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ framework \ errors_impl.py”，第466行     pywrap_tensorflow.TF_GetCode（状态）） tensorflow.python.framework.errors_impl.FailedPreconditionError：尝试使用未初始化的值Variable_1          [[Node：Variable_1 / read = IdentityT = DT_FLOAT，_ class = [“loc：@ Variable_1”]，_ device =“/ job：localhost / replica：0 / task：0 / cpu：0”]]

在处理上述异常期间，发生了另一个异常：

追踪（最近一次通话）：   文件“NeuralNet_L1.py”，第176行，in     sess.run（[train_op]，feed_dict = {x：imgs，y_：lbls}）   运行文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”，第895行     run_metadata_ptr）   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”，第1124行，在_run中     feed_dict_tensor，options，run_metadata）   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”，第1321行，在_do_run中     选项，run_metadata）   在_do_call中输入文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ client \ session.py”，第1340行     提升类型（e）（node_def，op，message） tensorflow.python.framework.errors_impl.FailedPreconditionError：尝试使用未初始化的值Variable_1          [[Node：Variable_1 / read = IdentityT = DT_FLOAT，_ class = [“loc：@ Variable_1”]，_ device =“/ job：localhost / replica：0 / task：0 / cpu：0”]]

由op'Variable_1 / read'引起，定义于：   文件“NeuralNet_L1.py”，第113行，in     b = tf.Variable（tf.zeros（[NUM_CLASSES]））   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ ops \ variables.py”，第199行， init     expected_shape = expected_shape）   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ ops \ variables.py”，第330行，在_init_from_args中     self._snapshot = array_ops.identity（self._variable，name =“read”）   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ ops \ gen_array_ops.py”，第1400行，标识     result = _op_def_lib.apply_op（“Identity”，input = input，name = name）   在apply_op中的文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ framework \ op_def_library.py”，第767行     op_def = op_def）   在create_op中的文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ framework \ ops.py”，第2630行     original_op = self._default_original_op，op_def = op_def）   文件“C：\ Program Files \ Python36 \ lib \ site-packages \ tensorflow \ python \ framework \ ops.py”，第1204行， init     self._traceback = self._graph._extract_stack（）#pylint：disable = protected-access

FailedPreconditionError（参见上面的回溯）：尝试使用未初始化的值Variable_1          [[Node：Variable_1 / read = IdentityT = DT_FLOAT，_ class = [“loc：@ Variable_1”]，_ device =“/ job：localhost / replica：0 / task：0 / cpu：0”]]

1 个答案:

答案 0 :(得分：0)

错误消息表明问题的原因：

  

2017-10-11 22：52：23.533465：WC：\ tf_jenkins \ home \ workspace \ rel-win \ M \ windows \ PY \ 36 \ tensorflow \ core \ framework \ op_kernel.cc：1192]未找到： NewRandomAccessFile无法创建/打开：/ Users / RetailAdmin / Documents / Inteligencia Artificial / Python / Ejemplotrain / si / peaton_457.jpg：El sistema no puede encontrar la ruta especificada。

没有名为"/Users/RetailAdmin/Documents/Inteligencia Artificial/Python/Ejemplotrain/si/peaton_457.jpg"的文件。猜测一下，我会说路径构造不正确，应该是"/Users/RetailAdmin/Documents/Inteligencia Artificial/Python/Ejemplo/train/si/peaton_457.jpg"（/和Ejemplo之间train。

要避免此类问题，请使用os.path.join()代替字符串连接来构建路径：

for num in range( minId, maxId+1 ):

    filenames.append(os.path.join(
        "/Users/RetailAdmin/Documents/Inteligencia Artificial/Python/Ejemplo",
        folder, "si", name_si(num) + ".jpg")
    labels.append(int(1))

    filenames.append(
        "/Users/RetailAdmin/Documents/Inteligencia Artificial/Python/Ejemplo",
        folder, "no", name_no( num ) + ".jpg")
    labels.append(int(0))

    print(num_name(num))
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