我在张量流中从tfrecords文件读取FIFO队列。每个记录由图像及其注释组成，即一组特征。根据一些功能，我试图跳过一些图像，而不是将它们输入到图形中，或者不查看它们。因此，我认为最好的情况是在while循环中使用。该循环将测试指定功能的值并决定是否继续。
请查看以下代码：
render最后，当尝试运行以下代码时，似乎主体没有执行，因此陷入无限循环。而且身体中的import tensorflow as tf
import numpy as np
num_epoch = 100
tfrecords_filename_seq = ["C:/Users/user/PycharmProjects/AffectiveComputing/P16_db.tfrecords"]
filename_queue = tf.train.string_input_producer(tfrecords_filename_seq, num_epochs=num_epoch, shuffle=False, name='queue')
reader = tf.TFRecordReader()
current_image_confidence = tf.constant(0.0, dtype=tf.float32)
def body(i):
key, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
# This is how we create one example, that is, extract one example from the database.
image = tf.decode_raw(features['image_raw'], tf.uint8)
# The height and the weights are used to
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
# The image is reshaped since when stored as a binary format, it is flattened. Therefore, we need the
# height and the weight to restore the original image back.
image = tf.reshape(image, [height, width, 3])
annotation = tf.cast(features['annotation_raw'], tf.string)
t1 = tf.string_split([annotation], delimiter=',')
t2 = tf.reshape(t1.values, [1, -1])
t3 = tf.string_to_number(t2, out_type=tf.float32)
t_ = tf.slice(t3, begin=[0, 3], size=[1, 1])
# Note that t_ is holding a value of 1.0 or 0.0. So its a particular feature I'm interested in.
t_ = tf.Print(t_, data=[tf.shape(t_)], message='....')
z = tf.cond(t_[0][0] < 1.0, lambda: tf.add(i, 0.0), lambda: tf.add(i, 1.0))
return z
cond = lambda i: tf.equal(i, tf.constant(0.0, dtype=tf.float32))
loop = tf.while_loop(cond, body, [current_image_confidence])
init_op = tf.group(tf.local_variables_initializer(),
tf.global_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
sess.run(loop)
没有被执行。
为什么会这样？
非常感谢任何帮助!!
答案 0 :(得分：0)
你的程序越来越难，因为你没有启动队列跑步者。在运行循环操作之前运行tf.start_queue_runners()。