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私は現在tensorFlowで遊んでいて、チュートリアルがちょっと単純であると思っていたとしても、自分のデータを入力しようとすると真の作業が始まります。テンソルフローとRandomShuffleQueue "不十分な要素（要求された64、現在のサイズ0）"
私は動物と背景の非常に基本的なデータセットの作曲家を使用しました。
私は3つのtfrecords（train/val/test）を作成しました。 私はそれらを読んで、シンプルなモデルを訓練しようとしました（ここのAlexnet）。 "FLAGS.num_iter"を使用して、私が反復範囲外に出ていないことを確認しようとしました。
このコード処理では、「RandomShuffleQueueが不十分です（要求された64、現在のサイズ0）」エラーが発生します。
私はウェブを掘り下げようとしましたが、私の質問には答えが見つかりませんでした。ここに彼らがあります：私たちはこれをどのように修正するのですか？ tfrecordに間違いがないかどうかをどうやって確認できますか？十分な要素があることを保証するための条件を書くことはできますか？ 私のコードにさらに質問がある場合は、私は周りにいます！
I/OのキューランナーのAPIを使用してよろしく、
import tensorflow as tf
import os.path
from model import Model
from alexnet import Alexnet
FLAGS = tf.app.flags.FLAGS
NUM_LABELS = 2
IMAGE_WIDTH = 64
IMAGE_HEIGHT = 64
NUMBER_OF_CHANNELS = 3
#SOURCE_DIR = './data/'
#TRAINING_IMAGES_DIR = SOURCE_DIR + 'train/'
#LIST_FILE_NAME = 'list.txt'
BATCH_SIZE = 2
#TRAINING_SET_SIZE = 81112
TRAIN_FILE = '/home/sebv/SebV/datas/tfRecording/train.tfrecords'
VAL_FILE = '/home/sebv/SebV/datas/tfRecording/val.tfrecor'
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image/encoded': tf.FixedLenFeature([], tf.string),
'image/format': tf.FixedLenFeature([], tf.string),
'image/class/label': tf.FixedLenFeature([], tf.int64),
'image/height': tf.FixedLenFeature([], tf.int64),
'image/width': tf.FixedLenFeature([], tf.int64),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.image.decode_png(features['image/encoded'], 3, tf.uint8)
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image = tf.cast(image, tf.float32)# * (1./255) - 0.5
image = tf.reshape(image, [IMAGE_WIDTH,IMAGE_HEIGHT,NUMBER_OF_CHANNELS])
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['image/class/label'], tf.int64)
return image, label
def inputs(train, filen, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
filename = filen
filename_queue = tf.train.string_input_producer([filename], num_epochs=num_epochs)
# Even when reading in multiple threads, share the filename
# queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch([image, label], batch_size=batch_size, num_threads=2,capacity=20000 + 3 * batch_size,min_after_dequeue=20000)
sparse_labels = tf.reshape(sparse_labels, [batch_size])
return images, sparse_labels
def train():
model = Alexnet()
with tf.Graph().as_default():
x = tf.placeholder(tf.float32, [None, IMAGE_WIDTH,IMAGE_HEIGHT,NUMBER_OF_CHANNELS], name='x-input')
y = tf.placeholder(tf.float32, [None], name='y-input')
images, labels = inputs(train=True, filen=TRAIN_FILE, batch_size=FLAGS.batch_size,num_epochs=FLAGS.num_iter)
images_val, labels_val = inputs(train=False, filen=VAL_FILE, batch_size=FLAGS.batch_size,num_epochs=1)
keep_prob = tf.placeholder(tf.float32, name='dropout_prob')
global_step = tf.contrib.framework.get_or_create_global_step()
logits = model.inference(images, keep_prob=keep_prob)
loss = model.loss(logits=logits, labels=labels)
accuracy = model.accuracy(logits, labels)
summary_op = tf.summary.merge_all()
train_op = model.train(loss, global_step=global_step)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
writer = tf.summary.FileWriter(FLAGS.summary_dir, sess.graph)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in xrange(FLAGS.num_iter):
_, cur_loss, summary = sess.run([train_op, loss, summary_op],
feed_dict={keep_prob: 0.5})
writer.add_summary(summary, i)
if i % 10 == 0:
batch_x = sess.run(images_val)
batch_y = sess.run(labels_val)
validation_accuracy = accuracy.eval(feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
print('Iter {} Accuracy: {}'.format(i, validation_accuracy))
saver.save(sess, FLAGS.checkpoint_file_path, global_step)
if i == FLAGS.num_iter:
coord.request_stop()
coord.join(threads)
def main(argv=None):
train()
if __name__ == '__main__':
tf.app.flags.DEFINE_integer('batch_size', 64, 'size of training batches')
tf.app.flags.DEFINE_integer('num_iter', 4001, 'number of training iterations') #10000
tf.app.flags.DEFINE_string('checkpoint_file_path', 'checkpoints/model.ckpt-10000', 'path to checkpoint file')
tf.app.flags.DEFINE_string('train_data', 'data', 'path to train and test data')
tf.app.flags.DEFINE_string('summary_dir', 'graphs', 'path to directory for storing summaries')
tf.app.run()