主要思想是将TFRecords转换为numpy数组。假设TFRecord存储图像。具体做法是:
1.jpg 2 2.jpg 4 3.jpg 5
我目前使用以下代码:
import tensorflow as tf
import os
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_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'depth': tf.FixedLenFeature([], tf.int64)
})
image = tf.decode_raw(features['image_raw'], tf.uint8)
label = tf.cast(features['label'], tf.int32)
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
depth = tf.cast(features['depth'], tf.int32)
return image, label, height, width, depth
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer(["../data/svhn/svhn_train.tfrecords"])
image, label, height, width, depth = read_and_decode(filename_queue)
image = tf.reshape(image, tf.pack([height, width, 3]))
image.set_shape([32,32,3])
init_op = tf.initialize_all_variables()
sess.run(init_op)
print (image.eval())
我正在阅读尝试为初学者获取至少一张图片。当我运行时,代码就会卡住。
答案 0 :(得分:17)
with tf.Session() as sess:
filename_queue = tf.train.string_input_producer(["../data/svhn/svhn_train.tfrecords"])
image, label, height, width, depth = read_and_decode(filename_queue)
image = tf.reshape(image, tf.pack([height, width, 3]))
image.set_shape([32,32,3])
init_op = tf.initialize_all_variables()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(1000):
example, l = sess.run([image, label])
print (example,l)
coord.request_stop()
coord.join(threads)