如果把文件全部加载到内存中，对大数据量来说，是不可行的，tensorflow使用列队，通过多线程来操作队列进出。举例子来说明>
tf.train.slice_input_producer是一个tensor生成器，作用是按照设定，每次从一个tensor列表中按顺序或者随机抽取出一个tensor放入文件名队列。
下面这个例子是将文件名加入到队列中，每次从列队中只能取出一个tensor，然后读取图片数据，还是频繁io操作，
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
def get_image(image_path):
content = tf.read_file(image_path)
tf_image = tf.image.decode_jpeg(content, channels=3)
return tf_image
def plot_pic(batch_img_one_val, batch_img_two_val, label):
fig = plt.figure(figsize=(6,2))
plt.suptitle(label)
ax1 = fig.add_subplot(1,2,1)
#ax1.set_title(label)
ax1.imshow(batch_img_one_val)
ax1.axis('off')
ax2 = fig.add_subplot(1,2,2)
ax2.imshow(batch_img_two_val)
ax2.axis('off')
plt.show()
def slice_input_producer_one_sample():
# 重置graph
tf.reset_default_graph()
batch_size = 1
images_one_path_list = ['lda.png', 'snapshot.png','hua.jpeg']
images_two_path_list = ['tuzi.jpg', 'test.png', 'hua.jpeg']
label_list = ['lad_tuzi', 'snap_test', 'hua']
# 构造数据queue
# capacity队列的大小，本例子中一个队列元素是['lda.png','tuzi.jpg','lad_tuzi],理解slice切片功能
train_input_queue = tf.train.slice_input_producer(
[images_one_path_list, images_two_path_list,label_list],
capacity= 1*batch_size, shuffle=False)
# queue输出数据
img_one_queue = get_image(train_input_queue[0])
img_two_queue = get_image(train_input_queue[1])
label_queue = train_input_queue[2]
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# 启动queue线程
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(10):
batch_img_one_val, batch_img_two_val, label= sess.run(
[img_one_queue, img_two_queue,label_queue])
plot_pic(batch_img_one_val, batch_img_two_val, label)
coord.request_stop()
coord.join(threads)
sess.close()
slice_input_producer_one_sample()
第一个
第二个
等等。。注意每次读取两个图片一个label与输入list的对应关系
现在把读取的图片内存加入到新列队中
使用tf.train.shuffle_batch
取两次图片，每次取三个,这样程序就从列队中取出已经加载好的图片内存数据
import matplotlib.pyplot as plt
def conver_image_size(img,hsize, wsize):
img = tf.image.convert_image_dtype(img, dtype=tf.float32)
img = tf.image.resize_images(img, [hsize, wsize])
return img
def slice_input_producer_demo():
# 重置graph
tf.reset_default_graph()
# 获取图片系统路径，标签信息
batch_size = 3
hsize = 377
wsize = 500
images_one_path_list = ['lda.png', 'snapshot.png','hua.jpeg']
images_two_path_list = ['tuzi.jpg', 'test.png', 'hua.jpeg']
label_list = ['lad_tuzi', 'snap_test', 'hua']
# 构造数据queue
train_input_queue = tf.train.slice_input_producer(
[images_one_path_list, images_two_path_list,label_list],
capacity= 3, shuffle=False)
# queue输出数据
img_one_queue = get_image(train_input_queue[0])
img_two_queue = get_image(train_input_queue[1])
label_queue = train_input_queue[2]
# shuffle_batch 批量从queue批量读取数据
img_one_queue = conver_image_size(img_one_queue, hsize, wsize)
img_two_queue = conver_image_size(img_two_queue, hsize, wsize)
batch_img_one, batch_img_two, batch_label = tf.train.shuffle_batch(
[img_one_queue, img_two_queue, label_queue],
batch_size=batch_size,
capacity = 10 + 10* batch_size,
min_after_dequeue = 10,
num_threads=16)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# 启动queue线程
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(2):
batch_img_one_val, batch_img_two_val, label= sess.run(
[batch_img_one, batch_img_two,batch_label])
print label
fig = plt.figure(figsize=(4,6))
for k in range(batch_size):
ax1 = fig.add_subplot(batch_size,2,2*k+1)
ax1.set_title(label[k])
plt.imshow(batch_img_one_val[k])
ax2 = fig.add_subplot(batch_size,2,2*k+2)
ax2.set_title(label[k])
ax2.imshow(batch_img_two_val[k])
plt.show()
coord.request_stop()
coord.join(threads)
sess.close()
第一次
第二次
string_input_producer加载序列
def string_input_producter_demo():
tf.reset_default_graph()
images_one_path_list = ['lda.png', 'snapshot.png','hua.jpeg']
images_two_path_list = ['tuzi.jpg', 'test.png', 'hua.jpeg']
label_list = ['lad_tuzi', 'snap_test', 'hua']
batch_size = 2
hsize = 377
wsize = 500
# 构造数据queue
train_input_queue = tf.train.string_input_producer(
images_one_path_list, capacity=10*batch_size)
#queue输出数据
img_one_queue = get_image(train_input_queue.dequeue())
img_one_queue = conver_image_size(img_one_queue, hsize, wsize)
# 将图片数据加载到新的队列
batch_img_one = tf.train.shuffle_batch(
[img_one_queue],
batch_size=batch_size,
capacity = 10 + 10* batch_size,
min_after_dequeue = 10,
num_threads=16)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(2):
for k in range(batch_size):
img_one_val = sess.run(batch_img_one[k])
fig = plt.figure()
plt.imshow(img_one_val)
plt.show()
coord.request_stop()
coord.join(threads)
sess.close()
string_input_producter_demo()
A.csv文件如下加载CSV文件
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()
batch_size = 2
filenames = ['A.csv', 'B.csv', 'C.csv']
filename_queue = tf.train.string_input_producer(
filenames, shuffle=False)
# 定义Reader
reader = tf.TextLineReader()
key,value = reader.read(filename_queue)
# 定义Decoder
example, label = tf.decode_csv(
value, record_defaults = [['null'], ['null']])
batch_data,label_data = tf.train.shuffle_batch(
[example, label],
batch_size=batch_size,
capacity = 10 + 10* batch_size,
min_after_dequeue = 10,
num_threads=16)
with tf.Session() as sess:
# 创建一个协调器,管理线程
coord = tf.train.Coordinator()
# 启动QueueRunner,此时文件数据列队已经进队
threads = tf.train.start_queue_runners(coord=coord)
sess.run(tf.global_variables_initializer())
for i in range(9):
batch_, label_ = sess.run([batch_data, label_data])
print batch_
print label_
print '-----'
coord.request_stop()
coord.join(threads)
每次从列队中加载两个数据
参考
Tensorflow 数据预读取--Queue
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