1. 利用TFRecord 格式 读、存 取 Mnist数据集的方法
存取 Mnist数据集的方法 (TFRecord格式)
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
def _float32_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
mnist=input_data.read_data_sets('./data', dtype=tf.uint8, one_hot=True)
"""
print(mnist.train.images)
print(mnist.train.labels)
print(mnist.test.images)
print(mnist.test.labels)
"""
train_images=mnist.train.images
train_labels=mnist.train.labels
#test_images=mnist.test.images
#test_labels=mnist.test.labels
train_num=mnist.train.num_examples
#test_num=mnist.test.num_examples
pixels=train_images.shape[1] # 784 = 28*28
file_out='./data/output.tfrecords'
writer=tf.python_io.TFRecordWriter(file_out)
for index in range(train_num):
image_raw=train_images[index].tostring() #转换为bytes序列
example=tf.train.Example(features=tf.train.Features(feature={
'pixels': _int64_feature(pixels),
'label':_int64_feature(np.argmax(train_labels[index])),
'x':_float32_feature(0.1),
'image_raw':_bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
writer.close()
读取 Mnist数据集的方法 (TFRecord格式)
import tensorflow as tf
reader=tf.TFRecordReader()
files=tf.train.match_filenames_once('./data/output.*')
#filename_queue=tf.train.string_input_producer(['./data/output.tfrecords'])
filename_queue=tf.train.string_input_producer(files)
_, serialized_example=reader.read(filename_queue)
features=tf.parse_single_example(serialized_example,
features={
'image_raw':tf.FixedLenFeature([], tf.string),
'pixels':tf.FixedLenFeature([], tf.int64),
'label':tf.FixedLenFeature([], tf.int64),
'x':tf.FixedLenFeature([], tf.float32)
})
#print(features['image_raw']) # tensor string (bytes tensor string tensor)
# necessary operation
# bytes_list to uint8_list
image=tf.decode_raw(features['image_raw'], tf.uint8)
#print(image) # tensor uint8
label=tf.cast(features['label'], tf.int32)
pixels=tf.cast(features['pixels'], tf.int32)
#image.set_shape([pixels**0.5, pixels**0.5])
image.set_shape([784])
batch_size=128
image_batch, label_batch, pixels_batch=tf.train.batch([image, label, pixels], batch_size=batch_size, capacity=1000+3*batch_size)
coord=tf.train.Coordinator()
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
threads=tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(3):
print(sess.run([image_batch, label_batch, pixels_batch]))
coord.request_stop()
coord.join(threads)
==================================================================
2. 利用TFRecord 格式 存取 文件夹内图片的方法
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
def _float32_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
file_out='./data/output1.tfrecords'
writer=tf.python_io.TFRecordWriter(file_out)
files = tf.gfile.Glob('./data/*.jpg')
sess=tf.Session()
for file in files:
image_raw_data = tf.gfile.FastGFile(file,'rb').read()
img_data = tf.image.decode_jpeg(image_raw_data) # tensor
img_data = sess.run(img_data) # np.array int
resized = img_data.tostring() # np.array string uint8
example=tf.train.Example(features=tf.train.Features(feature={
'y':_int64_feature(1),
'x':_float32_feature(0.1),
'image_raw':_bytes_feature(resized)}))
writer.write(example.SerializeToString())
writer.close()
文件读取过程使用 输入队列 :
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
def _float32_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
def _int64_feature(value):
#value类型应为:int,long,float
#return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
#value类型应为:[int],[long],[float], 这里为int的list类型
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
file_out='./data/output2.tfrecords'
writer=tf.python_io.TFRecordWriter(file_out)
files = tf.train.match_filenames_once('./data/*.jpg')
# string_input_producer会产生一个文件名队列
filename_queue = tf.train.string_input_producer(files, shuffle=False, num_epochs=3)
# reader从文件名队列中读数据。对应的方法是reader.read
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
img_data = tf.image.decode_jpeg(value) # np.array 转换为 tensor
#print(sess.run([key, img_data]))
#print(img_data.get_shape())
img_data.set_shape([None, None, 3])
img_float = tf.image.convert_image_dtype(img_data, tf.float32)
img_float = tf.image.resize_images(img_float, [300, 300], method=0)
with tf.Session() as sess:
# tf.train.string_input_producer定义了一个epoch变量,要对它进行初始化
tf.local_variables_initializer().run()
x=np.array([[1,1,1,1],[1,1,1,1]])
coord = tf.train.Coordinator()
# 使用start_queue_runners之后,才会开始填充队列
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for _ in range(3):
resized=sess.run(img_data)
resized = resized.tostring()
example=tf.train.Example(features=tf.train.Features(feature={
'x':_int64_feature(x.reshape(x.size).tolist()),
'x2':_int64_feature([1,1]),
'y':_float32_feature(0.1),
'image_raw':_bytes_feature(resized)}))
writer.write(example.SerializeToString())
writer.close()
coord.request_stop()
coord.join(threads)