tensorflow 使用数据集(tf.data)的方法对数据集进行操纵。
1. 对 数组(内存向量) 进行操纵 :
import tensorflow as tf
input_data = [1, 2, 3, 4, 5]
#从数组生成数据集
dataset = tf.data.Dataset.from_tensor_slices(input_data)
#dataset = dataset.shuffle(3)
#dataset = dataset.repeat(10)
#dataset = dataset.batch(2)
dataset = dataset.shuffle(3).repeat(10).batch(2)
# 定义迭代器。
iterator = dataset.make_one_shot_iterator()
# get_next() 返回代表一个输入数据的张量(batch)。
x = iterator.get_next()
y = x * x
coord=tf.train.Coordinator()
with tf.Session() as sess:
for i in range(25):
print(sess.run(y))
2. 读取文本文件里的数据 ( tf.data.TextLineDataset )
import tensorflow as tf
# 创建文本文件作为本例的输入。
with open("./test1.txt", "w") as file:
file.write("File1, line1.\n")
file.write("File1, line2.\n")
file.write("File1, line3.\n")
file.write("File1, line4.\n")
file.write("File1, line5.\n")
with open("./test2.txt", "w") as file:
file.write("File2, line1.\n")
file.write("File2, line2.\n")
file.write("File2, line3.\n")
file.write("File2, line4.\n")
file.write("File2, line5.\n")
# 从文本文件创建数据集。这里可以提供多个文件。
input_files = ["./test1.txt", "./test2.txt"]
dataset = tf.data.TextLineDataset(input_files)
#dataset = dataset.shuffle(3).repeat(2).batch(2)
# 定义迭代器。
iterator = dataset.make_one_shot_iterator()
# 这里get_next()返回一个字符串类型的张量,代表文件中的一行。
x = iterator.get_next()
with tf.Session() as sess:
for i in range(10):
print(sess.run(x))
3. 解析TFRecord文件里的数据
准备工作:(mnist数据集的tfrecord格式的保存)
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),
'y':_bytes_feature(bytes('abcde', 'utf-8')),
'image_raw':_bytes_feature(image_raw)}))
writer.write(example.SerializeToString())
writer.close()
准备工作:(mnist数据集的tfrecord格式的读取)
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),
'y':tf.FixedLenFeature([], tf.string)
})
#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
x=features['x']
#y=tf.cast(features['y'], tf.string)
y=features['y']
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=2
image_batch, label_batch, pixels_batch, x_batch, y_batch=tf.train.batch([image, label, pixels,x,y], 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(1):
print(sess.run([image_batch, label_batch, pixels_batch, x_batch, y_batch]))
coord.request_stop()
coord.join(threads)
正式工作:(mnist数据集的tfrecord格式 使用 TFRecordDataset 数据集读取)
import tensorflow as tf
files=tf.gfile.Glob('./data/output.*')
dataset = tf.data.TFRecordDataset(files)
def parser(record):
features=tf.parse_single_example(record,
features={
'image_raw':tf.FixedLenFeature([], tf.string),
'pixels':tf.FixedLenFeature([], tf.int64),
'label':tf.FixedLenFeature([], tf.int64),
'x':tf.FixedLenFeature([], tf.float32),
'y':tf.FixedLenFeature([], tf.string)
})
#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
x=features['x']
#y=tf.cast(features['y'], tf.string)
y=features['y']
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])
return image, label, pixels, x, y
# map()函数表示对数据集中的每一条数据进行调用解析方法。
dataset = dataset.map(parser)
# dataset 数据集操纵
dataset = dataset.shuffle(3).repeat(2).batch(2)
# 定义遍历数据集的迭代器。
iterator = dataset.make_one_shot_iterator()
# 读取数据,可用于进一步计算
image, label, pixels, x, y = iterator.get_next()
with tf.Session() as sess:
for i in range(1):
print(sess.run([image, label, pixels, x, y]))
4. 使用 initializable_iterator 来动态初始化数据集
# 从TFRecord文件创建数据集,具体文件路径是一个placeholder,稍后再提供具体路径。
input_files = tf.placeholder(tf.string)
dataset = tf.data.TFRecordDataset(input_files)
dataset = dataset.map(parser)
# 定义遍历dataset的initializable_iterator。
iterator = dataset.make_initializable_iterator()
image, label = iterator.get_next()
with tf.Session() as sess:
# 首先初始化iterator,并给出input_files的值。
sess.run(iterator.initializer,
feed_dict={input_files: ["output.tfrecords"]})
# 遍历所有数据一个epoch。当遍历结束时,程序会抛出OutOfRangeError。
while True:
try:
x, y = sess.run([image, label])
except tf.errors.OutOfRangeError:
break
详细例子:
import tensorflow as tf
files=tf.placeholder(tf.string)
dataset = tf.data.TFRecordDataset(files)
def parser(record):
features=tf.parse_single_example(record,
features={
'image_raw':tf.FixedLenFeature([], tf.string),
'pixels':tf.FixedLenFeature([], tf.int64),
'label':tf.FixedLenFeature([], tf.int64),
'x':tf.FixedLenFeature([], tf.float32),
'y':tf.FixedLenFeature([], tf.string)
})
#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
x=features['x']
#y=tf.cast(features['y'], tf.string)
y=features['y']
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])
return image, label, pixels, x, y
# map()函数表示对数据集中的每一条数据进行调用解析方法。
dataset = dataset.map(parser)
# dataset 数据集操纵
dataset = dataset.shuffle(3).repeat(2).batch(2)
# 定义遍历数据集的迭代器。
#iterator = dataset.make_one_shot_iterator()
# 定义遍历dataset的initializable_iterator。
iterator = dataset.make_initializable_iterator()
# 读取数据,可用于进一步计算
image, label, pixels, x, y = iterator.get_next()
with tf.Session() as sess:
# 首先初始化iterator,并给出input_files的值。
sess.run(iterator.initializer,
feed_dict={files: ["data/output.tfrecords"]})
for i in range(1):
print(sess.run([image, label, pixels, x, y]))
或(修改版):
import tensorflow as tf
files=tf.train.match_filenames_once('./data/output.*')
dataset = tf.data.TFRecordDataset(files)
def parser(record):
features=tf.parse_single_example(record,
features={
'image_raw':tf.FixedLenFeature([], tf.string),
'pixels':tf.FixedLenFeature([], tf.int64),
'label':tf.FixedLenFeature([], tf.int64),
'x':tf.FixedLenFeature([], tf.float32),
'y':tf.FixedLenFeature([], tf.string)
})
#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
x=features['x']
#y=tf.cast(features['y'], tf.string)
y=features['y']
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])
return image, label, pixels, x, y
# map()函数表示对数据集中的每一条数据进行调用解析方法。
dataset = dataset.map(parser)
# dataset 数据集操纵
dataset = dataset.shuffle(3).repeat(2).batch(2)
# 定义遍历数据集的迭代器。
#iterator = dataset.make_one_shot_iterator()
# 定义遍历dataset的initializable_iterator。
iterator = dataset.make_initializable_iterator()
# 读取数据,可用于进一步计算
image, label, pixels, x, y = iterator.get_next()
with tf.Session() as sess:
# 初始化变量。
sess.run((tf.global_variables_initializer(),
tf.local_variables_initializer()))
# 首先初始化iterator,并给出input_files的值。
sess.run(iterator.initializer)
while True:
try:
print(sess.run([image, label, pixels, x, y]))
except tf.errors.OutOfRangeError:
break
注:
迭代器:
make_one_shot_iterator 方法不能重复初始化,即one-shot(一次性),但是可以自动初始化。