TensorFlow提供了一种统一的格式来存储数据，这个格式就是TFRecord，TFRecord文件中的数据都是通过tf.train.Example Protocol Buffer的格式.proto来存储的。以下代码给出了tf.train.Example的定义。
message Example{
Features features = 1;
}
message Features{
map<string, Feature> feature = 1;
}
message Feature {
oneof kind {
BytesList bytes_list = 1;
FloatList float_list = 2;
Int64List int64_list = 3;
}
};从以上代码可以看出tf.train.Example的数据结构是比较简单的。tf.train.Example中包含了一个从属性和名称到取值的字典。其中属性名称为一个字符串，属性的取值可以为字符串(ByteList)、实数列表(FloatList)或者整数列表(int64List)。比如将一张解码前的图像存为一个字符串，图像所对应的类别编号为整数列表。以下程序给出了如何将MNIST输入数据转化为TFRecord的格式。
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
# 生成整数型的属性。
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
# 生成字符串型的属性
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
mnist = input_data.read_data_sets("data/mnist", dtype=tf.uint8, one_hot=True)
images = mnist.train.images
# 训练数据对应的正确率，可以作为一个属性保存在TFRecord中
labels = mnist.train.labels
# 训练数据的图像分辨率，这可以作为Example中的一个属性。
pixels = images.shape[1]
num_examples = mnist.train.num_examples
# 输出TFRecord文件的地址。
filename = "data/output.tfrecords"
# 创建一个writer来写TFRecord文件。
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
# 将图像矩阵转化成一个字符串
image_raw = images[index].tostring()
# 将一个样例转化为Example Protocol Buffer，并将所有的信息写入这个数据结构
example = tf.train.Example(features=tf.train.Features(feature={
'pixels': _int64_feature(pixels),
'label': _int64_feature(np.argmax(labels[index])),
'image_raw': _bytes_feature(image_raw)}))
# 将一个Example写入TFRecord文件
writer.write(example.SerializeToString())
writer.close()
Output:
---------------------------------------------------------------------------------------
Extracting data/mnist/train-images-idx3-ubyte.gz
WARNING:tensorflow:From /home/user8/Desktop/python_learning/tf_py_func.py:16: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Extracting data/mnist/train-labels-idx1-ubyte.gz
Extracting data/mnist/t10k-images-idx3-ubyte.gz
Extracting data/mnist/t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/user8/Desktop/python_learning/tf_py_func.py:29: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.
---------------------------------------------------------------------------------------以上程序可以将MNIST数据集中所有的训练数据存储到一个TFRecord文件中。当数据量较大时，也可以将数据写入多个TFRecord文件。Tensorflow对从文件列表中读取数据提供了很好的支持，以下程序给出了如何读取TFRecord文件中的数据。
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "4"
# 创建一个reader来读取TFRecord文件中的样例。
reader = tf.TFRecordReader()
# 创建一个队列来维护输入文件列表
# tf.train.string_input_product函数。
filename_queue = tf.train.string_input_producer(["/home/user8/Desktop/python_learning/data/output.tfrecords"])
# 从文件中读出一个样例、也可以使用read_up_to函数一次性多个样例。
_, serialized_example = reader.read(filename_queue)
# 解析读入的一个样例，如果需要解析多个样例，可以用parse_example函数
features = tf.parse_single_example(
serialized_example,
features = {
# tensorflow提供两种不同的属性解析方法。一种方法是tf.FixedLenFeature，
# 这种方法解析的结果为一个Tensor。另一种方法是tf.VarLenFeature，这种方法
# 得到的解析结果为SparseTensor，用于处理稀疏函数。这里解析数据的格式需要和
# 上面程序写入的数据的格式一致。
'image_raw': tf.FixedLenFeature([], tf.string),
'pixels': tf.FixedLenFeature([], tf.int64),
'label': tf.FixedLenFeature([], tf.int64),
})
# tf.decode_raw可以将字符串解析成图像对应的像素数组。
image = tf.decode_raw(features['image_raw'], tf.uint8)
label = tf.cast(features['label'], tf.int32)
pixels = tf.cast(features['pixels'], tf.int32)
sess = tf.Session( )
# 启动多线程处理数据
coord = tf.train.Coordinator( )
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# 每次运行可以读取TFRecord文件中的一个样例。当所有样例都读完之后，在此样例中程序会再重头读取。
for i in range(10):
print(sess.run([image, label, pixels]))
Output:
/home/user8/anaconda3/envs/keras/bin/python3 /home/user8/Desktop/python_learning/read_tfrecord.py
WARNING:tensorflow:From /home/user8/Desktop/python_learning/read_tfrecord.py:6: TFRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.TFRecordDataset`.
WARNING:tensorflow:From /home/user8/Desktop/python_learning/read_tfrecord.py:9: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/python/training/input.py:277: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/python/training/input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/python/training/input.py:198: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/user8/anaconda3/envs/keras/lib/python3.6/site-packages/tensorflow_core/python/training/input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /home/user8/Desktop/python_learning/read_tfrecord.py:15: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.
WARNING:tensorflow:From /home/user8/Desktop/python_learning/read_tfrecord.py:22: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.
WARNING:tensorflow:From /home/user8/Desktop/python_learning/read_tfrecord.py:32: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
2020-01-11 18:23:57.702926: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-01-11 18:23:57.931375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:86:00.0
2020-01-11 18:23:57.931700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-01-11 18:23:57.933458: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-01-11 18:23:57.935244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-01-11 18:23:57.935666: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-01-11 18:23:57.937676: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-01-11 18:23:57.939316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-01-11 18:23:57.946119: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-01-11 18:23:57.949453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-01-11 18:23:57.950012: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2020-01-11 18:23:57.968663: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199900000 Hz
2020-01-11 18:23:57.974541: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55edf797f250 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-01-11 18:23:57.974597: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-01-11 18:23:57.978107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:86:00.0
2020-01-11 18:23:57.978186: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-01-11 18:23:57.978203: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-01-11 18:23:57.978218: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-01-11 18:23:57.978232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-01-11 18:23:57.978246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-01-11 18:23:57.978260: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-01-11 18:23:57.978275: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-01-11 18:23:57.981300: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-01-11 18:23:57.981348: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-01-11 18:23:58.376006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-01-11 18:23:58.376045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2020-01-11 18:23:58.376054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2020-01-11 18:23:58.403679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11448 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:86:00.0, compute capability: 6.1)
2020-01-11 18:23:58.407388: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55edf81027b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-01-11 18:23:58.407409: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): TITAN X (Pascal), Compute Capability 6.1
WARNING:tensorflow:From /home/user8/Desktop/python_learning/read_tfrecord.py:35: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
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Process finished with exit code 0
