TensorFlow笔记(基础篇):加载数据之从队列中读取
前言
整体步骤
在TensorFlow中进行模型训练时,在官网给出的三种读取方式,中最好的文件读取方式就是将利用队列进行文件读取,而且步骤有两步:
1. 把样本数据写入TFRecords二进制文件
2. 从队列中读取数据
读取TFRecords文件步骤
使用队列读取数TFRecords 文件 数据的步骤
1. 创建张量,从二进制文件读取一个样本数据
2. 创建张量,从二进制文件随机读取一个mini-batch
3. 把每一批张量传入网络作为输入点
TensorFlow使用TFRecords文件训练样本的步骤
- 在生成文件名的序列中,设定epoch数量
- 训练时,设定为无穷循环
- 在读取数据时,如果捕捉到错误,终止
source code
tensorflow-master\tensorflow\examples\how_tos\reading_data\fully_connected_reader.py(1.2.1)
CODE
代码与解析
解析主要在注释中,最后一个模块if __name__ == '__main__':的运行,建议参考’http://blog.csdn.net/fontthrone/article/details/76735591’
import tensorflow as tf
import os
# from tensorflow.contrib.learn.python.learn.datasets import mnist
# 注意上面的这个mnist 与 example 中的 mnist 是不同的,本文件中请使用下面的那个 mnist
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import argparse
import os.path
import sys
import time
from tensorflow.examples.tutorials.mnist import mnist
# Basic model parameters as external flags.
FLAGS = None
# This part of the code is added by FontTian,which comes from the source code of tensorflow.examples.tutorials.mnist
# The MNIST images are always 28x28 pixels.
# IMAGE_SIZE = 28
# IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
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.
# 必须写明faetures 中的 key 的名称
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
# 将一个标量字符串张量(其单个字符串的长度是mnist.image像素) # 0 维的Tensor
# 转换为一个带有形状mnist.图像像素的uint8张量。 # 一维的Tensor
image = tf.decode_raw(features['image_raw'], tf.uint8)
# print(tf.shape(image)) # Tensor("input/Shape:0", shape=(1,), dtype=int32)
image.set_shape([mnist.IMAGE_PIXELS])
# print(tf.shape(image)) # Tensor("input/Shape_1:0", shape=(1,), dtype=int32)
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
# Convert from [0, 255] -> [-0.5, 0.5] floats.
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
# print(tf.shape(image)) # Tensor("input/Shape_2:0", shape=(1,), dtype=int32)
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
# print(tf.shape(label)) # Tensor("input/Shape_3:0", shape=(0,), dtype=int32)
return image, label
# 使用 tf.train.shuffle_batch 将前面生成的样本随机化,获得一个最小批次的张量
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
输入参数:
train: Selects between the training (True) and validation (False) data.
batch_size: 训练的每一批有多少个样本
num_epochs: 读取输入数据的次数, or 0/None 表示永远训练下去
返回结果:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
范围: [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
范围: [0, mnist.NUM_CLASSES).
注意 : tf.train.QueueRunner 被添加进 graph, 它必须用 tf.train.start_queue_runners() 来启动线程.
"""
if not num_epochs: num_epochs = None
filename = os.path.join(FLAGS.train_dir,
TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
# tf.train.string_input_producer 返回一个 QueueRunner,里面有一个 FIFQueue
filename_queue = tf.train.string_input_producer(
[filename], num_epochs=num_epochs)
# 如果样本数据很大,可以分成若干文件,把文件名列表传入
# Even when reading in multiple threads, share the filename queue.
image, label = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
images, sparse_labels = tf.train.shuffle_batch(
[image, label], batch_size=batch_size, num_threads=2,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
# 留下一部分队列,来保证每次有足够的数据做随机打乱
min_after_dequeue=1000)
return images, sparse_labels
def run_training():
"""Train MNIST for a number of steps."""
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Input images and labels.
images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
# 构建一个从推理模型来预测数据的图
logits = mnist.inference(images,
FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the loss calculation.
# 定义损失函数
loss = mnist.loss(logits, labels)
# 将模型添加到图操作中
train_op = mnist.training(loss, FLAGS.learning_rate)
# 初始化变量的操作
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Create a session for running operations in the Graph.
# 在图中创建一个用于运行操作的会话
sess = tf.Session()
# 初始化变量,注意:string_input_product 内部创建了一个epoch计数器
sess.run(init_op)
# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
step = 0
while not coord.should_stop():
start_time = time.time()
# Run one step of the model. The return values are
# the activations from the `train_op` (which is
# discarded) and the `loss` op. To inspect the values
# of your ops or variables, you may include them in
# the list passed to sess.run() and the value tensors
# will be returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
# Print an overview fairly often.
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
duration))
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
finally:
# 通知其他线程关闭
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
def main(_):
run_training()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--learning_rate',
type=float,
default=0.01,
help='Initial learning rate.'
)
parser.add_argument(
'--num_epochs',
type=int,
default=2,
help='Number of epochs to run trainer.'
)
parser.add_argument(
'--hidden1',
type=int,
default=128,
help='Number of units in hidden layer 1.'
)
parser.add_argument(
'--hidden2',
type=int,
default=32,
help='Number of units in hidden layer 2.'
)
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='Batch size.'
)
parser.add_argument(
'--train_dir',
type=str,
default='/tmp/data',
help='Directory with the training data.'
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)运行结果
Step 0: loss = 2.31 (0.106 sec)
Step 100: loss = 2.14 (0.016 sec)
Step 200: loss = 1.91 (0.016 sec)
Step 300: loss = 1.69 (0.016 sec)
Step 400: loss = 1.28 (0.016 sec)
Step 500: loss = 1.02 (0.016 sec)
Step 600: loss = 0.70 (0.016 sec)
Step 700: loss = 0.71 (0.016 sec)
Step 800: loss = 0.71 (0.016 sec)
Step 900: loss = 0.49 (0.016 sec)
Step 1000: loss = 0.58 (0.016 sec)
Done training for 2 epochs, 1100 steps.相关
- 把样本数据写入TFRecords二进制文件 : http://blog.csdn.net/fontthrone/article/details/76727412
- TensorFlow笔记(基础篇):加载数据之预加载数据与填充数据:http://blog.csdn.net/fontthrone/article/details/76727466
- python中的argparse模块:http://blog.csdn.net/fontthrone/article/details/76735591
