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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""解码CIFAR-10二进制文件"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from six.moves import xrange
import tensorflow as tf
# 处理图像为这个大小。注意这与原始的CIFAR图像大小32*32不同。 如果改变这个数字，那么整个模型结构会随之改变并需要重新训练。
IMAGE_SIZE = 24
# 描述CIFAR-10数据集的全局常量。
NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000
def read_cifar10(filename_queue):
"""从CIFAR10数据文件中阅读并解析example。
推荐：如果你想要N路并行阅读，那么调用这个函数N次。这样会返回N个独立的Reader用来阅读那些文件里不同的文件和位置，这样会返回更好的混合example。
参数:
filename_queue: 文件名字符串队列。
返回:
一个object代表一个example，包括以下内容：
高: result的行数(32)
宽: result的列数(32)
深: result的色彩通道数(3)
key: 一个标量字符串描述这个example的文件名和record number。
标签: 一个带有标签（0..9）的int32 Tensor
uint8image: 一个带有图像数据的[height, width, depth] uint8 Tensor
"""
class CIFAR10Record(object):
pass
result = CIFAR10Record()
# CIFAR-10数据集中图像的维度。
label_bytes = 1
result.height = 32
result.width = 32
result.depth = 3
image_bytes = result.height * result.width * result.depth
# 每一个record包含一个标签和一个固定长度的用来描述图像的bytes。
record_bytes = label_bytes + image_bytes
# 阅读一个record，从filename_queue中获得filename。CIFAR-10格式没有header何footer，所以我们默认header——bytes和footer_bytes为0。
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
result.key, value = reader.read(filename_queue)
# 将一个字符串转换成一个uint8向量
record_bytes = tf.decode_raw(value, tf.uint8)
# 第一个bytes代表标签，我们将它转换成int32。
result.label = tf.cast(
tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
# 剩下的bytes代表图像，我们将它reshape成[depth, height, width]。
depth_major = tf.reshape(
tf.strided_slice(record_bytes, [label_bytes],
[label_bytes + image_bytes]),
[result.depth, result.height, result.width])
# 转换成[height, width, depth]
result.uint8image = tf.transpose(depth_major, [1, 2, 0])
return result
def _generate_image_and_label_batch(image, label, min_queue_examples,
batch_size, shuffle):
"""生成一个图像和标签batch队列。
Args:
image: float32类型的[height, width, 3]Tensor
label: int32类型的Tensor
min_queue_examples: int32,保留在队列中的samples的最小数量，用来提供example batch。
batch_size: 每个batch的图像数量。
shuffle: boolean 表示是否打乱队列。
Returns:
images: Images. [batch_size, height, width, 3] tensor
labels: Labels. [batch_size] tensor
"""
# 创建一个队列来打乱example，然后阅读‘batch_size‘ images + labels
num_preprocess_threads = 16
if shuffle:
images, label_batch = tf.train.shuffle_batch(
[image, label],
batch_size=batch_size,
num_threads=num_preprocess_threads,
capacity=min_queue_examples + 3 * batch_size,
min_after_dequeue=min_queue_examples)
else:
images, label_batch = tf.train.batch(
[image, label],
batch_size=batch_size,
num_threads=num_preprocess_threads,
capacity=min_queue_examples + 3 * batch_size)
# 在visualizer中展示训练图像。
tf.summary.image(‘images‘, images)
return images, tf.reshape(label_batch, [batch_size])
def distorted_inputs(data_dir, batch_size):
"""Construct distorted input for CIFAR training using the Reader ops.
Args:
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
filenames = [os.path.join(data_dir, ‘data_batch_%d.bin‘ % i)
for i in xrange(1, 6)]
for f in filenames:
if not tf.gfile.Exists(f):
raise ValueError(‘Failed to find file: ‘ + f)
# Create a queue that produces the filenames to read.
filename_queue = tf.train.string_input_producer(filenames)
with tf.name_scope(‘data_augmentation‘):
# Read examples from files in the filename queue.
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
# Image processing for training the network. Note the many random
# distortions applied to the image.
# Randomly crop a [height, width] section of the image.
distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
# Randomly flip the image horizontally.
distorted_image = tf.image.random_flip_left_right(distorted_image)
# Because these operations are not commutative, consider randomizing
# the order their operation.
# NOTE: since per_image_standardization zeros the mean and makes
# the stddev unit, this likely has no effect see tensorflow#1458.
distorted_image = tf.image.random_brightness(distorted_image,
max_delta=63)
distorted_image = tf.image.random_contrast(distorted_image,
lower=0.2, upper=1.8)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_standardization(distorted_image)
# Set the shapes of tensors.
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])
# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
min_fraction_of_examples_in_queue)
print (‘Filling queue with %d CIFAR images before starting to train. ‘
‘This will take a few minutes.‘ % min_queue_examples)
# Generate a batch of images and labels by building up a queue of examples.
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples, batch_size,
shuffle=True)
def inputs(eval_data, data_dir, batch_size):
"""Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
data_dir: Path to the CIFAR-10 data directory.
batch_size: Number of images per batch.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
"""
if not eval_data:
filenames = [os.path.join(data_dir, ‘data_batch_%d.bin‘ % i)
for i in xrange(1, 6)]
num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
else:
filenames = [os.path.join(data_dir, ‘test_batch.bin‘)]
num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
for f in filenames:
if not tf.gfile.Exists(f):
raise ValueError(‘Failed to find file: ‘ + f)
with tf.name_scope(‘input‘):
# Create a queue that produces the filenames to read.
filename_queue = tf.train.string_input_producer(filenames)
# Read examples from files in the filename queue.
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
# Image processing for evaluation.
# Crop the central [height, width] of the image.
resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
height, width)
# Subtract off the mean and divide by the variance of the pixels.
float_image = tf.image.per_image_standardization(resized_image)
# Set the shapes of tensors.
float_image.set_shape([height, width, 3])
read_input.label.set_shape([1])
# Ensure that the random shuffling has good mixing properties.
min_fraction_of_examples_in_queue = 0.4
min_queue_examples = int(num_examples_per_epoch *
min_fraction_of_examples_in_queue)
# Generate a batch of images and labels by building up a queue of examples.
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples, batch_size,
shuffle=False)
原文地址：https://www.cnblogs.com/estellellll/p/10551157.html
时间： 2024-08-30 03:40:44