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| def _generate_image_and_label_batch(image, label, min_queue_examples,
batch_size, shuffle):
# 创建一个混排样本的队列，然后从样本队列中读取 'batch_size'数量的 images + labels数据（每个样本都是由images + labels组成）
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)
# Display the training images in the visualizer.
tf.image_summary('images', images)
return images, tf.reshape(label_batch, [batch_size])
'''
作用： 使用Reader操作构建扭曲的输入(图像)用作CIFAR训练
@param data_dir: CIFAR-10数据目录
batch_size: 每一批量的图像数
@Returns:
images: Images. 尺寸为 [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] 的4D张量
labels: Labels. 大小为[batch_size] 的一维张量
'''
def distorted_inputs(data_dir, batch_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)
#创建一个先进先出的文件名队列，文件阅读器需要它来读取数据
filename_queue = tf.train.string_input_producer(filenames)
# 从文件名队列中读取样本
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
# 用于训练神经网络的图像处理，注意对图像进行了很多随机扭曲处理
# 随机修建图像的某一块[height, width]区域
distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
#随机水平翻转图像
distorted_image = tf.image.random_flip_left_right(distorted_image)
# 由于这些操作都是不可累积的，考虑随机这些操作的顺序
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)
# 减去均值并处以像素的方差 (标准化)
float_image = tf.image.per_image_whitening(distorted_image)
# 确保随机混排有很好的混合性
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)
# 通过构建一个样本队列来生成一批量的图像和标签
return _generate_image_and_label_batch(float_image, read_input.label,
min_queue_examples, batch_size,
shuffle=True)
'''
作用： 使用Reader ops操作构建CIFAR评估的输入
@param:
eval_data: boolean类型, 是否使用训练或评估数据集
data_dir: CIFAR-10数据目录.
batch_size: 每一批的图像数量
Returns:
images: Images. 尺寸为[batch_size, IMAGE_SIZE, IMAGE_SIZE, 3]的4D张量.
labels: Labels. 大小为[batch_size]的一维张量.
'''
def inputs(eval_data, data_dir, batch_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)
#创建一个先进先出的文件名队列，文件阅读器需要它来读取数据
filename_queue = tf.train.string_input_producer(filenames)
# 从文件名队列中读取抽样
read_input = read_cifar10(filename_queue)
reshaped_image = tf.cast(read_input.uint8image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
# 用于做评估的图像处理
# 裁减图像的中心 [height, width] Crop the central [height, width] of the image.
resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
width, height)
# 减去均值并处以像素的方差（标准化）
float_image = tf.image.per_image_whitening(resized_image)
# 确保良好的随机性
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)
|