将cifar10数据集保存为可见图片，,下载cifar10数

将cifar10数据集保存为可见图片，,下载cifar10数

下载cifar10数据集：http://www.cs.toronto.edu/~kriz/cifar.html

选择cifar-10-python.tar.gz进行下载。

1 建立 main.py

import tensorflow as tfimport osimport scipy.miscimport cifar10_inputdef inputs_origin(data_dir):    filenames = [os.path.join(data_dir, ‘data_batch_%d‘ % i) for i in range(1, 6)]    for f in filenames:        print(f)        if not tf.gfile.Exists(f):            raise ValueError(‘Failed to find file‘ + f)    filenames_queue =tf.train.string_input_producer(filenames)    read_input = cifar10_input.read_cifar10(filenames_queue)    reshaped_image = tf.cast(read_input.uint8image,tf.float32)    print(reshaped_image)    return reshaped_imageif __name__ == ‘__main__‘:    with tf.Session() as sess:        reshaped_image = inputs_origin(‘cifar-10-batches-py‘)        threads = tf.train.start_queue_runners(sess=sess)        print(threads)        sess.run(tf.global_variables_initializer())        if not os.path.exists(‘cifar-10-batches-py/raw/‘):            os.makedirs(‘cifar-10-batches-py/raw/‘)        for i in range(30):            image = sess.run(reshaped_image)            scipy.misc.toimage(image).save(‘cifar-10-batches-py/raw/%d.jpg‘ %i)

2 建立cifar10_input.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport osfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tf# Process images of this size. Note that this differs from the original CIFAR# image size of 32 x 32. If one alters this number, then the entire model# architecture will change and any model would need to be retrained.IMAGE_SIZE = 24# Global constants describing the CIFAR-10 data set.NUM_CLASSES = 10NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000def read_cifar10(filename_queue):  """Reads and parses examples from CIFAR10 data files.  Recommendation: if you want N-way read parallelism, call this function  N times.  This will give you N independent Readers reading different  files & positions within those files, which will give better mixing of  examples.  Args:    filename_queue: A queue of strings with the filenames to read from.  Returns:    An object representing a single example, with the following fields:      height: number of rows in the result (32)      width: number of columns in the result (32)      depth: number of color channels in the result (3)      key: a scalar string Tensor describing the filename & record number        for this example.      label: an int32 Tensor with the label in the range 0..9.      uint8image: a [height, width, depth] uint8 Tensor with the image data  """  class CIFAR10Record(object):    pass  result = CIFAR10Record()  # Dimensions of the images in the CIFAR-10 dataset.  # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the  # input format.  label_bytes = 1  # 2 for CIFAR-100  result.height = 50  result.width = 50  result.depth = 3  image_bytes = result.height * result.width * result.depth  # Every record consists of a label followed by the image, with a  # fixed number of bytes for each.  record_bytes = label_bytes + image_bytes  # Read a record, getting filenames from the filename_queue.  No  # header or footer in the CIFAR-10 format, so we leave header_bytes  # and footer_bytes at their default of 0.  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)  result.key, value = reader.read(filename_queue)  # Convert from a string to a vector of uint8 that is record_bytes long.  record_bytes = tf.decode_raw(value, tf.uint8)  # The first bytes represent the label, which we convert from uint8->int32.  result.label = tf.cast(      tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)  # The remaining bytes after the label represent the image, which we reshape  # from [depth * height * width] to [depth, height, width].  depth_major = tf.reshape(      tf.strided_slice(record_bytes, [label_bytes],                       [label_bytes + image_bytes]),      [result.depth, result.height, result.width])  # Convert from [depth, height, width] to [height, width, depth].  result.uint8image = tf.transpose(depth_major, [1, 2, 0])  return resultdef _generate_image_and_label_batch(image, label, min_queue_examples,                                    batch_size, shuffle):  """Construct a queued batch of images and labels.  Args:    image: 3-D Tensor of [height, width, 3] of type.float32.    label: 1-D Tensor of type.int32    min_queue_examples: int32, minimum number of samples to retain      in the queue that provides of batches of examples.    batch_size: Number of images per batch.    shuffle: boolean indicating whether to use a shuffling queue.  Returns:    images: Images. 4D tensor of [batch_size, height, width, 3] size.    labels: Labels. 1D tensor of [batch_size] size.  """  # Create a queue that shuffles the examples, and then  # read ‘batch_size‘ images + labels from the example queue.  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.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)  # 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.  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)  # 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, width, height)  # 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)

　显示部分图片：

将cifar10数据集保存为可见图片