cifar10数据集训练

cifar10数据集训练有关CIFAR-10数据集(1)CIFAR-10数据集由10个类的60000个32×32彩色图像组成,每个类有6000个图像。有50000个训练图像和10000个测试图像。(2)数据集分为五个训练批次和一个测试批次,每个批次有10000个图像。测试批次包含来自每个类别的恰好1000个随机选择的图像。(3)第一部分是特征部分,使用一个[10000,3072的uint8的矩阵进行存储,每一行向量都是3X3大小的3通道图片,构成的格式类似于[3,3,3];第二部分为标签部分,使用一个10000.

有关CIFAR-10数据集 

(1)CIFAR-10数据集由10个类的60000个32×32彩色图像组成,每个类有6000个图像。有50000个训 练图像和10000个测试图像。

(2)数据集分为五个训练批次和一个测试批次,每个批次有10000个图像。测试批次包含来自每个类别 的恰好1000个随机选择的图像。

(3)第一部分是特征部分,使用一个[10000,3072的uint8的矩阵进行存储,每一行向量都是3X3大小的 3通道图片,构成的格式类似于[3,3,3];第二部分为标签部分,使用一个10000数据的list进行存 储,每个list对应的是0-9中的一个数字,对应于物品分类。另外对于python的数据集,还有一个 标签为“label_names”,例如label_names[0] == “airplane”等。

CIFAR-10数据集下载 

官网:http://www.cs.toronto.edu/~kriz/cifar.html

CIFAR-10数据集训练

最终可以运行的代码和一些解释:

1. cifar10_input.py 

"""Routine for decoding the CIFAR-10 binary file format."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from six.moves import xrange  # pylint: disable=redefined-builtin
import 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.
# 原图像的尺度为32*32,但根据常识,信息部分通常位于图像的中央,
# 这里定义了以中心裁剪后图像的尺寸
IMAGE_SIZE = 24

# Global constants describing the CIFAR-10 data set.
NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000


def 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
    """

    # 定义一个空的类对象,类似于c语言里面的结构体定义
    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 = 32
    result.width = 32
    result.depth = 3
    # 一张图像占用空间
    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.
    # 数据集中一条记录的组成
    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.
    # 定义一个Reader,它每次能从文件中读取固定字节数
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    # 返回从filename_queue中读取的(key, value)对,key和value都是字符串类型的tensor,并且当队列中的某一个文件读完成时,该文件名会dequeue
    result.key, value = reader.read(filename_queue)

    # Convert from a string to a vector of uint8 that is record_bytes long.
    # 解码操作可以看作读二进制文件,把字符串中的字节转换为数值向量,每一个数值占用一个字节,在[0, 255]区间内,因此out_type要取uint8类型
    record_bytes = tf.decode_raw(value, tf.uint8)  # 将字符串Tensor转化成uint8类型

    # The first bytes represent the label, which we convert from uint8->int32.
    # 从一维tensor对象中截取一个slice,类似于从一维向量中筛选子向量,因为record_bytes中包含了label和feature,故要对向量类型tensor进行'parse'操作
    result.label = tf.cast(
        tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)  # 分别表示待截取片段的起点和长度,并且把标签由之前的uint8转变成int32数据类型

    # The remaining bytes after the label represent the image, which we reshape.
    # from [depth * height * width] to [depth, height, width].
    # 提取每条记录中的图像数据为result.depth, result.height, result.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].
    # 改变为height, width, depth
    result.uint8image = tf.transpose(depth_major, [1, 2, 0])

    return result


# 构建一个排列后的一组图片和分类
def _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.
    # 线程数
    num_preprocess_threads = 16
    # 布尔指示是否使用一个shuffling队列
    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:
        # tf.train.batch(tensors, batch_size, num_threads=1, capacity=32,
        # enqueue_many=False, shapes=None, dynamic_pad=False,
        # allow_smaller_final_batch=False, shared_name=None, name=None)
        # 这里是用队列实现,已经默认使用enqueue_runner将enqueue_runner加入到Graph'senqueue_runner集合中
        # 其默认enqueue_many=False时,输入的tensor为一个样本【x,y,z】,输出为Tensor的一批样本
        # capacity:队列中允许最大元素个数
        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.
    # 将训练图片可视化,可拱直接检查图片正误
    tf.summary.image('images', images)

    return images, tf.reshape(label_batch, [batch_size])


# 为CIFAR评价构建输入
# data_dir路径
# batch_size一个组的大小
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.
    # 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.
    # 设置张量的型
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])

    # Ensure that the random shuffling has good mixing properties.
    # 确保洗牌的随机性
    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)

    # 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)


# 为CIFAR评价构建输入
# eval_data使用训练还是评价数据集
# data_dir路径
# batch_size一个组的大小
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.
    # 文件名队列
    # def string_input_producer(string_tensor,
    # num_epochs=None,
    # shuffle=True,
    # seed=None,
    # capacity=32,
    # shared_name=None,
    # name=None,
    # cancel_op=None):
    # 根据上面的函数可以看出下面的这个默认对输入队列进行shuffle,string_input_producer返回的是字符串队列,
    # 使用enqueue_runner将enqueue_runner加入到Graph'senqueue_runner集合中
    filename_queue = tf.train.string_input_producer(filenames)

    # Read examples from files in the filename queue.
    # 从文件队列中读取解析出的图片队列
    # read_cifar10从输入文件名队列中读取一条图像记录
    read_input = read_cifar10(filename_queue)
    # 将记录中的图像记录转换为float32
    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.
    # 将图像裁剪成24*24
    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.
    # 对图像数据进行归一化
    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.
    # 根据当前记录中第一条记录的值,采用多线程的方法,批量读取一个batch中的数据
    return _generate_image_and_label_batch(float_image, read_input.label,
                                           min_queue_examples, batch_size,
                                           shuffle=False)

2. cifar10.py 

"""Builds the CIFAR-10 network.
Summary of available functions:
 # Compute input images and labels for training. If you would like to run
 # evaluations, use inputs() instead.
 inputs, labels = distorted_inputs()
 # Compute inference on the model inputs to make a prediction.
 predictions = inference(inputs)
 # Compute the total loss of the prediction with respect to the labels.
 loss = loss(predictions, labels)
 # Create a graph to run one step of training with respect to the loss.
 train_op = train(loss, global_step)
"""
# pylint: disable=missing-docstring
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os
import re
import sys
import tarfile
import argparse

from six.moves import urllib
import tensorflow as tf

import cifar10_input

parser = argparse.ArgumentParser()

# Basic model parameters.
parser.add_argument('--batch_size', type=int, default=128,
                    help='Number of images to process in a batch.')

parser.add_argument('--data_dir', type=str, default='D:/QQ文件/cifar10-test1/cifar10_data/',
                    help='Path to the CIFAR-10 data directory.')

parser.add_argument('--use_fp16', type=bool, default=False,
                    help='Train the model using fp16.')

FLAGS = parser.parse_args()

# Global constants describing the CIFAR-10 data set.
IMAGE_SIZE = cifar10_input.IMAGE_SIZE
NUM_CLASSES = cifar10_input.NUM_CLASSES
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

# Constants describing the training process.
MOVING_AVERAGE_DECAY = 0.9999  # The decay to use for the moving average.
NUM_EPOCHS_PER_DECAY = 350.0  # Epochs after which learning rate decays.# 衰减呈阶梯函数,控制衰减周期(阶梯宽度)
LEARNING_RATE_DECAY_FACTOR = 0.1  # Learning rate decay factor.# 学习率衰减因子
INITIAL_LEARNING_RATE = 0.1  # Initial learning rate.# 初始学习率

# If a model is trained with multiple GPUs, prefix all Op names with tower_name
# to differentiate the operations. Note that this prefix is removed from the
# names of the summaries when visualizing a model.
TOWER_NAME = 'tower'

DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'

def _activation_summary(x):
    """Helper to create summaries for activations.
    Creates a summary that provides a histogram of activations.
    Creates a summary that measures the sparsity of activations.
    Args:
      x: Tensor
    Returns:
      nothing
    """
    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
    # session. This helps the clarity of presentation on tensorboard.
    tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
    tf.summary.histogram(tensor_name + '/activations', x)
    tf.summary.scalar(tensor_name + '/sparsity',
                      tf.nn.zero_fraction(x))


def _variable_on_cpu(name, shape, initializer):
    """Helper to create a Variable stored on CPU memory.
    Args:
      name: name of the variable
      shape: list of ints
      initializer: initializer for Variable
    Returns:
      Variable Tensor
    """
    with tf.device('/cpu:0'):  # 一个 context manager,用于为新的op指定要使用的硬件
        dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
        var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
    return var


def _variable_with_weight_decay(name, shape, stddev, wd):
    """Helper to create an initialized Variable with weight decay.
    Note that the Variable is initialized with a truncated normal distribution.
    A weight decay is added only if one is specified.
    Args:
      name: name of the variable
      shape: list of ints
      stddev: standard deviation of a truncated Gaussian
      wd: add L2Loss weight decay multiplied by this float. If None, weight
          decay is not added for this Variable.
    Returns:
      Variable Tensor
    """
    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
    var = _variable_on_cpu(
        name,
        shape,
        tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
    if wd is not None:
        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
        tf.add_to_collection('losses', weight_decay)
    return var


def distorted_inputs():
    """Construct distorted input for CIFAR training using the Reader ops.
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    Raises:
      ValueError: If no data_dir
    """
    if not FLAGS.data_dir:
        raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,
                                                    batch_size=FLAGS.batch_size)
    if FLAGS.use_fp16:
        images = tf.cast(images, tf.float16)
        labels = tf.cast(labels, tf.float16)
    return images, labels


def inputs(eval_data):
    """Construct input for CIFAR evaluation using the Reader ops.
    Args:
      eval_data: bool, indicating if one should use the train or eval data set.
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    Raises:
      ValueError: If no data_dir
    """
    if not FLAGS.data_dir:
        raise ValueError('Please supply a data_dir')
    data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
    images, labels = cifar10_input.inputs(eval_data=eval_data,
                                          data_dir=data_dir,
                                          batch_size=FLAGS.batch_size)
    if FLAGS.use_fp16:
        images = tf.cast(images, tf.float16)
        labels = tf.cast(labels, tf.float16)
    return images, labels


# 开始建立网络,第一层卷积层的 weight 不进行 L2正则,因此 kernel(wd) 这一项设为0,建立值为0的 biases,
# conv1的结果由 ReLu 激活,由 _activation_summary() 进行汇总;然后建立第一层池化层,
# 最大池化尺寸和步长不一致可以增加数据的丰富性;最后建立 LRN 层
def inference(images):
    """Build the CIFAR-10 model.
    Args:
      images: Images returned from distorted_inputs() or inputs().
    Returns:
      Logits.
    """
    # We instantiate all variables using tf.get_variable() instead of
    # tf.Variable() in order to share variables across multiple GPU training runs.
    # If we only ran this model on a single GPU, we could simplify this function
    # by replacing all instances of tf.get_variable() with tf.Variable().
    #
    # conv1
    with tf.variable_scope('conv1') as scope:
        kernel = _variable_with_weight_decay('weights',
                                             shape=[5, 5, 3, 64],
                                             stddev=5e-2,
                                             wd=0.0)
        conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
        biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)
        _activation_summary(conv1)

    # pool1
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                           padding='SAME', name='pool1')

    # norm1 局部相响应归一化
    # LRN层模仿了生物神经系统的
    # "侧抑制"
    # 机制,对局部神经元的活动创建竞争环境,使得其中响应比较大的值变得相对更大,并抑制其他反馈较小的神经元,增强了模型的泛化能力,LRN
    # 对Relu 这种没有上限边界的激活函数会比较有用,因为它会从附近的多个卷积核的响应中挑选比较大的反馈,但不适合
    # sigmoid这种有固定边界并且能抑制过大的激活函数。
    norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                      name='norm1')

    # conv2
    # 第二层卷积层与第一层,除了输入参数的改变之外,将 biases 值全部初始化为0.1,
    # 调换最大池化和 LRN 层的顺序,先进行LRN,再使用最大池化层。
    with tf.variable_scope('conv2') as scope:
        kernel = _variable_with_weight_decay('weights',
                                             shape=[5, 5, 64, 64],
                                             stddev=5e-2,
                                             wd=0.0)
        conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
        biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name=scope.name)
        _activation_summary(conv2)

    # norm2
    norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                      name='norm2')
    # pool2
    pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],
                           strides=[1, 2, 2, 1], padding='SAME', name='pool2')

    # local3
    # 第三层全连接层 ,需要先把前面的卷积层的输出结果全部 flatten,
    # 使用 tf.reshape 函数将每个样本都变为一维向量,使用 get_shape 函数获取数据扁平化之后的长度;
    # 然后对全连接层的 weights 和 biases 进行初始化,为了防止全连接层过拟合,设置一个非零的 wd 值0.004,
    # 让这一层的所有参数都被 L2正则所约束,最后依然使用 Relu 激活函数进行非线性化。
    # 同理,可以建立第四层全连接层。
    with tf.variable_scope('local3') as scope:
        # Move everything into depth so we can perform a single matrix multiply.
        reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = _variable_with_weight_decay('weights', shape=[dim, 384],
                                              stddev=0.04, wd=0.004)
        biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
        _activation_summary(local3)

    # local4
    with tf.variable_scope('local4') as scope:
        weights = _variable_with_weight_decay('weights', shape=[384, 192],
                                              stddev=0.04, wd=0.004)
        biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
        _activation_summary(local4)

    # linear layer(WX + b),
    # We don't apply softmax here because
    # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits
    # and performs the softmax internally for efficiency.
    # 最后的 softmax_linear 层,先创建这一层的 weights 和 biases,不添加L2正则化。
    # 在这个模型中,不像之前的例子使用 sotfmax 输出最后的结果,因为将 softmax 的操作放在来计算 loss 的部分,
    # 将 softmax_linear 的线性返回值 logits 与 labels 计算 loss,
    with tf.variable_scope('softmax_linear') as scope:
        weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
                                              stddev=1 / 192.0, wd=0.0)
        biases = _variable_on_cpu('biases', [NUM_CLASSES],
                                  tf.constant_initializer(0.0))
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
        _activation_summary(softmax_linear)

    return softmax_linear


# 损失函数
# 通过 tf.nn.softmax 后的 logits 值(属于每个类别的概率值)
def loss(logits, labels):
    """Add L2Loss to all the trainable variables.
    Add summary for "Loss" and "Loss/avg".
    Args:
      logits: Logits from inference().
      labels: Labels from distorted_inputs or inputs(). 1-D tensor
              of shape [batch_size]
    Returns:
      Loss tensor of type float.
    """
    # Calculate the average cross entropy loss across the batch.
    labels = tf.cast(labels, tf.int64)

    # 在 CIFAR-10 中,labels 的 shape 为 [batch_size],每个样本的 label 为0到9的一个数,代表10个分类,
    # 这些类之间是相互排斥的,每个 CIFAR-10 图片只能被标记为唯一的一个标签:一张图片可能是一只狗或一辆卡车,而不能两者都是。
    # 因此我们需要对 label 值 onehot encoding,转化过程比较繁琐,
    # 新版的 TensorFlow API 支持对唯一值 labels 的 sparse_to_dense,只需要一步:
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=labels, logits=logits, name='cross_entropy_per_example')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')

    # 这里的 labels 的 shape 为 [batch_size, 1]。
    # 再使用 tf.add_to_collection 把 cross entropy 的 loss 添加到整体 losses 的 collection 中。
    #  最后,使用 tf.add_n 将整体 losses 的 collection中 的全部 loss 求和,得到最终的 loss 并返回,
    # 其中包含 cross entropy loss,还有后两个全连接层中的 weight 的 L2 loss
    tf.add_to_collection('losses', cross_entropy_mean)

    # The total loss is defined as the cross entropy loss plus all of the weight
    # decay terms (L2 loss).
    return tf.add_n(tf.get_collection('losses'), name='total_loss')


def _add_loss_summaries(total_loss):
    """Add summaries for losses in CIFAR-10 model.
    Generates moving average for all losses and associated summaries for
    visualizing the performance of the network.
    Args:
      total_loss: Total loss from loss().
    Returns:
      loss_averages_op: op for generating moving averages of losses.
    """
    # Compute the moving average of all individual losses and the total loss.
    # 创建一个新的指数滑动均值对象
    loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')

    # 从字典集合中返回关键字'losses'对应的所有变量,包括交叉熵损失和正则项损失
    losses = tf.get_collection('losses')

    # 创建'shadow variables'并添加维护滑动均值的操作
    # apply() 方法会添加 trained variables 的 shadow copies,并添加操作来维护变量的滑动均值到 shadow copies。
    # 滑动均值是通过指数衰减计算得到的,shadow variable 的初始化值和 trained variables 相同,
    # 其更新公式为 shadow_variable = decay * shadow_variable + (1 - decay) * variable。
    loss_averages_op = loss_averages.apply(losses + [total_loss])

    # Attach a scalar summary to all individual losses and the total loss; do the
    # same for the averaged version of the losses.
    for l in losses + [total_loss]:
        # Name each loss as '(raw)' and name the moving average version of the loss
        # as the original loss name.
        tf.summary.scalar(l.op.name + ' (raw)', l)
        tf.summary.scalar(l.op.name, loss_averages.average(l))

    return loss_averages_op


def train(total_loss, global_step):
    """Train CIFAR-10 model.
    Create an optimizer and apply to all trainable variables. Add moving
    average for all trainable variables.
    Args:
      total_loss: Total loss from loss().
      global_step: Integer Variable counting the number of training steps
        processed.
    Returns:
      train_op: op for training.
    """
    # Variables that affect learning rate.
    num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size
    decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)

    # Decay the learning rate exponentially based on the number of steps.
    # 首先定义学习率(learning rate),并设置随迭代次数衰减,并进行 summary:
    lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,
                                    global_step,
                                    decay_steps,
                                    LEARNING_RATE_DECAY_FACTOR,
                                    staircase=True)
    tf.summary.scalar('learning_rate', lr)

    # Generate moving averages of all losses and associated summaries.
    # 对 loss 生成滑动均值和汇总,通过使用指数衰减,来维护变量的滑动均值(Moving Average)。
    # 当训练模型时,维护训练参数的滑动均值是有好处的,在测试过程中使用滑动参数比最终训练的参数值本身,会提高模型的实际性能即准确率。
    loss_averages_op = _add_loss_summaries(total_loss)  # 损失变量的更新操作

    # Compute gradients.
    # 定义训练方法与目标,tf.control_dependencies 是一个 context manager,控制节点执行顺序,先执行[ ]中的操作,再执行 context 中的操作:
    with tf.control_dependencies([loss_averages_op]):
        opt = tf.train.GradientDescentOptimizer(lr)  # 优化器  随机梯度下降法
        grads = opt.compute_gradients(total_loss)  # 返回计算出的(gradient, variable) pairs

    # Apply gradients.
    # 返回一步梯度更新操作
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    # Add histograms for trainable variables.
    for var in tf.trainable_variables():
        tf.summary.histogram(var.op.name, var)

    # Add histograms for gradients.
    for grad, var in grads:
        if grad is not None:
            tf.summary.histogram(var.op.name + '/gradients', grad)

    # Track the moving averages of all trainable variables.
    # 最后,动态调整衰减率,返回模型参数变量的滑动更新操作即 train op:
    variable_averages = tf.train.ExponentialMovingAverage(
        MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())

    with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
        train_op = tf.no_op(name='train')

    return train_op


def maybe_download_and_extract():
    """Download and extract the tarball from Alex's website."""
    dest_directory = FLAGS.data_dir
    if not os.path.exists(dest_directory):
        os.makedirs(dest_directory)
    filename = DATA_URL.split('/')[-1]
    filepath = os.path.join(dest_directory, filename)
    print(filepath)
    if not os.path.exists(filepath):
        def _progress(count, block_size, total_size):
            sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
                                                             float(count * block_size) / float(total_size) * 100.0))
            sys.stdout.flush()

        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
        print()
        statinfo = os.stat(filepath)
        print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
    extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
    if not os.path.exists(extracted_dir_path):
        tarfile.open(filepath, 'r:gz').extractall(dest_directory)

3. cifar10_train.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import time

import tensorflow as tf

import cifar10

parser = cifar10.parser

parser.add_argument('--train_dir', type=str, default='cifar10_train/',
                    help='Directory where to write event logs and checkpoint.')

parser.add_argument('--max_steps', type=int, default=1000000,
                    help='Number of batches to run.')

parser.add_argument('--log_device_placement', type=bool, default=False,
                    help='Whether to log device placement.')

parser.add_argument('--log_frequency', type=int, default=10,
                    help='How often to log results to the console.')


def train():
    """Train CIFAR-10 for a number of steps."""
    # 指定当前图为默认graph
    with tf.Graph().as_default():
        # 设置trainable=False,是因为防止训练过程中对global_step变量也进行滑动更新操作 global_step = tf.Variable(0, trainable=False)
        global_step = tf.train.get_or_create_global_step()

        # Get images and labels for CIFAR-10.
        # Force input pipeline to CPU:0 to avoid operations sometimes ending up on
        # GPU and resulting in a slow down.
        with tf.device('/cpu:0'):
            images, labels = cifar10.distorted_inputs()

        # Build a Graph that computes the logits predictions from the
        # inference model.
        logits = cifar10.inference(images)

        # Calculate loss.
        loss = cifar10.loss(logits, labels)

        # Build a Graph that trains the model with one batch of examples and
        # updates the model parameters.
        train_op = cifar10.train(loss, global_step)

        class _LoggerHook(tf.train.SessionRunHook):
            """Logs loss and runtime."""

            def begin(self):
                self._step = -1
                self._start_time = time.time()

            def before_run(self, run_context):
                self._step += 1
                return tf.train.SessionRunArgs(loss)  # Asks for loss value.

            def after_run(self, run_context, run_values):
                if self._step % FLAGS.log_frequency == 0:
                    current_time = time.time()
                    duration = current_time - self._start_time
                    self._start_time = current_time

                    loss_value = run_values.results
                    examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
                    sec_per_batch = float(duration / FLAGS.log_frequency)

                    format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                                  'sec/batch)')
                    print(format_str % (datetime.now(), self._step, loss_value,
                                        examples_per_sec, sec_per_batch))

        with tf.train.MonitoredTrainingSession(
                checkpoint_dir=FLAGS.train_dir,
                hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
                       tf.train.NanTensorHook(loss),
                       _LoggerHook()],
                config=tf.ConfigProto(
                    log_device_placement=FLAGS.log_device_placement)) as mon_sess:
            while not mon_sess.should_stop():
                mon_sess.run(train_op)


def main(argv=None):  # pylint: disable=unused-argument
    cifar10.maybe_download_and_extract()
    if tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.DeleteRecursively(FLAGS.train_dir)
    tf.gfile.MakeDirs(FLAGS.train_dir)
    train()


if __name__ == '__main__':
    FLAGS = parser.parse_args()
    tf.app.run()

# tensorboard  --logdir=D:\tmp\cifar10_train

4. cifar10_eval.py 

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import math
import time

import numpy as np
import tensorflow as tf

import cifar10

parser = cifar10.parser

parser.add_argument('--eval_dir', type=str, default='/cifar10_eval',
                    help='Directory where to write event logs.')

parser.add_argument('--eval_data', type=str, default='test',
                    help='Either `test` or `train_eval`.')

parser.add_argument('--checkpoint_dir', type=str, default='/cifar10_train',
                    help='Directory where to read model checkpoints.')

parser.add_argument('--eval_interval_secs', type=int, default=60 * 5,
                    help='How often to run the eval.')

parser.add_argument('--num_examples', type=int, default=10000,
                    help='Number of examples to run.')

parser.add_argument('--run_once', type=bool, default=False,
                    help='Whether to run eval only once.')


# cifar10_train.py 会周期性的在检查点文件中保存模型中的所有参数,但是不会对模型进行评估。
# cifar10_eval.py 会使用该检查点文件在另一部分数据集上测试预测性能。
# 利用 inference() 函数重构模型,并使用了在评估数据集所有10,000张 CIFAR-10 图片进行测试。
# 最终计算出的精度为 1 : N,N = 预测值中置信度最高的一项与图片真实 label 匹配的频次。
# 为了监控模型在训练过程中的改进情况,评估用的脚本文件会周期性的在最新的检查点文件上运行,
# 这些检查点文件是由上述的 cifar10_train.py 产生
def eval_once(saver, summary_writer, top_k_op, summary_op):
    """Run Eval once.
    Args:
      saver: Saver.
      summary_writer: Summary writer.
      top_k_op: Top K op.
      summary_op: Summary op.
    """
    with tf.Session() as sess:
        ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
        if ckpt and ckpt.model_checkpoint_path:
            # Restores from checkpoint
            saver.restore(sess, ckpt.model_checkpoint_path)
            # Assuming model_checkpoint_path looks something like:
            #   /my-favorite-path/cifar10_train/model.ckpt-0,
            # extract global_step from it.
            global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
        else:
            print('No checkpoint file found')
            return

        # Start the queue runners.
        coord = tf.train.Coordinator()
        try:
            threads = []
            for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
                threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
                                                 start=True))

            num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
            true_count = 0  # Counts the number of correct predictions.
            total_sample_count = num_iter * FLAGS.batch_size
            step = 0
            while step < num_iter and not coord.should_stop():
                predictions = sess.run([top_k_op])
                true_count += np.sum(predictions)
                step += 1

            # Compute precision @ 1.
            precision = true_count / total_sample_count
            print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))

            summary = tf.Summary()
            summary.ParseFromString(sess.run(summary_op))
            summary.value.add(tag='Precision @ 1', simple_value=precision)
            summary_writer.add_summary(summary, global_step)
        except Exception as e:  # pylint: disable=broad-except
            coord.request_stop(e)

        coord.request_stop()
        coord.join(threads, stop_grace_period_secs=10)


def evaluate():
    """Eval CIFAR-10 for a number of steps."""
    with tf.Graph().as_default() as g:
        # Get images and labels for CIFAR-10.
        eval_data = FLAGS.eval_data == 'test'
        images, labels = cifar10.inputs(eval_data=eval_data)

        # Build a Graph that computes the logits predictions from the
        # inference model.
        logits = cifar10.inference(images)

        # Calculate predictions.
        top_k_op = tf.nn.in_top_k(logits, labels, 1)

        # Restore the moving average version of the learned variables for eval.
        variable_averages = tf.train.ExponentialMovingAverage(
            cifar10.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.summary.merge_all()

        summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)

        while True:
            eval_once(saver, summary_writer, top_k_op, summary_op)
            if FLAGS.run_once:
                break
            time.sleep(FLAGS.eval_interval_secs)


def main(argv=None):  # pylint: disable=unused-argument
    cifar10.maybe_download_and_extract()
    if tf.gfile.Exists(FLAGS.eval_dir):
        tf.gfile.DeleteRecursively(FLAGS.eval_dir)
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    evaluate()


if __name__ == '__main__':
    FLAGS = parser.parse_args()
    tf.app.run()

# 在训练脚本会为所有学习变量计算其滑动均值(Moving Average),
# 评估脚本则直接将所有学习到的模型参数替换成对应的滑动均值,这一替代方式可以在评估过程中提升模型的性能。

# tensorboard  --logdir=D:\tmp\cifar10_train

运行结果

cifar10_train.py的运行结果 :

cifar10数据集训练

TensorBoard的可视化:

cifar10数据集训练

遇到的问题 && 解决方法 

1. 问题一:pycharm找不到数据文件 
错误信息: ValueError: Failed to find file: cifar10_data/cifar-10-batches-bin\data_batc 
错误原因: 
看到下面博客中的解释,发现是因为预先定义了cifar-10的存储路径,默认路径是/tmp/cifar10_train, 所以运行时找不到文件。

cifar10数据集训练
解决方法: 
(1)将cifar10_train.py中第14行的默认路径改为了   ‘cifar10_train/’

cifar10数据集训练
(2)将cifar10.py中第36行的默认路径改成了cifar10数据存储的绝对路径,例:’D:/QQ文件/cifar10test1/cifar10_data/’
(然后就可以运行了)

cifar10数据集训练
  2. 问题二:tensorboard的网址无法打开 
在命令行输入tensorboard –logdir cifar10_train/可以查看训练进度, 其中 –logdir cifar10_train/ 表 示模型训练日志保存的位置:

cifar10数据集训练
该网址谷歌浏览器无法打开,解决方法:在原命令语句后加上” –host=127.0.0.1″,这样返回的地址也 变成完整的127.0.0.1:6006,再到浏览器将地址输入,即可正常打开网页。 重新在命令行输入:tensorboard –logdir cifar10_train/ –host=127.0.0.1

cifar10数据集训练

参考

https://blog.csdn.net/barry_j/article/details/79252438
https://www.cnblogs.com/gangzhucoll/p/12778246.html 

https://blog.csdn.net/wang_kmin/article/details/81637816

https://www.cnblogs.com/YouXiangLiThon/articles/7246169.html

https://blog.csdn.net/qq_30377909/article/details/89946818

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