CIFAR-10模型训练python版cifar10数据集

在之前的博客中已经对CIFAR-10做了整体的解析，但是目前从tensorflow/models/tree/master/tutorials/image/cifar10中下载下来，运行cifar10_train.py后训练的是binary(适用于C语言)版的数据集。

那么想训练CIFAR-10 python version数据集该怎么修改代码呢？

其实主要需要修改的部分是cifar10_input.py文件。因为python版本的数据集形式不相同，具体格式请上Alex官网的The CIFAR-10 dataset去了解。因为格式不同，导入数据集的代码部分对于数据集的解析也就不相同。python版如下：

def unpickle(file):import picklewith open(file, 'rb') as fo:dict = pickle.load(fo, encoding='bytes')return dict

这里就不罗嗦啦，直接向大家奉上整个代码：

from __future__ import print_function
import os
import tensorflow as tf
import pickle as pickle
import numpy as npfrom PIL import Image#encoding:utf-8
from scipy import ndimage# Global constants describing the CIFAR-10 data set
# CIFAR10 image size of 32x32. will distort to 24x24
IMAGE_SIZE = 24NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000# train_data_queue = None
# train_labels_queue = None
# train_f_names_queue = None#读取数据集中的各个文件，按照类型生成相应格式，或列表 或矩阵
def read_cifar10_python_pickles(filenames):data = Nonelabels = Nonef_names = None# Dict Keys from pickle files# ['data', 'labels', 'batch_label', 'filenames']"""filenames = [os.path.join(data_dir, 'data_batch_%d' % i)for i in xrange(1, 6)]"""for pickle_file in filenames:if not tf.gfile.Exists(pickle_file):raise ValueError('Failed to find file: ' + pickle_file)with open(pickle_file, 'rb') as p:# pickle.load(file,*,fix_imports=True, encoding="ASCII", errors="strict")#必填参数file必须以二进制可读模式打开，即“rb”，其他都为可选参数save = pickle.load(p,encoding='iso-8859-1')s_data = save['data']s_labels = np.array(save['labels'])s_f_names = np.array(save['filenames'])# 删除列表del saveprint('data set', s_data.shape, s_labels.shape)#numpy提供了numpy.append(arr, values, axis=None)函数。对于参数规定，# 要么一个数组和一个数值；要么两个数组，不能三个及以上数组直接append拼接。append函数返回的始终是一个一维数组。data = np.append(data, s_data, axis=0) if data is not None else s_datalabels = np.append(labels, s_labels, axis=0) if labels is not None else s_labelsf_names = np.append(f_names, s_f_names, axis=0) if f_names is not None else s_f_namesprint('Data set: ', data.shape, len(labels))return data, labels, f_namesdef read_cifar10_python_pickle(filename):if not tf.gfile.Exists(filename):raise ValueError('Failed to find file: ' + filename)with open(filename, 'rb') as p:save = pickle.load(p,encoding='iso-8859-1')data = save['data']labels = np.array(save['labels'])f_names = np.array(save['filenames'])del saveprint('data set', data.shape, labels.shape)return data, labels, f_namesdef read_cifar10_to_queue(filenames):data, labels, f_names = read_cifar10_python_pickles(filenames)# def input_producer(input_tensor,#                    element_shape=None,#                    num_epochs=None,#                    shuffle=True,#                    seed=None,#                    capacity=32,#                    shared_name=None,#                    summary_name=None,#                    name=None,#                    cancel_op=None):#这个地方是将数据按照类型作用进行生成队列data_queue = tf.train.input_producer(data, shuffle=False)labels_queue = tf.train.input_producer(labels, shuffle=False)f_names_queue = tf.train.input_producer(f_names, shuffle=False)return data_queue, labels_queue, f_names_queuedef read_cifar10_reader(data_q, labels_q):#dequeue，函数名，用于移除每个匹配元素的指定队列中的第一个函数，并执行被移除的函数。#将元素从队列中移出。如果在执行该操作时队列已空，#那么将会阻塞直到元素出列，返回出列的tensors的tuplereturn data_q.dequeue(), labels_q.dequeue()def read_cifar10(filenames):class CIFAR10Record(object):passresult = CIFAR10Record()label_bytes = 1  # 2 for CIFAR-100result.height = 32result.width = 32result.depth = 3data_q, label_q, _ = read_cifar10_to_queue(filenames)data, label = read_cifar10_reader(data_q, label_q)print(data.get_shape(), data.dtype)print(label.get_shape(), label.dtype)result.label = tf.cast(label, tf.int32)#uint8转变成int32数据类型depth_major = tf.reshape(data, [result.depth, result.height, result.width])# Convert from [depth, height, width] to [height, width, depth].result.uint8image = tf.transpose(depth_major, [1, 2, 0])print(depth_major.get_shape(), depth_major.dtype)print(result.label.get_shape(), result.label.dtype)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.int32min_queue_examples: int32, minimum number of samples to retainin 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 = 16if 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.#tf.image_summary('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' % i) for i in range(1,6)]for f in filenames:if not tf.gfile.Exists(f):raise ValueError('Failed to find file: ' + f)read_input = read_cifar10(filenames)reshaped_image = tf.cast(read_input.uint8image, tf.float32)height = IMAGE_SIZEwidth = 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)# Ensure that the random shuffling has good mixing properties.# min_queue_examples: train(50000*0.4=20000) eval(10000*0.4=4000)min_fraction_of_examples_in_queue = 0.4min_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' % i) for i in range(1, 6)]num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAINelse:filenames = [os.path.join(data_dir, 'test_batch')]num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVALfor f in filenames:if not tf.gfile.Exists(f):raise ValueError('Failed to find file: ' + f)read_input = read_cifar10(filenames)reshaped_image = tf.cast(read_input.uint8image, tf.float32)height = IMAGE_SIZEwidth = IMAGE_SIZEresized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,width, height)float_image = tf.image.per_image_whitening(resized_image)min_fraction_of_examples_in_queue = 0.4min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue)return _generate_image_and_label_batch(float_image, read_input.label,min_queue_examples, batch_size,shuffle=False)def test_inputs(data_dir):filenames = [os.path.join(data_dir, 'test_batch')]for f in filenames:if not tf.gfile.Exists(f):raise ValueError('Failed to find file: ' + f)read_input = read_cifar10(filenames)reshaped_image = tf.cast(read_input.uint8image, tf.float32)height = IMAGE_SIZEwidth = IMAGE_SIZEresized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,width, height)float_image = tf.image.per_image_whitening(resized_image)return_image = tf.reshape(float_image, [-1, height, width, 3])return return_image, read_input.labeldef test_input_process(image, label=None):reshaped_image = tf.cast(image, tf.float32)if label is not None:label = tf.cast(label, tf.int32)height = IMAGE_SIZEwidth = IMAGE_SIZEresized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,width, height)float_image = tf.image.per_image_whitening(resized_image)return_image = tf.reshape(float_image, [-1, height, width, 3])return return_image, labeldef read_n_preprocess_external_input(image_file):temp_image = Image.open(image_file)height = temp_image.size[1]width = temp_image.size[0]if height > width:temp_image = temp_image.crop((0, (height - width)/2, width, (height + width)/2))elif height < width:temp_image = temp_image.crop(((width - height)/2, 0, (height + width)/2, height))temp_image.thumbnail((32, 32), Image.ANTIALIAS)temp_image_arr = np.array(temp_image)return temp_image_arr

cifar10.py修改后:

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================="""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_functionimport argparse
import os
import re
import sys
import tarfilefrom six.moves import urllib
import tensorflow as tfimport cifar10_inputparser = 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='/tmp/cifar10_data_py',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 = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.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: TensorReturns: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 variableshape: list of intsinitializer: initializer for VariableReturns: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.float32var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)return vardef _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 variableshape: list of intsstddev: standard deviation of a truncated Gaussianwd: add L2Loss weight decay multiplied by this float. If None, weightdecay is not added for this Variable.Returns:Variable Tensor"""dtype = tf.float16 if FLAGS.use_fp16 else tf.float32var = _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 vardef 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-py')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, labelsdef 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-py')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().## conv1with 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)# pool1pool1 = 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)# norm2norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,name='norm2')# pool2pool2 = 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].valueweights = _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)# local4with 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 tensorof 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 losstf.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 forvisualizing 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_opdef train(total_loss, global_step):"""Train CIFAR-10 model.Create an optimizer and apply to all trainable variables. Add movingaverage for all trainable variables.Args:total_loss: Total loss from loss().global_step: Integer Variable counting the number of training stepsprocessed.Returns:train_op: op for training."""# Variables that affect learning rate.num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_sizedecay_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_opdef maybe_download_and_extract():"""Download and extract the tarball from Alex's website."""dest_directory = FLAGS.data_dirif not os.path.exists(dest_directory):os.makedirs(dest_directory)filename = DATA_URL.split('/')[-1]filepath = os.path.join(dest_directory, filename)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-py')if not os.path.exists(extracted_dir_path):tarfile.open(filepath, 'r:gz').extractall(dest_directory)

cifar10_train.py修改后：

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================="""A binary to train CIFAR-10 using a single GPU.Accuracy:
cifar10_train.py achieves ~86% accuracy after 100K steps (256 epochs of
data) as judged by cifar10_eval.py.Speed: With batch_size 128.System        | Step Time (sec/batch)  |     Accuracy
------------------------------------------------------------------
1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)
1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)Usage:
Please see the tutorial and website for how to download the CIFAR-10
data set, compile the program and train the model.http://tensorflow.org/tutorials/deep_cnn/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionfrom datetime import datetime
import timeimport tensorflow as tfimport cifar10parser = cifar10.parserparser.add_argument('--train_dir', type=str, default='/tmp/cifar10_data_py/cifar10_train',help='Directory where to write event logs and checkpoint.')parser.add_argument('--max_steps', type=int, default=10000,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graphwith 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 = -1self._start_time = time.time()def before_run(self, run_context):self._step += 1return 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_timeself._start_time = current_timeloss_value = run_values.resultsexamples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / durationsec_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-argumentcifar10.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_data_py/cifar10_train

注意，路径部分问题以及之前训练后的cifar10_train文件夹已存在导致出错只有将路径改对，并且删除之前的训练生成的文件夹就可以运行啦。

参考文献链接：

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

https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10

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那么想训练CIFAR-10 python version数据集该怎么修改代码呢？

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