tensorflow之CNN进阶cifar10实现

之前一篇文章有写到简单到两层卷积神经网络（http://blog.csdn.net/xuan_zizizi/article/details/77816745）完成mnist手写数据集的识别，正确率达到96%以上。这篇文章将采用经典的CIFAR-10数据集，包含60000张32×32的彩色图像，其中共10类物体，每一类6000张。参看《tensorflow实战》
1.下载tensorflow models库，在终端进行操作

sudo apt install git #安装git，若有则无需安装git clone https://github.com/tensorflow/models.gitcd models/tutorials/image/cifar10

2.载入需要的库，在.py文件中操作

import cifar10import cifar10_inputimport tensorflow as tfimport numpy as npimport time 

3.定义迭代参数

max_steps = 3000 #最大迭代次数batch_size = 128  data_dir = '/home/chunmei/tmp/cifar10_data/cifar-10-batches-bin'#文件下载解压后的路径

4.定义初始化权值函数

def variable_with_weight_loss(shape, stddev, w1):    var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))#截断到正态分布来初始化权重    if w1 is not None:    #w1控制L2正则化的大小        weight_loss = tf.multiply(tf.nn.l2_loss(var), w1, name='weight_loss')#L2正则化权值后再和w1相乘，用w1控制L2loss        tf.add_to_collection('losses',weight_loss)#储存weight_loss到名为'loses'的collection上面    return var

正则化用于惩罚特征权重，即特征权重为模型损失函数的一部分。一般，L1正则化可以理解为制造稀疏特征，即大部分无用特征被置为0，而L2正则化则是让特征的权重不要过大，使得特征权重较为平均。
5.使用cifar10下载数据集并解压展开到默认位置。

cifar10.maybe_download_and_extract()#下载数据集#训练集images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=batch_size)#cifar10_input类中带的distorted_inputs()函数可以产生训练需要的数据，包括特征和label，返回封装好的tensor，每次执行都会生成一个batch_size大小的数据。#测试集images_test, labels_test = cifar10_input.inputs(eval_data = True, data_dir=data_dir, batch_size=batch_size)

数据增强函数cifar10_input.distorted_inputs()的操作包括：
随机的水平旋转（tf.image.random_flip_left_right）
随机剪切一块24×24大小的图片(tf.random_crop)
设置随机的亮度和对比度(tf.image.random_brightness、tf.image.random_contrast)
数据标准化：
tf.image.per_image_whitening，对数据减去均值，除以方差，保证数据均值为0，方差为1。
6.输入数据

image_in = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])#裁剪后尺寸为24×24，彩色图像通道数为3label_in = tf.placeholder(tf.float32, [batch_size])

7.第一个卷积层
首先设置卷积权值，进行卷积，加上偏置，然后进行ReLU非线性处理，然后进行max_pooling，最后加一个LRN（Local Response Nomalization，局部响应归一化），模仿了生物系统的’侧抑制’机制，对局部神经元的活动创建竞争环境，使得相对较大的权值更大，并抑制其他相对较小的神经元，增强模型的泛化能力。

weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64],stddev=5e-2, w1=0.0)#5×5的卷积和，3个通道，64个滤波器kernel1 = tf.nn.conv2d(image_in, weight1, strides=[1, 1, 1, 1], padding = 'SAME')#卷积1bias1 = tf.Variable(tf.constant(0.0, shape=[64]))conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1))pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')#same?尺寸？norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)

8.第二个卷积层
首先设置卷积权值，进行卷积，加上偏置，然后进行ReLU非线性处理，然后进行LRN，最后加一个进行max_pooling。

weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64],stddev=5e-2, w1=0.0)#5×5的卷积和，第一个卷积层输出64个通道，64个滤波器kernel2 = tf.nn.conv2d(norm1, weight2, strides=[1, 1, 1, 1], padding = 'SAME')#卷积1bias2 = tf.Variable(tf.constant(0.1, shape=[64]))#此处bias初始化为0.1conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2))norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')#same?尺寸？

9.全连接层1
将第二个卷积层的输出进行flatten，然后输入一个全连接层，全连接层隐含节点为384，然后还是经过一个ReLU非线性处理。

reshape = tf.reshape(pool2, [batch_size, -1])#将数据变为1D数据dim = reshape.get_shape()[1].value#获取维度weight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, w1=0.004)bias3 = tf.Variable(tf.constant(0.1, shape=[384]))#此处bias初始化为0.1local3 = tf.nn.relu(tf.matmul(reshape,  weight3) + bias3))

10.全连接层2

weight4 = variable_with_weight_loss(shape=[384, 192], stddev=0.04, w1=0.004)bias4 = tf.Variable(tf.constant(0.1, shape=[192]))#此处bias初始化为0.1local4 = tf.nn.relu(tf.matmul(local3,  weight4) + bia4))

11.最后一层

weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1/199.0, w1=0.0)bias5 = tf.Variable(tf.constant(0.0, shape=[10]))logits = tf.add(tf.matmul(local4, weight5), bias5)

12.网络结构

layer名称 描述 conv1 卷积层和ReLU pool1 最大池化 norm1 LRN conv2 卷积层和ReLU norm2 LRN pool2 最大池化 local3 全连接层和ReLU local4 全连接层和ReLU logits 模型的inference的输出结果

13.计算softmax和loss

def loss(logits, labels):    labels = tf.cast(labels, tf.int64)    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='cross_entropy_per_example')    #softmax和cross entropy loss的计算合在一起    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')    #计算cross entropy 均值    tf.add_to_collection('losses', cross_entropy_mean)    #将整体losses的collection中的全部loss求和，得到最终的loss，其中包括cross entropy loss，还有后两个全连接层中weight的L2 loss    return tf.add_n(tf.get_collection('losses'), name = 'total_loss')

14.数据准备

loss = loss(logits, labels_i)  #传递误差和labelstrain_op = tf.train.AdamOptimizer(1e-3).minimize(loss) #优化器top_k_op = tf.nn.in_top_k(logits, label_in, 1) #得分最高的那一类的准确率sess = tf.InteractiveSession()tf.global_variables_initializer().run()#初始化变量tf.train.start_queue_runners()#启动线程，在图像数据增强队列例使用了16个线程进行加速。

15.训练

for step in range(max_steps)    start_time = time.time()    image_batch, label_batch = sess.run([images_train, labels_train])    free, loss_value = sess.run([train_op, loss], feed_dict = {image_in:  image_batch, label_in: label_batch})    duration = time.time() - start_time    if step %10 == 0:        example_per_sec = batch_size/duration        sec_per_batch = float(duration)        format_str = ('step %d, loss=%.2f(%.1f exaples/sec; %.3f sec/batch)')        print(format_str % (step, loss_value, example_per_sec, sec_per_batch))

16.测试模型准确率

num_examples = 1000import mathnum_iter = int(math.ceil(num_examples / batch_size))true_count = 0total_sample_count = num_iter * batch_sziestep = 0while step < num_iter:    image_batch, label_batch = sess.run([images_test, labels_test])    predictions = sess.run([top_k_op],feed_dict={image_in: image_batch, label_in: label_batch})    true_count += np.sum(predictions)    step +=1

17.打印准确率

precision = true_count / total_sample_countprint('precision @ 1 = %.3f' % precision)

18.程序综合

##载入库import tensorflow as tfimport timeimport numpy as npimport cifar10import cifar10_input##定义迭代参数max_steps = 3000 #最大迭代次数batch_size = 128  data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'#默认下载路径/home/zcm/tensorf/test/cifar10/cifar10_data/cifar-10-batches-bin/cifar-10-batches-py##定义初始化权值函数def variable_with_weight_loss(shape, stddev, w1):    var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))#截断到正态分布来初始化权重    if w1 is not None:    #w1控制L2正则化的大小        weight_loss = tf.multiply(tf.nn.l2_loss(var), w1, name='weight_loss')        #L2正则化权值后再和w1相乘，用w1控制L2loss        tf.add_to_collection('losses',weight_loss)        #储存weight_loss到名为'loses'的collection上面    return var##使用cifar10下载数据集并解压展开到默认位置cifar10.maybe_download_and_extract()#下载数据集#训练集images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=batch_size)#cifar10_input类中带的distorted_inputs()函数可以产生训练需要的数据，包括特征和label，返回封装好的tensor，每次执行都会生成一个batch_size大小的数据。#测试集images_test, labels_test = cifar10_input.inputs(eval_data = True, data_dir=data_dir, batch_size=batch_size)##载入数据image_in = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])#裁剪后尺寸为24×24，彩色图像通道数为3label_in = tf.placeholder(tf.int32, [batch_size])##第一个卷积层weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64],stddev=5e-2,w1=0.0)#5×5的卷积和，3个通道，64个滤波器kernel1 = tf.nn.conv2d(image_in, weight1, strides=[1, 1, 1, 1], padding = 'SAME')#卷积1bias1 = tf.Variable(tf.constant(0.0, shape=[64]))conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1))pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')#same?尺寸？norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)##第二个卷积层weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64],stddev=5e-2,w1=0.0)#5×5的卷积和，第一个卷积层输出64个通道，64个滤波器kernel2 = tf.nn.conv2d(norm1, weight2, strides=[1, 1, 1, 1], padding = 'SAME')#卷积1bias2 = tf.Variable(tf.constant(0.1, shape=[64]))#此处bias初始化为0.1conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2))norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001/9.0, beta=0.75)pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')#same?尺寸？print (pool2.shape)##全连接层1reshape = tf.reshape(pool2, [batch_size, -1])#将数据变为1D数据dim = reshape.get_shape()[1].value#获取维度print (dim)weight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, w1=0.004)bias3 = tf.Variable(tf.constant(0.1, shape=[384]))#此处bias初始化为0.1local3 = tf.nn.relu(tf.matmul(reshape,weight3)+bias3)##全连接层2weight4 = variable_with_weight_loss(shape=[384, 192], stddev=0.04, w1=0.004)bias4 = tf.Variable(tf.constant(0.1, shape=[192]))#此处bias初始化为0.1local4 = tf.nn.relu(tf.matmul(local3,  weight4) + bias4)##最后一层weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1/199.0, w1=0.0)bias5 = tf.Variable(tf.constant(0.0, shape=[10]))logits = tf.add(tf.matmul(local4, weight5), bias5)##计算softmax和lossdef loss(logits, labels):    labels = tf.cast(labels, tf.int64)    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='cross_entropy_per_example')    #softmax和cross entropy loss的计算合在一起    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')    #计算cross entropy 均值    tf.add_to_collection('losses', cross_entropy_mean)    #将整体losses的collection中的全部loss求和，得到最终的loss，其中包括cross entropy loss，还有后两个全连接层中weight的L2 loss    return tf.add_n(tf.get_collection('losses'), name = 'total_loss')##数据准备loss = loss(logits, label_in)  #传递误差和labeltrain_op = tf.train.AdamOptimizer(1e-3).minimize(loss) #优化器top_k_op = tf.nn.in_top_k(logits, label_in, 1) #得分最高的那一类的准确率sess = tf.InteractiveSession()tf.global_variables_initializer().run()#初始化变量tf.train.start_queue_runners()#启动线程，在图像数据增强队列例使用了16个线程进行加速。##训练for step in range(max_steps):    start_time = time.time()    image_batch, label_batch = sess.run([images_train, labels_train])    free, loss_value = sess.run([train_op, loss], feed_dict = {image_in: image_batch, label_in: label_batch})    duration = time.time() - start_time #运行时间    if step %10 == 0:        example_per_sec = batch_size/duration#每秒训练样本数        sec_per_batch = float(duration) #每个batch时间        format_str = ('step %d, loss=%.2f(%.1f exaples/sec; %.3f sec/batch)')        print(format_str % (step, loss_value, example_per_sec, sec_per_batch))##测试模型准确率num_examples = 1000import mathnum_iter = int(math.ceil(num_examples / batch_size))#math.ceil()为向上取整true_count = 0total_sample_count = num_iter * batch_sizestep = 0while step < num_iter:    image_batch, label_batch = sess.run([images_test, labels_test])    predictions = sess.run([top_k_op],feed_dict={image_in: image_batch, label_in: label_batch})##打印准确率    true_count += np.sum(predictions)    step +=1precision = true_count / total_sample_countprint('precision @ 1 = %.3f' % precision)

19.结论
在max_steps=3000, batch_size=128时，正确率为73.4% 左右，每次运行结果随机，增加max_steps=5000，batch_size=200，正确率可以达到76.9%。
20.cifar10的python文件，出现在上述的import,这是我直接在网上下载的，来自于tensorflow的models
(1)cifar10_input.py

# 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.# =============================================================================="""Routine for decoding the CIFAR-10 binary file format."""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 = 32  result.width = 32  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.  # 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)  # 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)

(2)cifar10.py

# 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-docstringfrom __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport osimport reimport sysimport tarfilefrom six.moves import urllibimport 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',                    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_SIZENUM_CLASSES = cifar10_input.NUM_CLASSESNUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAINNUM_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.LEARNING_RATE_DECAY_FACTOR = 0.1  # Learning rate decay factor.INITIAL_LEARNING_RATE = 0.1       # Initial learning rate.# 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-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'):    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32    var = 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 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 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-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, 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-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, labelsdef 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  norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,                    name='norm1')  # conv2  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  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.  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_lineardef 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)  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')  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.  loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')  losses = tf.get_collection('losses')  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 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.  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.  loss_averages_op = _add_loss_summaries(total_loss)  # Compute gradients.  with tf.control_dependencies([loss_averages_op]):    opt = tf.train.GradientDescentOptimizer(lr)    grads = opt.compute_gradients(total_loss)  # Apply gradients.  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.  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_dir  if 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-bin')  if not os.path.exists(extracted_dir_path):    tarfile.open(filepath, 'r:gz').extractall(dest_directory)