tensorflow从0开始（7）——利用tensorflow进行开发的准备工作（续1）

tensorflow开发流程——表情分析（前期准备）

Cifar10程序分析

为什么要做这个解读，个人看来，Cifar是一个图像多分类的经典案例。学习tensorflow以来，还没有任何实质性的进展。个人认为把Cifar问题的输入，换成其它的分类图像数据，应该也可以训练出来不同的分类模型，来对不同的图像数据集进行分类，所以先要搞清楚Cifar Demo的整个过程（关于此观点是否成立，待弄懂之后，导入自己的数据集，简单测试下，再做修改）。下面只做其中关于train部分代码的解读。 
cifar10_train的流程如下：

• 下载提取数据（如果数据不存在）（cifar10_input.py）
• 读取数据(cifar10_input.py)
• 创建模型CNN并初始化参数(cifar10.py)
• 利用模型来预测数据
• 将数据的预测值和真实值进行比对，进行loss运算，并通过梯度下降原则，不断调整模型的参数

整个Cifar10相关的代码位于路径下，~/libsource/tensorflow/为你自己的tensorflow源码路径：

~/libsource/tensorflow/tensorflow/tensorflow/models/image/cifar10

Cifar10数据下载

Cifar10的官方例子中，已经提供了数据的下载代码，如下:

tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar10_data',"""Path to the CIFAR-10 data directory.""")DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'def maybe_download_and_extract():  """Download and extract the tarball from Alex's website."""C  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.')    tarfile.open(filepath, 'r:gz').extractall(dest_directory)

代码中：urllib.request.urlretrieve用来下载数据，过程中会毁掉_progress函数，对下载进度进行显示。_progress(count, block_size, total_size)的三个参数分别指下载的数据块数目，一个数据块的大小，服务器端的总的数据大小。 
下载完成后利用tarfile进行解压。 
tarfile.open(filepath, ‘r:gz’).extractall(dest_directory)，open返回一个tarfile对象，调用tarfile的extreactall方法将压缩文件解压到目标目录。 
https://docs.python.org/2/library/tarfile.html

Cifar数据读入

我本来的想法是把这部分，改为opencv的读入。但是读到这一部分的实现时，我还是有点搞不清楚的。 
整个过程中，查阅了官网提供的很多资料，但还是不能很好的理解，但是认识还是加深不少。以下是我觉得可能会有帮助的几个官网的链接： 
关于线程和队列的： 
https://www.tensorflow.org/versions/r0.9/how_tos/threading_and_queues/index.html 
关于tensorflow中读入数据的集中方式： 
https://www.tensorflow.org/versions/r0.9/how_tos/reading_data/index.html 
关于输入和读入器的： 
https://www.tensorflow.org/versions/r0.9/api_docs/python/io_ops.html#batching-at-the-end-of-an-input-pipeline

关于读入数据，我自己写的验证代码如下：

#first we summary the input image by queue#first file pathfile_path='/tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin'data_dir='/tmp/cifar10_data/cifar-10-batches-bin'summary_dir='/tmp/tf_summary'#import libimport tensorflow as tfIMAGE_SIZE=24NUM_CLASSES=10NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000#define input image formatclass CifarData(object):    passimg=CifarData()img.height = 32img.width = 32img.depth = 3img_bytes=img.height * img.width * img.depthlabel_bytes=1record_bytes=label_bytes+img_bytes#define filequeuefile_queue=tf.train.string_input_producer([file_path])#read data from queuereader = tf.FixedLengthRecordReader(record_bytes=record_bytes)img.key,value = reader.read(file_queue)num_recs = reader.num_records_producedrecord_bytes=tf.decode_raw(value,tf.uint8)img.label=tf.cast(tf.slice(record_bytes,[0],[label_bytes]),tf.int32)depth_major=tf.reshape(tf.slice(record_bytes,[label_bytes],[img_bytes]),[img.depth,img.height,img.width])img.unit8image=tf.transpose(depth_major,[1,2,0])img_summary=tf.reshape(img.unit8image, [-1,32,32,3])tf.image_summary('img',img_summary)reshaped_img=tf.cast(img.unit8image, tf.float32)height = IMAGE_SIZEwidth=IMAGE_SIZEdistorted_img=tf.random_crop(reshaped_img,[height,width,3])distorted_img=tf.image.random_flip_left_right(distorted_img)distorted_img=tf.image.random_brightness(distorted_img,max_delta=63)distorted_img=tf.image.random_contrast(distorted_img,lower=0.2,upper=1.8)float_img= tf.image.per_image_whitening(distorted_img)min_queue_examples=20000num_preprocess_threads = 16images=tf.train.shuffle_batch([float_img],128,num_threads=num_preprocess_threads,capacity=min_queue_examples+3*128,min_after_dequeue=min_queue_examples)tf.image_summary('images',images,100)distorted_img_summary=tf.reshape(distorted_img,[-1, height,width,3])tf.image_summary('img1',distorted_img_summary,100)sess = tf.InteractiveSession()tf.initialize_all_variables().run()merged=tf.merge_all_summaries()print('begin to start queue')tf.train.start_queue_runners(sess=sess)print('queue has been started')writer=tf.train.SummaryWriter(summary_dir,sess.graph)for i in range(1000):    summary, img_, key_ = sess.run([merged,value,img.key])    print("step%d: %s"%(i,key_))    writer.add_summary(summary,i)

输出结果如下，说明利用上述的queue的读入方式，程序每次自动的更新了数据：

begin to start queuequeue has been startedstep0: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:332step1: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:456step2: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:492step3: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:818step4: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:910step5: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:994step6: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:1174step7: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:1302step8: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:1529step9: /tmp/cifar10_data/cifar-10-batches-bin/data_batch_1.bin:1561

tensorboard的输出图像如下： 

queue_input_summary.png 
上述图像说明一次reader的操作，能够将一个文件里的图像读出，并进行batch，但是这一步是怎么做到的，还不清楚。这个要后续在来研究。。。。。。。。。。。。。。。。。 
下面这段代码，是如何做到读出一系列数据的，不理解。。。。。。。。。。。。。暂且跳过。

img.key,value = reader.read(file_queue)

Cifar10的Model

例子中的代码，模型会对神经网络的各个参数给一个初始值，利用初始的神经网络，对图片进行推断，会得出一个分类。然后根据这个分类与正确的分类的比对，不断地调整CNN的参数，使得分类结果越来越准确，训练得出最终的模型。

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=1e-4, 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))    bias = tf.nn.bias_add(conv, biases)    conv1 = tf.nn.relu(bias, 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=1e-4, 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))    bias = tf.nn.bias_add(conv, biases)    conv2 = tf.nn.relu(bias, 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)  # softmax, i.e. softmax(WX + b)  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

根据loss不断修正CNN模型参数

利用给出的模型推测结果：

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

将推测结果与图片的真实分类进行比对

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

loss函数的代码：

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)  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(      logits, labels, 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')

loss函数中主要是计算了推测分类结果与实际分类结果的交叉熵，交叉熵越小，证明模型越好。 
根据交叉熵，利用梯度下降原则，迭代计算，不断寻找loss的全局最小值，完成训练。代码如下：

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.  lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,                                  global_step,                                  decay_steps,                                  LEARNING_RATE_DECAY_FACTOR,                                  staircase=True)  tf.scalar_summary('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.histogram_summary(var.op.name, var)  # Add histograms for gradients.  for grad, var in grads:    if grad is not None:      tf.histogram_summary(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_op

其中根据学习率（learning rate）得出一个梯度下降的优化器（GradientDescentOptimizer），然后计算出一个下降的梯度值，并将此梯度作用在模型上，优化模型。

整个训练过程：

整个训练过程的代码如下：

def train():  """Train CIFAR-10 for a number of steps."""  with tf.Graph().as_default():    global_step = tf.Variable(0, trainable=False)    # Get images and labels for CIFAR-10.    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)    # Create a saver.    saver = tf.train.Saver(tf.all_variables())    # Build the summary operation based on the TF collection of Summaries.    summary_op = tf.merge_all_summaries()    # Build an initialization operation to run below.    init = tf.initialize_all_variables()    # Start running operations on the Graph.    sess = tf.Session(config=tf.ConfigProto(        log_device_placement=FLAGS.log_device_placement))    sess.run(init)    # Start the queue runners.    tf.train.start_queue_runners(sess=sess)    summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)    for step in xrange(FLAGS.max_steps):      start_time = time.time()      _, loss_value = sess.run([train_op, loss])      duration = time.time() - start_time      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'      if step % 10 == 0:        num_examples_per_step = FLAGS.batch_size        examples_per_sec = num_examples_per_step / duration        sec_per_batch = float(duration)        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '                      'sec/batch)')        print (format_str % (datetime.now(), step, loss_value,                             examples_per_sec, sec_per_batch))      if step % 100 == 0:        summary_str = sess.run(summary_op)        summary_writer.add_summary(summary_str, step)      # Save the model checkpoint periodically.      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')        saver.save(sess, checkpoint_path, global_step=step)

train过程如下图： 

待续。。。。。