tensorflow入门教程之CIFAR-10源代码

楼主用的是目前最新版本的tensorflow，所以从网上找到的CIFAR-10 代码不能直接使用，改了好久，终于能成功训练并检测。先将代码汇总如下，并附个人修改后的代码。
cifar10_train.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport os.pathimport timeimport numpy as npfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tfimport cifar10FLAGS = tf.app.flags.FLAGStf.app.flags.DEFINE_string('train_dir', 'train_log/',                           """Directory where to write event logs """                           """and checkpoint.""")tf.app.flags.DEFINE_integer('max_steps', 10000,                            """Number of batches to run.""")tf.app.flags.DEFINE_boolean('log_device_placement', False,                            """Whether to log device placement.""")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.summary.merge_all()    # 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.summary.FileWriter(FLAGS.train_dir,                                            graph_def=sess.graph_def)    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)if __name__ == '__main__':  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()

cifar10.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport gzipimport osimport reimport sysimport tarfileimport tensorflow.python.platformfrom six.moves import urllibimport tensorflow as tf#from tensorflow.models.image.cifar10 import cifar10_inputimport cifar10_inputFLAGS = tf.app.flags.FLAGS# Basic model parameters.tf.app.flags.DEFINE_integer('batch_size', 128,                            """Number of images to process in a batch.""")tf.app.flags.DEFINE_string('data_dir', 'datasets/',                           """Path to the CIFAR-10 data directory.""")# 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 GPU's 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 measure 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'):    var = tf.get_variable(name, shape, initializer=initializer)  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  """  var = _variable_on_cpu(name, shape,                         tf.truncated_normal_initializer(stddev=stddev))  if wd:    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')  return cifar10_input.distorted_inputs(data_dir=data_dir,                                        batch_size=FLAGS.batch_size)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')  return cifar10_input.inputs(eval_data=eval_data, data_dir=data_dir,                              batch_size=FLAGS.batch_size)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.    dim = 1    for d in pool2.get_shape()[1:].as_list():      dim *= d    reshape = tf.reshape(pool2, [FLAGS.batch_size, dim])    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_lineardef loss(logits, labels):  """Add L2Loss to all the trainable variables.  Add summary for 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.  """  # Reshape the labels into a dense Tensor of  # shape [batch_size, NUM_CLASSES].  sparse_labels = tf.reshape(labels, [FLAGS.batch_size, 1])  indices = tf.reshape(tf.range(FLAGS.batch_size), [FLAGS.batch_size, 1])  concated = tf.concat([indices, sparse_labels],1)  dense_labels = tf.sparse_to_dense(concated,                                    [FLAGS.batch_size, NUM_CLASSES],                                    1.0, 0.0)  # Calculate the average cross entropy loss across the batch.  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,                                             reporthook=_progress)    print()    statinfo = os.stat(filepath)    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')    tarfile.open(filepath, 'r:gz').extractall(dest_directory)

cifar10_input.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport osimport tensorflow.python.platformfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tffrom tensorflow.python.platform import gfile# 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 = 5000NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 1000def 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.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.slice(record_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):  """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.  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  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)  # 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 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  # randomize 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_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)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 gfile.Exists(f):      raise ValueError('Failed to find file: ' + f)  # Create a queue that produces the filenames to read.  filename_queue = tf.train.string_input_producer(filenames)  # Read examples from files in the filename queue.  read_input = read_cifar10(filename_queue)  reshaped_image = tf.cast(read_input.uint8image, tf.float32)  height = IMAGE_SIZE  width = IMAGE_SIZE  # Image processing for evaluation.  # Crop the central [height, width] of the image.  resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,                                                         width, height)  # Subtract off the mean and divide by the variance of the pixels.  float_image = tf.image.per_image_standardization(resized_image)  # 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)

cifar10_eval.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport mathimport timeimport tensorflow.python.platformfrom tensorflow.python.platform import gfileimport numpy as npimport tensorflow as tffrom tensorflow import flagsimport cifar10flags.DEFINE_string('eval_dir', 'eval/',                           """Directory where to write event logs.""")flags.DEFINE_string('eval_data', 'test',                           """Either 'test' or 'train_eval'.""")flags.DEFINE_string('checkpoint_dir', 'train_log/',                           """Directory where to read model checkpoints.""")flags.DEFINE_integer('eval_interval_secs', 60 * 5,                            """How often to run the eval.""")flags.DEFINE_integer('num_examples', 10000,                            """Number of examples to run.""")flags.DEFINE_boolean('run_once', True,                         """Whether to run eval only once.""")FLAGS = flags.FLAGSdef 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():    # 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()    graph_def = tf.get_default_graph().as_graph_def()    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir,                                            graph_def=graph_def)    while True:      eval_once(saver, summary_writer, top_k_op, summary_op)      if FLAGS.run_once:        break      time.sleep(FLAGS.eval_interval_secs)if __name__ == '__main__':  cifar10.maybe_download_and_extract()  if gfile.Exists(FLAGS.eval_dir):    gfile.DeleteRecursively(FLAGS.eval_dir)  gfile.MakeDirs(FLAGS.eval_dir)  evaluate()  exit('testing finished')  

精简版（去除冗余的summary）
cifar10_new.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport gzipimport osimport sysimport tarfilefrom six.moves import urllibimport tensorflow as tffrom tensorflow import flagsimport numpy as npimport mathfrom datetime import datetime#from tensorflow.models.image.cifar10 import cifar10_input# Basic model parameters.flags.DEFINE_integer('batch_size', 128,                            """Number of images to process in a batch.""")flags.DEFINE_string('data_dir', 'datasets/',                           """Path to the CIFAR-10 data directory.""")flags.DEFINE_string('checkpoint_dir', 'train_log/',                           """Directory where to read model checkpoints.""")FLAGS=flags.FLAGS# Global constants describing the CIFAR-10 data set.IMAGE_SIZE = 24NUM_CLASSES = 10NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 5000NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 1000num_examples=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.test_epoch=50# If a model is trained with multiple GPU's 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 _variable_on_cpu(name, shape, initializer):  with tf.device('/cpu:0'):    var = tf.get_variable(name, shape, initializer=initializer)  return vardef _variable_with_weight_decay(name, shape, stddev, wd):  var = _variable_on_cpu(name, shape,                         tf.truncated_normal_initializer(stddev=stddev))  if wd:    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')    tf.add_to_collection('losses', weight_decay)  return vardef inference(images):  # 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)  # pool1  pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],                         padding='SAME', name='pool1')  # norm1  按照通道归一化  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)  # 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.    dim = 1    for d in pool2.get_shape()[1:].as_list():      dim *= d    reshape = tf.reshape(pool2, [FLAGS.batch_size, dim])    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)  # 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)  # 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)  return softmax_lineardef loss(logits, labels):  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=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')def _add_loss_summaries(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])  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 eval_once(saver, top_k_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(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():        prediction=tf.reduce_sum(tf.cast(top_k_op, tf.float32))        prediction_value=sess.run(prediction)        true_count += prediction_value        step += 1      # Compute precision @ 1.      precision = true_count / total_sample_count      print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))    except Exception as e:  # pylint: disable=broad-except      coord.request_stop(e)    coord.request_stop()    coord.join(threads, stop_grace_period_secs=10)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)  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,                                             reporthook=_progress)    print()    statinfo = os.stat(filepath)    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')    tarfile.open(filepath, 'r:gz').extractall(dest_directory)

new_train_test.py

import tensorflow as tffrom tensorflow import flagsimport osimport cifar10_newfrom datetime import datetimeimport numpy as npimport timeflags.DEFINE_string('data_path','datasets/',                    """Path to the CIFAR-10 data directory.""")flags.DEFINE_string('train_dir', 'train_log/',                           """Directory where to write event logs """)flags.DEFINE_boolean('log_device_placement', False,                            """Whether to log device placement.""")flags.DEFINE_integer('max_steps', 100000,                            """Number of batches to run.""")FLAGS=flags.FLAGSNUM_EXAMPLES_PER_EPOCH_FOR_TRAIN=500NUM_EXAMPLES_PER_EPOCH_FOR_EVAL=200def _generate_image_and_label_batch(image, label, min_queue_examples,                                    batch_size):  num_preprocess_threads = 16  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)  tf.summary.image('images', images)  return images, tf.reshape(label_batch, [batch_size])def read_cifar10(filename_queue):  class CIFAR10Record(object):    pass  result = CIFAR10Record()  label_bytes = 1    result.height = 32  result.width = 32  result.depth = 3  image_bytes = result.height * result.width * result.depth  record_bytes = label_bytes + image_bytes  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)  result.key, value = reader.read(filename_queue)  record_bytes = tf.decode_raw(value, tf.uint8)  result.label = tf.cast(tf.slice(record_bytes, [0], [label_bytes]), tf.int32)   depth_major = tf.reshape(tf.slice(record_bytes, [label_bytes], [image_bytes]),                           [result.depth, result.height, result.width])  result.uint8image = tf.transpose(depth_major, [1, 2, 0])  return resultdef distorted_inputs(data_path, batch_size):  filenames = [os.path.join(data_path, 'data_batch_%d.bin' % i)               for i in range(1, 6)]  for f in filenames:    if not tf.gfile.Exists(f):      raise ValueError('Failed to find file: ' + f)  filename_queue = tf.train.string_input_producer(filenames)  read_input = read_cifar10(filename_queue)  #返回一个类  reshaped_image = tf.cast(read_input.uint8image, tf.float32)  height = 24  width = 24  distorted_image = tf.random_crop(reshaped_image, [height, width,3])  distorted_image = tf.image.random_flip_left_right(distorted_image)  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)  float_image = tf.image.per_image_standardization(distorted_image)  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)  return _generate_image_and_label_batch(float_image, read_input.label,                                         min_queue_examples, batch_size)def inputs(eval_data, data_dir, batch_size):  if not eval_data:    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)                 for i in range(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 = 24  width = 24  # Image processing for evaluation.  # Crop the central [height, width] of the image.  resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,                                                         width, height)  # Subtract off the mean and divide by the variance of the pixels.  float_image = tf.image.per_image_standardization(resized_image)  # 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)def running_train():  global_step = tf.Variable(0, trainable=False)  images, labels=distorted_inputs(data_dir_path,batch_size=128)  logits = cifar10_new.inference(images)  loss = cifar10_new.loss(logits, labels)  train_op = cifar10_new.train(loss, global_step)  saver = tf.train.Saver(tf.global_variables())  init = tf.global_variables_initializer()  sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))  sess.run(init)  tf.train.start_queue_runners(sess=sess)  for step in range(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 % 500 == 0 or (step + 1) == FLAGS.max_steps:      num_examples_per_step = 128      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))      # 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)  top_k_op = tf.nn.in_top_k(logits, labels, 1)  cifar10_new.eval_once(saver,top_k_op)        def evaluate():  test_epoch=cifar10_new.test_epoch  images, labels = inputs(True,data_dir_path,batch_size=128)  logits = cifar10_new.inference(images)  top_k_op = tf.nn.in_top_k(logits, labels, 1)  saver = tf.train.Saver()  while True:    cifar10_new.eval_once(saver, top_k_op)    test_epoch-=1    if test_epoch==0:      breakif __name__ == '__main__':  data_dir_path = os.path.join(FLAGS.data_path, 'cifar-10-batches-bin')#  running_train()  evaluate()