5cifar100数据集的读取-5.4将CIFAR10上的卷积网络用于CIFAR100数据集

代码：
convnets_test.py

#-*- coding:utf-8 -*-#实现简单卷积神经网络对MNIST数据集进行分类：conv2d + activation + pool + fcimport csvimport tensorflow as tfimport osfrom tensorflow.examples.tutorials.mnist import input_dataos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import sysfrom six.moves import urllibimport tarfileimport cifar_inputimport numpy as np# 设置算法超参数learning_rate_init = 0.001training_epochs = 1batch_size = 100display_step = 10conv1_kernel_num = 64conv2_kernel_num = 64fc1_units_num = 1024fc2_units_num = 512activation_func = tf.nn.reluactivation_name = 'relu'l2loss_ratio = 0.05# Network Parametersn_input = 784 # MNIST data input (img shape: 28*28)#数据集中输入图像的参数dataset_dir_cifar10 = '../CIFAR10_dataset/cifar-10-batches-bin'dataset_dir_cifar100 = '../CIFAR100_dataset/cifar-100-binary'dataset_dir_cifar10_root = '../CIFAR10_dataset'dataset_dir_cifar100_root = '../CIFAR100_dataset'num_examples_per_epoch_for_train = cifar_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN#50000num_examples_per_epoch_for_eval = cifar_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL#10000image_size = cifar_input.IMAGE_SIZEimage_channel = cifar_input.IMAGE_DEPTHcifar10_data_url = cifar_input.CIFAR10_DATA_URLcifar100_data_url = cifar_input.CIFAR100_DATA_URL#通过修改cifar10or20or100，就可以测试cifar10，cifar20，cifar100#或者使用假数据跑模型（让cifar10or20or100 = -1）cifar10or20or100 = 100if cifar10or20or100 == 10:    n_classes = cifar_input.NUM_CLASSES_CIFAR10    dataset_dir = dataset_dir_cifar10    cifar_data_url = cifar10_data_url    dataset_dir_cifar_root = dataset_dir_cifar10_rootif cifar10or20or100 == 20:    n_classes = cifar_input.NUM_CLASSES_CIFAR20    dataset_dir = dataset_dir_cifar100    cifar_data_url = cifar100_data_url    dataset_dir_cifar_root = dataset_dir_cifar100_rootif cifar10or20or100 == 100:    n_classes = cifar_input.NUM_CLASSES_CIFAR100    dataset_dir = dataset_dir_cifar100    cifar_data_url = cifar100_data_url    dataset_dir_cifar_root = dataset_dir_cifar100_rootdef get_distorted_train_batch(data_dir,batch_size):    """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 data_dir:        raise ValueError('Please supply a data_dir')    images, labels = cifar_input.distorted_inputs(cifar10or20or100=n_classes,                                                  data_dir=data_dir,                                                  batch_size=batch_size)    return images,labelsdef get_undistorted_eval_batch(data_dir,eval_data, 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.    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 data_dir:        raise ValueError('Please supply a data_dir')    images, labels = cifar_input.inputs(cifar10or20or100=n_classes,                                        eval_data=eval_data,                                        data_dir=data_dir,                                        batch_size=batch_size)    return images,labels#根据指定的维数返回初始化好的指定名称的权重 Variabledef WeightsVariable(shape, name_str, stddev=0.1):    # initial = tf.random_normal(shape=shape, stddev=stddev, dtype=tf.float32)    initial = tf.truncated_normal(shape=shape, stddev=stddev, dtype=tf.float32)    return tf.Variable(initial, dtype=tf.float32, name=name_str)#根据指定的维数返回初始化好的指定名称的偏置 Variabledef BiasesVariable(shape, name_str, init_value=0.00001):    initial = tf.constant(init_value, shape=shape)    return tf.Variable(initial, dtype=tf.float32, name=name_str)# 二维卷积层activation(conv2d+bias)的封装def Conv2d(x, W, b, stride=1, padding='SAME',activation=tf.nn.relu,act_name='relu'):    with tf.name_scope('conv2d_bias'):        y = tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding=padding)        y = tf.nn.bias_add(y, b)    with tf.name_scope(act_name):        y = activation(y)    return y# 二维池化层pool的封装def Pool2d(x, pool= tf.nn.max_pool, k=2, stride=2,padding='SAME'):    return pool(x, ksize=[1, k, k, 1], strides=[1, stride, stride, 1], padding=padding)# 全连接层activate(wx+b)的封装def FullyConnected(x, W, b, activation=tf.nn.relu, act_name='relu'):    with tf.name_scope('Wx_b'):        y = tf.matmul(x, W)        y = tf.add(y, b)    with tf.name_scope(act_name):        y = activation(y)    return y#为每一层的激活输出添加汇总节点def AddActivationSummary(x):    tf.summary.histogram('/activations',x)    tf.summary.scalar('/sparsity',tf.nn.zero_fraction(x))#为所有损失节点添加（滑动平均）标量汇总操作def AddLossesSummary(losses):    #计算所有（individual losses）和（total loss）的滑动平均    loss_averages = tf.train.ExponentialMovingAverage(0.9,name='avg')    loss_averages_op = loss_averages.apply(losses)    #为所有individual losses 和 total loss 绑定标量汇总节点    #为所有平滑处理过的individual losses 和 total loss也绑定标量汇总节点    for loss in losses:        #没有平滑过的loss名字后面加上‘（raw）’，平滑以后的loss使用其原来的名称        tf.summary.scalar(loss.op.name + '(raw)',loss)        tf.summary.scalar(loss.op.name + '(avg)',loss_averages.average(loss))    return loss_averages_op#修改了4处激活函数：Conv2d_1、Conv2d_2、FC1_nonlinear、FC2_nonlineardef Inference(image_holder):    # 第一个卷积层activate(conv2d + biase)    with tf.name_scope('Conv2d_1'):        # conv1_kernel_num = 64        weights = WeightsVariable(shape=[5, 5, image_channel, conv1_kernel_num],                                  name_str='weights',stddev=5e-2)        biases = BiasesVariable(shape=[conv1_kernel_num], name_str='biases',init_value=0.0)        conv1_out = Conv2d(image_holder, weights, biases, stride=1, padding='SAME',activation=activation_func,act_name=activation_name)        AddActivationSummary(conv1_out)    # 第一个池化层(pool 2d)    with tf.name_scope('Pool2d_1'):        pool1_out = Pool2d(conv1_out, pool=tf.nn.max_pool, k=3, stride=2,padding='SAME')    # 第二个卷积层activate(conv2d + biase)    with tf.name_scope('Conv2d_2'):        # conv2_kernels_num = 64        weights = WeightsVariable(shape=[5, 5, conv1_kernel_num, conv2_kernel_num], name_str='weights', stddev=5e-2)        biases = BiasesVariable(shape=[conv2_kernel_num], name_str='biases', init_value=0.0)        conv2_out = Conv2d(pool1_out, weights, biases, stride=1, padding='SAME',activation=activation_func,act_name=activation_name)        AddActivationSummary(conv2_out)    # 第二个池化层(pool 2d)    with tf.name_scope('Pool2d_2'):        pool2_out = Pool2d(conv2_out, pool=tf.nn.max_pool, k=3, stride=2, padding='SAME')    #将二维特征图变换为一维特征向量    with tf.name_scope('FeatsReshape'):        features = tf.reshape(pool2_out, [batch_size,-1])        feats_dim = features.get_shape()[1].value    # 第一个全连接层(fully connected layer)    with tf.name_scope('FC1_nonlinear'):        weights = WeightsVariable(shape=[feats_dim, fc1_units_num],name_str='weights',stddev=4e-2)        biases = BiasesVariable(shape=[fc1_units_num], name_str='biases',init_value=0.1)        fc1_out = FullyConnected(features, weights, biases, activation=activation_func,act_name=activation_name)        AddActivationSummary(fc1_out)        with tf.name_scope('L2_loss'):            weight_loss = tf.multiply(tf.nn.l2_loss(weights),l2loss_ratio,name="fc1_weight_loss")            tf.add_to_collection('losses',weight_loss)    # 第二个全连接层(fully connected layer)    with tf.name_scope('FC2_nonlinear'):        weights = WeightsVariable(shape=[fc1_units_num, fc2_units_num],name_str='weights',stddev=4e-2)        biases = BiasesVariable(shape=[fc2_units_num], name_str='biases',init_value=0.1)        fc2_out = FullyConnected(fc1_out, weights, biases, activation=activation_func,act_name=activation_name)        AddActivationSummary(fc2_out)        with tf.name_scope('L2_loss'):            weight_loss = tf.multiply(tf.nn.l2_loss(weights), l2loss_ratio, name="fc2_weight_loss")            tf.add_to_collection('losses', weight_loss)    # 第三个全连接层(fully connected layer)    with tf.name_scope('FC3_linear'):        fc3_units_num = n_classes        weights = WeightsVariable(shape=[fc2_units_num, fc3_units_num],name_str='weights',stddev=1.0/fc2_units_num)        biases = BiasesVariable(shape=[fc3_units_num], name_str='biases',init_value=0.0)        logits = FullyConnected(fc2_out, weights, biases,activation=tf.identity, act_name='linear')        AddActivationSummary(logits)    return logitsdef TrainModel():    #调用上面写的函数构造计算图    with tf.Graph().as_default():        # 计算图输入        with tf.name_scope('Inputs'):            image_holder = tf.placeholder(tf.float32, [batch_size, image_size,image_size,image_channel], name='images')            labels_holder = tf.placeholder(tf.int32, [batch_size], name='labels')        # 计算图前向推断过程        with tf.name_scope('Inference'):             logits = Inference(image_holder)        # 定义损失层(loss layer)        with tf.name_scope('Loss'):            cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels_holder,logits=logits)            cross_entropy_loss = tf.reduce_mean(cross_entropy,name='xentropy_loss')            tf.add_to_collection('losses',cross_entropy_loss)            #总体损失（total loss）= 交叉熵损失 + 所有权重的L2损失            total_loss = tf.add_n(tf.get_collection('losses'),name='total_loss')            average_losses = AddLossesSummary(tf.get_collection('losses') + [total_loss])        # 定义优化训练层(train layer)        with tf.name_scope('Train'):            learning_rate = tf.placeholder(tf.float32)            global_step = tf.Variable(0, name='global_step', trainable=False, dtype=tf.int64)            optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)            # optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,momentum=0.9)            # optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)            # optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)            # optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)            # optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate)            train_op = optimizer.minimize(total_loss,global_step=global_step)        # 定义模型评估层(evaluate layer)        with tf.name_scope('Evaluate'):            top_K_op = tf.nn.in_top_k(predictions=logits,targets=labels_holder,k=1)        #定义获取训练样本批次的计算节点        with tf.name_scope('GetTrainBatch'):            image_train,labels_train = get_distorted_train_batch(data_dir=dataset_dir,batch_size=batch_size)        # 定义获取测试样本批次的计算节点        with tf.name_scope('GetTestBatch'):            image_test, labels_test = get_undistorted_eval_batch(data_dir=dataset_dir,eval_data=True, batch_size=batch_size)        merged_summaries = tf.summary.merge_all()        # 添加所有变量的初始化节点        init_op = tf.global_variables_initializer()        print('把计算图写入事件文件，在TensorBoard里面查看')        summary_writer = tf.summary.FileWriter(logdir='logs')        summary_writer.add_graph(graph=tf.get_default_graph())        summary_writer.flush()        # 将评估结果保存到文件        results_list = list()        # 写入参数配置        results_list.append(['learning_rate', learning_rate_init,                             'training_epochs', training_epochs,                             'batch_size', batch_size,                             'conv1_kernel_num', conv1_kernel_num,                             'conv2_kernel_num', conv2_kernel_num,                             'fc1_units_num', fc1_units_num,                             'fc2_units_num', fc2_units_num])        results_list.append(['train_step', 'train_loss','train_step', 'train_accuracy'])        with tf.Session() as sess:            sess.run(init_op)            print('===>>>>>>>==开始训练集上训练模型==<<<<<<<=====')            total_batches = int(num_examples_per_epoch_for_train / batch_size)            print('Per batch Size:,',batch_size)            print('Train sample Count Per Epoch:',num_examples_per_epoch_for_train)            print('Total batch Count Per Epoch:', total_batches)            #启动数据读取队列            tf.train.start_queue_runners()            #记录模型被训练的步数            training_step = 0            # 训练指定轮数，每一轮的训练样本总数为：num_examples_per_epoch_for_train            for epoch in range(training_epochs):                #每一轮都要把所有的batch跑一遍                for batch_idx in range(total_batches):                    #运行获取训练数据的计算图，取出一个批次数据                    images_batch ,labels_batch = sess.run([image_train,labels_train])                    #运行优化器训练节点                    _,loss_value,avg_losses = sess.run([train_op,total_loss,average_losses],                                            feed_dict={image_holder:images_batch,                                                       labels_holder:labels_batch,                                                       learning_rate:learning_rate_init})                    #每调用一次训练节点，training_step就加1，最终==training_epochs * total_batch                    training_step = sess.run(global_step)                    #每训练display_step次，计算当前模型的损失和分类准确率                    if training_step % display_step == 0:                        #运行accuracy节点，计算当前批次的训练样本的准确率                        predictions = sess.run([top_K_op],                                               feed_dict={image_holder:images_batch,                                                          labels_holder:labels_batch})                        #当前批次上的预测正确的样本量                        batch_accuracy = np.sum(predictions)/batch_size                        results_list.append([training_step,loss_value,training_step,batch_accuracy])                        print("Training Step:" + str(training_step) +                              ",Training Loss = " + "{:.6f}".format(loss_value) +                              ",Training Accuracy = " + "{:.5f}".format(batch_accuracy) )                        #运行汇总节点                        summaries_str = sess.run(merged_summaries,feed_dict=                                                    {image_holder:images_batch,                                                     labels_holder:labels_batch})                        summary_writer.add_summary(summary=summaries_str,global_step=training_step)                        summary_writer.flush()            summary_writer.close()            print('训练完毕')            print('===>>>>>>>==开始在测试集上评估模型==<<<<<<<=====')            total_batches = int(num_examples_per_epoch_for_eval / batch_size)            total_examples = total_batches * batch_size            print('Per batch Size:,', batch_size)            print('Test sample Count Per Epoch:', total_examples)            print('Total batch Count Per Epoch:', total_batches)            correct_predicted = 0            for test_step in range(total_batches):                #运行获取测试数据的计算图，取出一个批次测试数据                images_batch,labels_batch = sess.run([image_test,labels_test])                #运行accuracy节点，计算当前批次的测试样本的准确率                predictions = sess.run([top_K_op],                                       feed_dict={image_holder:images_batch,                                                  labels_holder:labels_batch})                #累计每个批次上的预测正确的样本量                correct_predicted += np.sum(predictions)            accuracy_score = correct_predicted / total_examples            print('---------->Accuracy on Test Examples:',accuracy_score)            results_list.append(['Accuracy on Test Examples:',accuracy_score])            # 将评估结果保存到文件            results_file = open('evaluate_results/evaluate_results.csv', 'w', newline='')            csv_writer = csv.writer(results_file, dialect='excel')            for row in results_list:                csv_writer.writerow(row)def main(argv=None):    cifar_input.maybe_download_and_extract(data_dir=dataset_dir_cifar_root,data_url=cifar_data_url)    train_dir='/logs'    if tf.gfile.Exists(train_dir):        tf.gfile.DeleteRecursively(train_dir)    tf.gfile.MakeDirs(train_dir)    TrainModel()if __name__ =='__main__':    tf.app.run()

cifar_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 tfimport sysfrom six.moves import urllibimport tarfile# 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.#用于描述CiFar数据集的全局常量# NUM_CLASSES = 10IMAGE_SIZE = 32IMAGE_DEPTH = 3NUM_CLASSES_CIFAR10 = 10NUM_CLASSES_CIFAR20 = 20NUM_CLASSES_CIFAR100 = 100NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000print('调用我啦...cifar_input...')#从网址下载数据集存放到data_dir指定的目录下CIFAR10_DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'CIFAR100_DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-100-binary.tar.gz'#从网址下载数据集存放到data_dir指定的目录中def maybe_download_and_extract(data_dir,data_url=CIFAR10_DATA_URL):    """下载并解压缩数据集 from Alex's website."""    dest_directory = data_dir #'../CIFAR10_dataset'    DATA_URL = data_url    if not os.path.exists(dest_directory):        os.makedirs(dest_directory)    filename = DATA_URL.split('/')[-1] #'cifar-10-binary.tar.gz'    filepath = os.path.join(dest_directory, filename)#'../CIFAR10_dataset\\cifar-10-binary.tar.gz'    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.')    # if data_url== CIFAR10_DATA_URL:    #     extracted_dir_path = os.path.join(dest_directory,'cifar-10-batches-bin')  # '../CIFAR10_dataset\\cifar-10-batches-bin'    # else :    #     extracted_dir_path = os.path.join(dest_directory, 'cifar-100-binary')  # '../CIFAR10_dataset\\cifar-10-batches-bin'    # if not os.path.exists(extracted_dir_path):    #     tarfile.open(filepath, 'r:gz').extractall(dest_directory)    tarfile.open(filepath, 'r:gz').extractall(dest_directory)def read_cifar10(filename_queue,coarse_or_fine=None):  """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.  #cifar10 binary中的样本记录：3072=32x32x3  #<1 x label><3072 x pixel>  #...  #<1 x label><3072 x pixel>  # 类型标签字节数  label_bytes = 1  # 2 for CIFAR-100  result.height = 32  result.width = 32  result.depth = 3    #图像字节数  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.  # 每一条样本记录由 标签 + 图像 组成，其字节数是固定的。  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.  # 创建一个固定长度记录读取器，读取一个样本记录的所有字节（label_bytes + image_bytes)  # 由于cifar10中的记录没有header_bytes 和 footer_bytes,所以设置为0  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes,header_bytes=0,footer_bytes=0)  # 调用读取器对象的read 方法返回一条记录  result.key, value = reader.read(filename_queue)  # Convert from a string to a vector of uint8 that is record_bytes long.  #将一个字节组成的string类型的记录转换为长度为record_bytes，类型为unit8的一个数字向量  record_bytes = tf.decode_raw(value, tf.uint8)  # The first bytes represent the label, which we convert from uint8->int32.  # 将一个字节代表了标签，我们把它从unit8转换为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].  # 剩余的所有字节都是图像数据，把他从unit8转换为int32  # 转为三维张量[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].  # 把图像的空间位置和深度位置顺序由[depth, height, width] 转换成[height, width, depth]  result.uint8image = tf.transpose(depth_major, [1, 2, 0])  return resultdef read_cifar100(filename_queue,coarse_or_fine='fine'):  """Reads and parses examples from CIFAR100 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 CIFAR100Record(object):    pass  result = CIFAR100Record()  result.height = 32  result.width = 32  result.depth = 3  # cifar100中每个样本记录都有两个类别标签，每一个字节是粗略分类标签，  # 第二个字节是精细分类标签：<1 x coarse label><1 x fine label><3072 x pixel>  coarse_label_bytes = 1  fine_label_bytes = 1  #图像字节数  image_bytes = result.height * result.width * result.depth  # 每一条样本记录由 标签 + 图像 组成，其字节数是固定的。  record_bytes = coarse_label_bytes + fine_label_bytes + image_bytes  # 创建一个固定长度记录读取器，读取一个样本记录的所有字节（label_bytes + image_bytes)  # 由于cifar100中的记录没有header_bytes 和 footer_bytes,所以设置为0  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes,header_bytes=0,footer_bytes=0)  # 调用读取器对象的read 方法返回一条记录  result.key, value = reader.read(filename_queue)  #将一系列字节组成的string类型的记录转换为长度为record_bytes，类型为unit8的一个数字向量  record_bytes = tf.decode_raw(value, tf.uint8)  # 将一个字节代表了粗分类标签，我们把它从unit8转换为int32.  coarse_label = tf.cast(tf.strided_slice(record_bytes, [0], [coarse_label_bytes]), tf.int32)  # 将二个字节代表了细分类标签，我们把它从unit8转换为int32.  fine_label = tf.cast(tf.strided_slice(record_bytes, [coarse_label_bytes], [coarse_label_bytes + fine_label_bytes]), tf.int32)  if coarse_or_fine == 'fine':    result.label = fine_label #100个精细分类标签  else:    result.label = coarse_label #100个粗略分类标签  # 剩余的所有字节都是图像数据，把他从一维张量[depth * height * width]  # 转为三维张量[depth，height，width]  depth_major = tf.reshape(      tf.strided_slice(record_bytes, [coarse_label_bytes + fine_label_bytes],                       [coarse_label_bytes + fine_label_bytes + image_bytes]),                        [result.depth, result.height, result.width])  # 把图像的空间位置和深度位置顺序由[depth, height, width] 转换成[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(cifar10or20or100,data_dir, batch_size):  """使用Reader ops 构造distorted input 用于CIFAR的训练  输入参数:   cifar10or20or100:指定要读取的数据集是cifar10 还是细分类的cifar100 ，或者粗分类的cifar100    data_dir: 指向CIFAR-10 或者 CIFAR-100 数据集的目录    batch_size: 每个批次的图像数量  Returns:    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.    labels: Labels. 1D tensor of [batch_size] size.  """  #判断是读取cifar10 还是 cifar100（cifar100可分为20类或100类）  if cifar10or20or100 == 10:    filenames = [os.path.join(data_dir,'data_batch_%d.bin' % i) for i in xrange(1,6)]    read_cifar = read_cifar10    coarse_or_fine = None  if cifar10or20or100 == 20:    filenames = [os.path.join(data_dir,'train.bin')]    read_cifar = read_cifar100    coarse_or_fine = 'coarse'  if cifar10or20or100 == 100:      filenames = [os.path.join(data_dir, 'train.bin')]      read_cifar = read_cifar100      coarse_or_fine = 'fine'  #检查文件是否存在  for f in filenames:    if not tf.gfile.Exists(f):      raise ValueError('Failed to find file: ' + f)  # 根据文件名列表创建一个文件名队列  filename_queue = tf.train.string_input_producer(filenames)  # 从文件名队列的文件中读取样本  read_input = read_cifar(filename_queue)  # 将无符号8位图像数据转换成float32位  casted_image = tf.cast(read_input.uint8image, tf.float32)  # 要生成的目标图像的大小，在这里与原图像的尺寸保持一致  height = IMAGE_SIZE  width = IMAGE_SIZE  #为图像添加padding = 4，图像尺寸变为[32+4,32+4],为后面的随机裁切留出位置  padded_image = tf.image.resize_image_with_crop_or_pad(casted_image,width+4,height+4)  #下面的这些操作为原始图像添加了很多不同的distortions，扩增了原始训练数据集  # 在[36,36]大小的图像中随机裁切出[height,width]即[32,,32]的图像区域  distorted_image = tf.random_crop(padded_image, [height, width, 3])  # 将图像进行随机的水平翻转（左边和右边的像素对调）  distorted_image = tf.image.random_flip_left_right(distorted_image)  # 下面这两个操作不满足交换律，即 亮度调整+对比度调整 和 对比度调整+亮度调整  # 产生的结果是不一样的，你可以采取随机的顺序来执行这两个操作  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)  # 数据集标准化操作：减去均值+方差归一化(divide by the variance of the pixels)  float_image = tf.image.per_image_standardization(distorted_image)  # 设置张量的形状  float_image.set_shape([height, width, 3])  read_input.label.set_shape([1])  # 确保： 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(cifar10or20or100, eval_data, data_dir, batch_size):  """使用Reader ops 读取数据集，用于CIFAR的评估  输入参数:  cifar10or20or100:指定要读取的数据集是cifar10 还是细分类的cifar100 ，或者粗分类的cifar100    eval_data: True or False ,指示要读取的是训练集还是测试集    data_dir: 指向CIFAR-10 或者 CIFAR-100 数据集的目录    batch_size: 每个批次的图像数量  返回:    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.    labels: Labels. 1D tensor of [batch_size] size.  """  #判断是读取cifar10 还是 cifar100（cifar100可分为20类或100类）  if cifar10or20or100 == 10:      read_cifar = read_cifar10      coarse_or_fine = None      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  if cifar10or20or100 == 20 or cifar10or20or100 == 100:      read_cifar = read_cifar100      if not eval_data:          filenames = [os.path.join(data_dir,'train.bin')]          num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN      else:          filenames = [os.path.join(data_dir,'test.bin')]          num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL  if cifar10or20or100 == 100:      coarse_or_fine = 'fine'  if cifar10or20or100 == 20:      coarse_or_fine = 'coarse'  for f in filenames:    if not tf.gfile.Exists(f):      raise ValueError('Failed to find file: ' + f)  # 根据文件名列表创建一个文件名队列  filename_queue = tf.train.string_input_producer(filenames)  # 从文件名队列的文件中读取样本  read_input = read_cifar(filename_queue, coarse_or_fine = coarse_or_fine)  # 将无符号8位图像数据转换成float32位  casted_image = tf.cast(read_input.uint8image, tf.float32)  # 要生成的目标图像的大小，在这里与原图像的尺寸保持一致  height = IMAGE_SIZE  width = IMAGE_SIZE  # 用于评估过程的图像数据预处理  # Crop the central [height, width] of the image.（其实这里并未发生裁剪）  resized_image = tf.image.resize_image_with_crop_or_pad(casted_image,width,height)  #数据集标准化操作：减去均值 + 方差归一化  float_image = tf.image.per_image_standardization(resized_image)  # 设置数据集中张量的形状  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.  # 通过构造样本队列(a queue of examples)产生一个批次的图像和标签  return _generate_image_and_label_batch(float_image, read_input.label,                                         min_queue_examples, batch_size,                                         shuffle=False)