Tensorflow cifar模型源码

# -*- coding: utf-8 -*-# cifar模型:图像分类## Author: Igorimport gzipimport osimport reimport sysimport tarfileimport urllib.requestimport tensorflow as tffrom TensorFlow.cifar import cifar10_inputFLAGS = tf.app.flags.FLAGS# 基础的模型参数tf.app.flags.DEFINE_integer('batch_size', 128,                            "number of images to process in a batch")tf.app.flags.DEFINE_string('data_dir', 'data/',                           "Path to the CIFAR-10 data directory")# 描述CIFAR-10数据集的全局常量IMAGE_SIZE = cifar10_input.IMAGE_SIZENUM_CLASS = cifar10_input.NUM_CLASSESNUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVALNUM_EXAMPLES_PER_EPOCH_FOR_EVAL = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL# 训练过程的全局常量MOVING_AVERAGE_DECAY = 0.9999  # 移动平均衰减NUM_EPOCHS_PER_DECAY = 350.0  # 当学习速率开始下降的(期数)EpochsLEARNING_RATE_DECAY_FACTOR = 0.1  # 学习速率衰减因子INITIAL_LEARNING_RATE = 0.1  # 初始化学习速率TOWER_NAME = 'tower'DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'def distored_inputs():    '''    利用Reader ops为训练构建CIFAR数据集的输入    :return:        images:Images 4D tensor of [batch_size,IMAGE_SIZE,IMAGE_SIZE,3]        labels:Labels 1D [batch_size]    '''    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.distored_inputs(data_dir, FLAGS.batch_size)def inputs(eval_data):    '''    利用Reader ops为评价CIFAR构建输入    :param eval_data:是否利用测试集    :return:        images:Images 4D tensor of [batch_size,IMAGE_SIZE,IMAGE_SIZE,3]        labels:Labels 1D [batch_size]    '''    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, data_dir, FLAGS.batch_size)def _variable_on_cpu(name, shape, initializer):    '''    辅助函数:在CPU中创建Variable    :param name:变量名称    :param shape:形状    :param initializer:初始化    :return:        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):    '''    辅助函数:利用权重衰减初始化Variable    变量利用truncated normal distribution    只有指定的时候才进行权重衰减(weight decay)    :param name:变量的名称    :param shape:形状    :param stddev:trucated Gaussian的标准差    :param wd:增加 L2Loss 权重衰减,如果是None,weight decay不被增加    :return:        Variabel Tensor    '''    var = _variable_on_cpu(name, shape,                           tf.truncated_normal_initializer(stddev=stddev))    if wd:        weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')        # Stores value in the collection with the given name        tf.add_to_collection('losses', weight_decay)    return vardef _activation_summary(x):    '''    辅助函数:创建激活神经元的summaries    提供激活元的直方图的总结    提供激活元稀疏测量的总结    :param x:Tensor    :return:        nothing    '''    tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)    tf.histogram_summary(tensor_name + '/activations', x)    # 0的占比,稀疏性    tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x))def inference(images):    '''    构建 CIFAR-10 模型    为了变量共享统一用tf.get_variabel()实例化所有variables    多GPU:tf.get_variable()    单个GPU:tf.Variable()    :param images: Images returned from distored_inputs() or inputs()    :return:        Logits.    '''    # conv1 卷积层1    with tf.variable_scope('conv1') as scope:        # filter Tensor [filter_height, filter_width, in_channels, out_channels]        kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64],                                             stddev=1e-4, wd=0.0)        # 输出:和输出相同类型的Tensor        # [batch, out_height, out_width, filter_height * filter_width * in_channels]        # For each patch, right-multiplies the filter matrix and the image patch vector.        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=0.0001 / 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 = 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:        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)    # local    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],                                  initializer=tf.constant_initializer(0.1))        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases,                            name=scope.name)        _activation_summary(local4)    # softmax    with tf.variable_scope("softmax_linear") as scope:        weights = _variable_with_weight_decay('weights', shape=[192, NUM_CLASS],                                              stddev=1 / 192.0, wd=0.0)        biases = _variable_on_cpu('biases', [NUM_CLASS],                                  initializer=tf.constant_initializer(0.0))        # softmax(WX+b)        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope)        _activation_summary(softmax_linear)    return softmax_lineardef loss(logits, labels):    '''    对所有训练的变量添加L2Loss    计算单批次的平均交叉熵损失    为 "Loss" 和 "Loss/avg"添加总结    :param logits:Logits from inference()    :param labels:Labels from distored_inputs() or inputs() 1-D tensor    :return:        Loss tensor of type float    '''    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)    # 总的损失定义为交叉熵损失加熵所有权重衰减的损失    return tf.add_n(tf.get_collection('losses'), name='total_loss')def _add_loss_summaries(total_loss):    '''    为CIFAR模式添加损失总结    生成所有损失和相关总结的移动平均:为可视化网络的性能    :param total_loss:Total loss from loss()    :return:        loss_averages_op:op for generating moving averages of losses.    '''    # 为单个的损失和总共的损失计算指数移动平均    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]:        tf.scalar_summary(l.op.name + '(raw)', l)        tf.scalar_summary(l.op.name, loss_averages.average(l))    return loss_averages_opdef train(total_loss, global_step):    '''    训练 CIFAR-10 模型    创建一个optimizer应用于所有训练的变量    对所有的训练变量移动平均    :param total_loss:单批次总的损失Loss()    :param global_step:Integer 变量,计算训练步骤过程的数量    :return:        train_op : 训练的操作 op    '''    # 影响学习速率的变量,每一期的批次数量    num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size    decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)    # 基于步骤的学习速率指数衰减    lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,                                    global_step,                                    decay_steps,                                    LEARNING_RATE_DECAY_FACTOR,                                    staircase=True)    tf.scalar_summary('learning_rate', lr)    # 生成所有损失和相关总结的移动平均    loss_averages_op = _add_loss_summaries(total_loss)    # 计算梯度    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)    # 添加训练变量的直方图    for var in tf.trainable_variables():        tf.histogram_summary(var.op.name, var)    # 梯度直方图    for grad, var in grads:        if grad:            tf.histogram_summary(var.op.name + '/gradients', grad)    # 追踪训练变量的指数移动平均    variable_averages = tf.train.ExponentialMovingAverage(        MOVING_AVERAGE_DECAY, global_step)    variable_averages_op = variable_averages.apply(tf.trainable_variables())    with tf.control_dependencies([apply_gradient_op, variable_averages_op]):        train_op = tf.no_op(name='train')    return train_opdef maybe_download_and_extract():    '''    下载并提取数据集    '''    dest_directory = FLAGS.data_dir    if not os.path.exists(dest_directory):        os.mkdir(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)if __name__ == '__main__':    maybe_download_and_extract()