Tensorflow实例：实现ResNet

ResNet（Residual Neural Network）由微软研究院的Kaiming He等4名华人提出，通过使用Residual Unit成功训练152层深的神经网络，在ILSVRC 2015比赛中获得了冠军，取得3.57%的top-5错误率，同时参数量却比VGGNet低，效果非常突出。
ResNet允许原始输入信息直接传输到后面的层中。ResNet最初的灵感出自这个问题：在不断加神经网络的深度时，会出现一个Degradation的问题，即准确率会先上升然后达到饱和，再持续增加深度则会导致准确率下降。这并不是过拟合的问题，因为不光在测试集上误差增大，训练集本身误差也会增大。假设有一个比较浅的网络达到了饱和的准确率，那么后面再加上几个y=x的全等映射层，起码误差不会增加，即更深的网络不应该带来训练集上误差上升。

Residual Unit

假设某段神经网络的输入是x，期望输出是H(x)，如果我们直接把输入x传到输出作为初始结果，那么此时我们需要学习的目标就是F(x)=H(x)−x:

下图为VGGNet-19、34层深的普通卷积网络、34层深的ResNet网络的对比图：

可以看到，普通直连的卷积神经网络和ResNet的最大区别在于，ResNet有许多旁路的支线将输入直接连到后面的层，使得后面的层可以直接学习残差，这种结构也被称为shortcut或skip connections。
传统的卷积层或全连接层在信息传递时，或多或少会丢失信息、损耗等问题。ResNet在某种程度上解决了这个问题，通过直接将输入信息绕道传到输出，保护信息的完整性，整个网络则只需学习输入、输出差别的那一部分，简化学习目标和难度。
在使用了ResNet的结构后，可以发现层数不断加深导致的训练集上误差增大的现象被消除了，ResNet网络的训练会随着层数增大而逐渐减小，并且在测试集上的表现也会变好。
之后，Residual V2被提出。Residual V2和Residual V1的主要区别在于：

1. 作者通过研究ResNet残差学习单元的传播公式，发现前馈和反馈信号可以直接传输，因此skip connection的非线性激活函数（如ReLU）替代为Identity Mapping (y = x)。

2. ResNet V2在每一层都使用了Batch Normalization。这样处理之后，新的残差学习单元将比以前更容易训练且泛化性更强。

   ResNet V2网络的实现

   下面我们就用Tensorflow实现一个ResNet V2网络。我们依然使用方便的contrib.slim库来辅助创建ResNet。

import collectionsimport tensorflow as tfslim = tf.contrib.slim# 我们使用collections.namedtuple设计ResNet基本Block模块的named tuple，并用它创建Block的类，# 但只包含数据结构，不包含具体方法。class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):    'A named tuple describing a ResNet block'# 下面定义一个降采样subsample的方法def subsample(inputs, factor, scope=None):    if factor == 1:        return inputs    else:        return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)# 再定义一个conv2d_same函数创建卷积层。def conv2d_same(inputs, num_outputs, kernel_size, stride, scope=None):    if stride == 1:        return slim.conv2d(inputs, num_outputs, kernel_size,                           stride=1, padding='SAME', scope=scope)    else:        pad_total = kernel_size - 1        pad_beg = pad_total // 2        pad_end = pad_total - pad_beg        inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])        return slim.conv2d(inputs, num_outputs, kernel_size,                           stride=stride, padding='VALID', scope=scope)# 接下来定义堆叠Blocks的函数，参数中的net即为输入，blocks是之前定义的Block的class的列表，# 而outputs_collections则是用来收集各个end_points的collections。@slim.add_arg_scopedef stack_blocks_dense(net, blocks, outputs_collections=None):    for block in blocks:        with tf.variable_scope(block.scope, 'block', [net]) as sc:            for i, unit in enumerate(block.args):                with tf.variable_scope('unit_%d' % (i + 1), values=[net]):                    unit_depth, unit_depth_bottleneck, unit_stride = unit                    net = block.unit_fn(net,                                        depth=unit_depth,                                        depth_bottleneck=unit_depth_bottleneck,                                        stride=unit_stride)            net = slim.utils.collect_named_outputs(outputs_collections, sc.name,                                                   net)    return net# 这里创建ResNet通用的arg_scope,用来定义某些函数的参数默认值。def resnet_arg_scope(is_training=True,                     weight_decay=0.0001,                     batch_norm_decay=0.997,                     batch_norm_epsilon=1e-5,                     batch_norm_scale=True):    batch_norm_scale = {        'is_training': is_training,        'decay': batch_norm_decay,        'epsilon': batch_norm_epsilon,        'scale': batch_norm_scale,        'updates_collections': tf.GraphKeys.UPDATE_OPS,    }    with slim.arg_scope(        [slim.conv2d],        weights_regularizer = slim.l2_regularizer(weight_decay),        weights_initializer=slim.variance_scaling_initializer(),        activation_fn = tf.nn.relu,        normalizer_fn = slim.batch_norm,        normalizer_params = batch_norm_parms    ):        with slim.arg_scope([slim.batch_norm], **batch_norm_params):            with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:                return arg_sc# 接下来定义核心的bottlneck残差学习单元@slim.add_arg_scopedef bottleneck(inputs, depth, depth_bottleneck, stride, outputs_collections=None, scope=None):    with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)        preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')        if depth == depth_in:            shortcut = subsample(inputs, stride, 'shortcut')        else:            shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,                                   normalizer_fn=None, activation_fn=None,                                   scope='shortcut')        residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')        residual = conv2d_same(residual, depth_bottleneck, 3, stride, scope='conv2')        residual = slim.conv2d(residual, depth, [1, 1], stride=1, normalizer_fn=None,                               activation_fn=None, scope='conv3')        output = shortcut + residual        return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)# 下面定义生成ResNet V2的主函数，我们只要预先定义好网络的残差学习模块组blocks，它就可以生成对应# 的完整的ResNetdef resnet_v2(inputs, blocks, num_classes=None, global_pool=True,              include_root_block=True, reuse=None, scope=None):    with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc:        end_point_collection = sc.original_name_scope + '_end_points'        with slim.arg_scope([slim.conv2d, bottleneck, stack_blocks_dense],                            outputs_collections=end_point_collection):            net = inputs            if include_root_block:                with slim.arg_scope([slim.conv2d], activation_fn=None,                                    normalizer_fn=None):                    net = conv2d_same(net,64, 7, stride=2, scope='conv1')                net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')            net = stack_blocks_dense(net, blocks)            net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm')            if global_pool:                net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True)            if num_classes is not None:                net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,                                  normalizer_fn=None, scope='logits')            end_points = slim.utils.convert_collection_to_dict(end_point_collection)            if num_classes is not None:                end_points['predictions'] = slim.softmax(net, scope='predictions')            return net, end_points# 至此，我们就将ResNet的生成函数定义好了。下面根据几个不同深度的ResNet网络配置，来设计层数# 分别为50， 101， 152， 200的ResNet。def resnet_v2_50(inputs, num_classes=None,                 global_pool=True,                 reuse=None,                 scope='resnet_v2_50'):    blocks = [        Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),        Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),        Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 2)]),        Block('block4', bottleneck, [(2048, 512, 1)] * 3)]    return resnet_v2(inputs, blocks, num_classes, global_pool, include_root_block=True,                     reuse=reuse, scope=scope)def resnet_v2_101(inputs, num_classes=None,                  global_pool=True,                  reuse=None,                  scope='resnet_v2_101'):    blocks = [        Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),        Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),        Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),        Block('block4', bottleneck, [(2048, 512, 1)] * 3)    ]    return resnet_v2(inputs, blocks, num_classes, global_pool, include_root_block=True,                     reuse=reuse, scope=scope)def resnet_v2_152(inputs, num_classes=None, global_pool=True,                  reuse=None, scope='resnet_v2_152'):    blocks = [        Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),        Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]),        Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),        Block('block4', bottleneck, [(2048, 512, 1)] * 3)    ]    return resnet_v2(inputs, blocks, num_classes, global_pool, include_root_block=True,                     reuse=reuse, scope=scope)def resnet_v2_200(inputs, num_classes=None, global_pool=True,                  reuse=None, scope='resnet_v2_200'):    blocks = [        Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),        Block('block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]),        Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),        Block('block4', bottleneck, [(2048, 512, 1)] * 3)    ]    return resnet_v2(inputs, blocks, num_classes, global_pool, include_root_block=True,                     reuse=reuse, scope=scope)batch_size = 32height, width = 224, 224inputs = tf.random_uniform((batch_size, height, width, 3))with slim.arg_scope(resnet_arg_scope(is_training=False)):    net, end_points = resnet_v2_152(inputs, 1000)init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)num_batches = 100time_tensorflow_run(sess, net, "Forward")