从零开始自己搭建复杂网络(以Tensorflow为例)

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从零开始自己搭建复杂网络(以Tensorflow为例)

ayew 2018-06-09 14:40:00 浏览843
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从零开始自己搭建复杂网络(以MobileNetV2为例)

 tensorflow经过这几年的发展,已经成长为最大的神经网络框架。而mobileNetV2在经过Xception的实践与深度可分离卷积的应用之后,相对成熟和复杂,对于我们进行网络搭建的学习有着很大的帮助。

MobileNetV2结构基于inverted residual(本质是一个残差网络设计,传统Residual block是block的两端channel通道数多,中间少,而本文设计的inverted residual是block的两端channel通道数少,block内channel多,类似于沙漏和梭子形态的区别),另外保留Depthwise Separable Convolutions。论文模型在ImageNet classification, COCO object detection , VOC image segmentation等数据集验证,在精度、模型参数和计算时间之前取得平衡。
 
 
那么接下来就让我们开始吧!
首先,构建整体网络框架:
import tensorflow as tf
from mobilenet_v2.ops import *


def mobilenetv2(inputs, num_classes, is_train=True, reuse=False):
    exp = 6  # expansion ratio
    with tf.variable_scope('mobilenetv2'):
        net = conv2d_block(inputs, 32, 3, 2, is_train, name='conv1_1')  # size/2

        net = res_block(net, 1, 16, 1, is_train, name='res2_1')

        net = res_block(net, exp, 24, 2, is_train, name='res3_1')  # size/4
        net = res_block(net, exp, 24, 1, is_train, name='res3_2')

        net = res_block(net, exp, 32, 2, is_train, name='res4_1')  # size/8
        net = res_block(net, exp, 32, 1, is_train, name='res4_2')
        net = res_block(net, exp, 32, 1, is_train, name='res4_3')

        net = res_block(net, exp, 64, 1, is_train, name='res5_1')
        net = res_block(net, exp, 64, 1, is_train, name='res5_2')
        net = res_block(net, exp, 64, 1, is_train, name='res5_3')
        net = res_block(net, exp, 64, 1, is_train, name='res5_4')

        net = res_block(net, exp, 96, 2, is_train, name='res6_1')  # size/16
        net = res_block(net, exp, 96, 1, is_train, name='res6_2')
        net = res_block(net, exp, 96, 1, is_train, name='res6_3')

        net = res_block(net, exp, 160, 2, is_train, name='res7_1')  # size/32
        net = res_block(net, exp, 160, 1, is_train, name='res7_2')
        net = res_block(net, exp, 160, 1, is_train, name='res7_3')

        net = res_block(net, exp, 320, 1, is_train, name='res8_1', shortcut=False)

        net = pwise_block(net, 1280, is_train, name='conv9_1')
        net = global_avg(net)
        logits = flatten(conv_1x1(net, num_classes, name='logits'))

        pred = tf.nn.softmax(logits, name='prob')
        return logits, pred

MobileNetV2在第一层使用了一个通道数为3×3的卷积进行处理,之后才转入res_block(残差层),在经过res_block叠加之后,使用pwise_block(主要是1×1的卷积调整通道数),然后使用平均池化层,和一个1×1的卷积,将最后输出变为类数。

接着我们来细讲每一个模块:

首先是第一层卷积模块:

卷积模块由下面两个函数组成

卷积模块由卷积层和批正则化(batch_normalization),以及relu6组成

def conv2d(input_, output_dim, k_h, k_w, d_h, d_w, stddev=0.02, name='conv2d', bias=False):
    with tf.variable_scope(name):
        w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
              regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
              initializer=tf.truncated_normal_initializer(stddev=stddev))
        #truncated_normal_initializer生成截断正态分布的随机数
        conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
        if bias:
            biases = tf.get_variable('bias', [output_dim], initializer=tf.constant_initializer(0.0))
            conv = tf.nn.bias_add(conv, biases)

        return conv


def conv2d_block(input, out_dim, k, s, is_train, name):
    with tf.name_scope(name), tf.variable_scope(name):
        net = conv2d(input, out_dim, k, k, s, s, name='conv2d')
        net = batch_norm(net, train=is_train, name='bn')
        net = relu(net)
        return net

卷积层首先定义了w(可以把w理解为卷积核,是一个Tensor,w具有[filter_height, filter_width, in_channels, out_channels]这样的形状)(l2正则化之后+初始),然后通过 tf.nn.conv2d来进行卷积操作,之后加上偏置。

relu6和batch_normalization,tensorflow有直接的函数,调用即可。

def relu(x, name='relu6'):
    return tf.nn.relu6(x, name)


def batch_norm(x, momentum=0.9, epsilon=1e-5, train=True, name='bn'):
    return tf.layers.batch_normalization(x,
                      momentum=momentum,
                      epsilon=epsilon,
                      scale=True,
                      training=train,
                      name=name)

 接着我们要在卷积层后叠加res_block残差模块

def res_block(input, expansion_ratio, output_dim, stride, is_train, name, bias=False, shortcut=True):
    with tf.name_scope(name), tf.variable_scope(name):
        # pw
        bottleneck_dim=round(expansion_ratio*input.get_shape().as_list()[-1])
        net = conv_1x1(input, bottleneck_dim, name='pw', bias=bias)
        net = batch_norm(net, train=is_train, name='pw_bn')
        net = relu(net)
        # dw
        net = dwise_conv(net, strides=[1, stride, stride, 1], name='dw', bias=bias)
        net = batch_norm(net, train=is_train, name='dw_bn')
        net = relu(net)
        # pw & linear
        net = conv_1x1(net, output_dim, name='pw_linear', bias=bias)
        net = batch_norm(net, train=is_train, name='pw_linear_bn')

        # element wise add, only for stride==1
        if shortcut and stride == 1:
            in_dim=int(input.get_shape().as_list()[-1])
            if in_dim != output_dim:
                ins=conv_1x1(input, output_dim, name='ex_dim')
                net=ins+net
            else:
                net=input+net

        return net

残差模块使用倒置残差结构,如下图所示

MobileNetv2架构是基于倒置残差结构(inverted residual structure),原本的残差结构的主分支是有三个卷积,两个逐点卷积通道数较多,而倒置的残差结构刚好相反,中间的卷积通道数(依旧使用深度分离卷积结构)较多,旁边的较小。

每个残差结构由一个1的卷积和一个3的深度卷积和一个1的卷积经过线性变换得到。

bottleneck的维度有扩张系数=6来影响。使用1×1的卷积将输入通道转换为扩张系数×输入维度。

dwise_conv深度卷积代码具体如下:

def dwise_conv(input, k_h=3, k_w=3, channel_multiplier= 1, strides=[1,1,1,1],
               padding='SAME', stddev=0.02, name='dwise_conv', bias=False):
    with tf.variable_scope(name):
        in_channel=input.get_shape().as_list()[-1]
        w = tf.get_variable('w', [k_h, k_w, in_channel, channel_multiplier],
                        regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
                        initializer=tf.truncated_normal_initializer(stddev=stddev))
        conv = tf.nn.depthwise_conv2d(input, w, strides, padding, rate=None,name=None,data_format=None)
        if bias:
            biases = tf.get_variable('bias', [in_channel*channel_multiplier], initializer=tf.constant_initializer(0.0))
            conv = tf.nn.bias_add(conv, biases)

        return conv

卷积核大小 k_h, k_w, in_channel, 1

 1×1的卷积定义如下:

def conv_1x1(input, output_dim, name, bias=False):
    with tf.name_scope(name):
        return conv2d(input, output_dim, 1,1,1,1, stddev=0.02, name=name, bias=bias)

我们为残差层添加shortcut连接。

       # element wise add, only for stride==1
        if shortcut and stride == 1:
            in_dim=int(input.get_shape().as_list()[-1])
            if in_dim != output_dim:
                ins=conv_1x1(input, output_dim, name='ex_dim')
                net=ins+net
            else:
                net=input+net

只有当stride=1的时候,才启用shortcut链接。

接着叠加res模块

在最后使用全局平均池化:

def global_avg(x):
    with tf.name_scope('global_avg'):
        net=tf.layers.average_pooling2d(x, x.get_shape()[1:-1], 1)
        return net

我们没有使用全连接层,而是使用了1×1的卷积将维度转换为类数,再将其压平。

tf.contrib.layers.flatten(x)

最后使用softmax分类

        pred = tf.nn.softmax(logits, name='prob')

好了,MobilenetV2就搭建成功了。

这种网络的搭建模式可以当成一个模板,将其输入输出定好之后,很容易组装到Estimator中,进行网络的更换,以及后期的微调。

 

 

  

 本次我们使用了tf.nn搭建网络,下次我们会去尝试slim和tf.layer搭建网络。

 

 

 

 

 

 


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