Inception结构

心已赠人 2022-03-07 05:56 196阅读 0赞

代码和图片来源：[https://blog.csdn.net/index20001/article/details/76623749][https_blog.csdn.net_index20001_article_details_76623749]

### 前言 ###

2014年，GoogleNet模型在ILSVRC取得分类任务第一名，GoogleNet模型已经达到了22层的深度，而且在网络架构中引入了inception单元；这两个重要的改变说明，通过使用inception单元构造的深层次卷积神经网络模型，能够进一步提高模型整体的性能

![20170803155429473][]

在介绍inception单元之前，我们先来了解下NIN（network in network）中1\*1卷积层的意义和作用，我们都知道卷积层在用来做数据特征提取的，而且最常用的卷积核滑动窗口的高度和宽度一般都是3\*3或者5\*5，那么在卷积核中使用高度和宽度为1\*1的滑动窗又是什么意思呢？答案是能够完成特征图通道的聚合和发散

举个例子来说，假设我们有一个50\*50\*100的特征图，接下来我们将这个特征图输入1\*1的卷积层中，定义卷积层使用的卷积核的滑动窗维度是1\*1\*100，如果我们想输出一个深度为90的特征图，那么我们只需进行九十次的卷积操作就完成了维度为50\*50\*100的特征图，这就是特征图通道的聚合；同理，我们输出一个50\*50\*110的特征图也就实现了特征图通道的发散

比如讲一个32\*32\*10的特征图输入3\*3的卷积层当中，要求最后输出的深度为20，那么在这个过程中需要用到的卷积参数是1800个，但是我们如果先将这个特征图输入到1\*1\*10的卷积核当中，使其变成32\*32\*5的特征图，然后再将其输入到3\*3的卷积层上，这样的话最终参数就是1\*1\*10\*5 + 3\*3\*5\*20，也就是950个参数

总而言之，在GoogleNet模型中加入了inception单元，使卷积神经网络模型和的搭建实现了模块化，如果我们想要增加或者减少GoogleNet模型的深度，则只需要增添或者减少相应的inception单元就可以了，另外为了避免出现深层次模型中的梯度消失的问题，在GoogleNet模型结构中还增加了两个额外的辅助softmax函数，用于前向传播梯度

# -*- coding:utf-8 -*-
    #
    # inception_v3 net
    # default_image_size = 299
     
    import tensorflow as tf
     
    slim = tf.contrib.slim
     
    def inception_v3_base(inputs, num_classes, scope=None):
     
        end_points = {}
        with tf.variable_scope(scope, 'inception_v3', [inputs]):
            with scopes.arg_scope([slim.conv2d, slim.fc, slim.batch_norm, slim.dropout],
                                  is_training=is_training):
                # First part:5 conv layer
                with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                                    stride=1, padding='VALID'):
                    # 299 x 299 x 3
                    end_points['Conv2d_1a'] = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a')
     
                    # 149 x 149 x 32
                    end_points['Conv2d_2a'] = slim.conv2d(end_points['Conv2d_1a'], 32, [3, 3], scope='Conv2d_2a')
     
                    # 147 x 147 x 32
                    end_points['Conv2d_2b'] = slim.conv2d(end_points['Conv2d_2a'], 64, [3, 3], padding='SAME', scope='Conv2d_2b')
     
                    # 147 x 147 x 64
                    end_points['MaxPool_3a'] = slim.max_pool2d(end_points['Conv2d_2b'], [3, 3], stride=2, scope='MaxPool_3a')
     
                    # 73 x 73 x 64
                    end_points['Conv2d_3b'] = slim.conv2d(end_points['MaxPool_3a'], 80, [1, 1], scope='Conv2d_3b')
     
                    # 73 x 73 x 80
                    end_points['Conv2d_4a'] = slim.conv2d(end_points['Conv2d_3b'], 192, [3, 3], scope='Conv2d_4a')
     
                    # 71 x 71 x 192
                    end_points['MaxPool_5a'] = slim.max_pool2d(end_points['Conv2d_4a'], [3, 3], stride=2, scope='MaxPool_5a')
     
                    # 35 x 35 x 192
                    net = end_points['MaxPool_5a']
                    
                # Inception blocks
                with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                                    stride=1, padding='SAME'):
                    # mixed_0: 35 x 35 x 256
                    with tf.variable_scope('Mixed_5b'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_5x5')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_0')
                            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_1')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv_1x1')
                        # 256 = 64 + 64 + 96 + 32
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_5b'] = net
     
                    # mixed_1: 35 x 35 x 288
                    with tf.variable_scope('Mixed_5c'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_5x5')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_0')
                            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_1')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv_1x1')
                        # 288 = 64 + 64 + 96 + 64
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_5c'] = net
     
                    # mixed_2: 35 x 35 x 288
                    with tf.variable_scope('Mixed_5d'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_5x5')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_0')
                            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv_3x3_1')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv_1x1')
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_5d'] = net
     
                    # mixed_3: 17 x 17 x 768
                    with tf.variable_scope('Mixed_6a'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 384, [3, 3], stride=2, padding='VALID', scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv_3x3')
                            branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv_1x1')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_3x3')
                        # 768 = 384 + 96 + 288
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
                        end_points['Mixed_6a'] = net
     
                    # mixed_4: 17 x 17 x 768
                    with tf.variable_scope('Mixed_6b'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv_1x7')
                            branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv_1x1_a')
                            branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv_7x1_b')
                            branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv_1x7_c')
                            branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv_7x1_d')
                            branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1')
                        # 768 = 192 + 192 + 192 + 192
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_6b'] = net
     
                    # mixed_5: 17 x 17 x 768
                    with tf.variable_scope('Mixed_6c'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv_1x7')
                            branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1_a')
                            branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_b')
                            branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv_1x7_c')
                            branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_d')
                            branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1')
                        # 768 = 192 + 192 + 192 + 192
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_6c'] = net
     
                    # mixed_6: 17 x 17 x 768
                    with tf.variable_scope('Mixed_6d'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv_1x7')
                            branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv_1x1_a')
                            branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_b')
                            branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv_1x7_c')
                            branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv_7x1_d')
                            branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1')
                        # 768 = 192 + 192 + 192 + 192
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_6d'] = net
     
                    # mixed_7: 17 x 17 x 768
                    with tf.variable_scope('Mixed_6e'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv_1x7')
                            branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1_a')
                            branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv_7x1_b')
                            branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_c')
                            branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv_7x1_d')
                            branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv_1x7_e')
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1')
                        # 768 = 192 + 192 + 192 + 192
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_6e'] = net
     
                    # Auxiliary Head logits
                    aux_logits = tf.identity(end_points['Mixed_6e'])
                    with tf.variable_scope('AuxLogits'):
                        aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_5x5')
                        aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv_1x1')
        
                        # Shape of feature map before the final layer.
                        shape = aux_logits.get_shape().as_list()
                        aux_logits = slim.conv2d(aux_logits, 768, shape[1:3], weights_initializer=trunc_normal(0.01),
                                                 padding='VALID', scope='Conv2d_2a_{}x{}'.format(shape[1],shape[2]))
                        aux_logits = slim.conv2d(aux_logits, num_classes, [1, 1], activation_fn=None,
                                                 normalizer_fn=None, weights_initializer=trunc_normal(0.001), scope='Conv_1x1')
                        aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
                        end_points['AuxLogits'] = aux_logits
     
                    # mixed_8: 8 x 8 x 1280
                    with tf.variable_scope('Mixed_7a'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                            branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv_3x3')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv_1x1')
                            branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv_1x7')
                            branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv_7x1')
                            branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv_3x3')
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_3x3')
                        # 1280 = 320 + 192 + 768
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
                        end_points['Mixed_7a'] = net
     
                    # mixed_9: 8 x 8 x 2048
                    with tf.variable_scope('Mixed_7b'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv_1x1')
                            branch_1 = tf.concat(axis=3, values=[
                                slim.conv2d(branch_1, 384, [1, 3], scope='Conv_1x3'),
                                slim.conv2d(branch_1, 384, [3, 1], scope='Conv_3x1')])
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv_1x1')
                            branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv_3x3')
                            branch_2 = tf.concat(axis=3, values=[
                                slim.conv2d(branch_2, 384, [1, 3], scope='Conv_1x3'),
                                slim.conv2d(branch_2, 384, [3, 1], scope='Conv_3x1')])
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1')
                        # 2048 = 320 + 384*2 + 384*2 + 192
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_7b'] = net
     
                    # mixed_10: 8 x 8 x 2048
                    with tf.variable_scope('Mixed_7c'):
                        with tf.variable_scope('Branch_0'):
                            branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv_1x1')
                        with tf.variable_scope('Branch_1'):
                            branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv_1x1')
                            branch_1 = tf.concat(axis=3, values=[
                                slim.conv2d(branch_1, 384, [1, 3], scope='Conv_1x3'),
                                slim.conv2d(branch_1, 384, [3, 1], scope='Conv_3x1')])
                        with tf.variable_scope('Branch_2'):
                            branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv_1x1')
                            branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv_3x3')
                            branch_2 = tf.concat(axis=3, values=[
                                slim.conv2d(branch_2, 384, [1, 3], scope='Conv_1x3'),
                                slim.conv2d(branch_2, 384, [3, 1], scope='Conv_3x1')])
                        with tf.variable_scope('Branch_3'):
                            branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_3x3')
                            branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv_1x1')
                        # 2048 = 320 + 384*2 + 384*2 + 192
                        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
                        end_points['Mixed_7c'] = net
     
                    # Final pooling and prediction
                    with tf.variable_scope('Logits'):
                        shape = net.get_shape().as_list()
                        net = slim.avg_pool2d(net, shape[1:3], padding='VALID', scope='AvgPool_{}x{}'.format(shape[1],shape[2]))
                        # 1 x 1 x 2048
                        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout')
                        end_points['PreLogits'] = net
                        # 2048
                        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                                             normalizer_fn=None, scope='Conv_1x1')
                        logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
                        # 1000
                    end_points['Logits'] = logits
                    end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
        return logits, end_points
     
     
    def inception_v3(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.8,
                     prediction_fn=slim.softmax, reuse=None, scope='inception_v3'):
     
        with tf.variable_scope(scope, 'inception_v3', [inputs, num_classes], reuse=reuse) as scope:
            with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
     
                net, end_points = inception_v3_base(inputs, scope=scope)
     
        return logits, end_points

[https_blog.csdn.net_index20001_article_details_76623749]: https://blog.csdn.net/index20001/article/details/76623749
[20170803155429473]: /images/20220307/d59c8c1fc48145babed1cd9aa668a871.png