深度学习面试题20：GoogLeNet(Inception V1)

时间：2019-08-20 10:40:43 阅读：120 评论：0 收藏：0 [点我收藏+]

2014年，GoogLeNet和VGG是当年ImageNet挑战赛(ILSVRC14)的双雄，GoogLeNet获得了第一名、VGG获得了第二名，这两类模型结构的共同特点是层次更深了。VGG继承了LeNet以及AlexNet的一些框架结构，而GoogLeNet则做了更加大胆的网络结构尝试，虽然深度只有22层，但大小却比AlexNet和VGG小很多，GoogleNet参数为500万个，AlexNet参数个数是GoogleNet的12倍，VGGNet参数又是AlexNet的3倍，因此在内存或计算资源有限时，GoogleNet是比较好的选择；从模型结果来看，GoogLeNet的性能却更加优越。

GoogLeNet是谷歌（Google）研究出来的深度网络结构，为什么不叫“GoogleNet”，而叫“GoogLeNet”，是为了向“LeNet”致敬，因此取名为“GoogLeNet”

GoogLeNet团队要打造一个Inception模块（名字源于盗梦空间），让深度网络的表现更好。

技术图片

返回目录

网络结构

技术图片

PS：Slim是2016年开发出来的，即使在InceptionV1中，他也没有使用论文里说的5*5的卷积核，而是用的3*3的卷积核。

返回目录

对应代码

这里采用的官网的代码tensorflow/models/research/slim/nets/inception_v1.py

下载方式

git clone https://github.com/tensorflow/models.git

这个项目比较大，如果下载很慢的话，可以在qq群：537594183文件中只下载slim的代码即可。

# Copyright 2016 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.
# ==============================================================================
"""Contains the definition for inception v1 classification network."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from nets import inception_utils

slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)


def inception_v1_base(inputs,
                      final_endpoint=‘Mixed_5c‘,
                      include_root_block=True,
                      scope=‘InceptionV1‘):
  """Defines the Inception V1 base architecture.

  This architecture is defined in:
    Going deeper with convolutions
    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
    http://arxiv.org/pdf/1409.4842v1.pdf.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    final_endpoint: specifies the endpoint to construct the network up to. It
      can be one of [‘Conv2d_1a_7x7‘, ‘MaxPool_2a_3x3‘, ‘Conv2d_2b_1x1‘,
      ‘Conv2d_2c_3x3‘, ‘MaxPool_3a_3x3‘, ‘Mixed_3b‘, ‘Mixed_3c‘,
      ‘MaxPool_4a_3x3‘, ‘Mixed_4b‘, ‘Mixed_4c‘, ‘Mixed_4d‘, ‘Mixed_4e‘,
      ‘Mixed_4f‘, ‘MaxPool_5a_2x2‘, ‘Mixed_5b‘, ‘Mixed_5c‘]. If
      include_root_block is False, [‘Conv2d_1a_7x7‘, ‘MaxPool_2a_3x3‘,
      ‘Conv2d_2b_1x1‘, ‘Conv2d_2c_3x3‘, ‘MaxPool_3a_3x3‘] will not be available.
    include_root_block: If True, include the convolution and max-pooling layers
      before the inception modules. If False, excludes those layers.
    scope: Optional variable_scope.

  Returns:
    A dictionary from components of the network to the corresponding activation.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values.
  """
  end_points = {}
  with tf.variable_scope(scope, ‘InceptionV1‘, [inputs]):
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_initializer=trunc_normal(0.01)):
      with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                          stride=1, padding=‘SAME‘):
        net = inputs
        if include_root_block:
          end_point = ‘Conv2d_1a_7x7‘
          net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point:
            return net, end_points
          end_point = ‘MaxPool_2a_3x3‘
          net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point:
            return net, end_points
          end_point = ‘Conv2d_2b_1x1‘
          net = slim.conv2d(net, 64, [1, 1], scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point:
            return net, end_points
          end_point = ‘Conv2d_2c_3x3‘
          net = slim.conv2d(net, 192, [3, 3], scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point:
            return net, end_points
          end_point = ‘MaxPool_3a_3x3‘
          net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
          end_points[end_point] = net
          if final_endpoint == end_point:
            return net, end_points

        end_point = ‘Mixed_3b‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 64, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_3c‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘MaxPool_4a_3x3‘
        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_4b‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 192, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_4c‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 160, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 112, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_4d‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_4e‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 112, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 144, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_4f‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 160, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘MaxPool_5a_2x2‘
        net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_5b‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 160, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope=‘Conv2d_0a_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = ‘Mixed_5c‘
        with tf.variable_scope(end_point):
          with tf.variable_scope(‘Branch_0‘):
            branch_0 = slim.conv2d(net, 384, [1, 1], scope=‘Conv2d_0a_1x1‘)
          with tf.variable_scope(‘Branch_1‘):
            branch_1 = slim.conv2d(net, 192, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_2‘):
            branch_2 = slim.conv2d(net, 48, [1, 1], scope=‘Conv2d_0a_1x1‘)
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope=‘Conv2d_0b_3x3‘)
          with tf.variable_scope(‘Branch_3‘):
            branch_3 = slim.max_pool2d(net, [3, 3], scope=‘MaxPool_0a_3x3‘)
            branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope=‘Conv2d_0b_1x1‘)
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
    raise ValueError(‘Unknown final endpoint %s‘ % final_endpoint)


def inception_v1(inputs,
                 num_classes=1000,
                 is_training=True,
                 dropout_keep_prob=0.8,
                 prediction_fn=slim.softmax,
                 spatial_squeeze=True,
                 reuse=None,
                 scope=‘InceptionV1‘,
                 global_pool=False):
  """Defines the Inception V1 architecture.

  This architecture is defined in:

    Going deeper with convolutions
    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
    http://arxiv.org/pdf/1409.4842v1.pdf.

  The default image size used to train this network is 224x224.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes. If 0 or None, the logits layer
      is omitted and the input features to the logits layer (before dropout)
      are returned instead.
    is_training: whether is training or not.
    dropout_keep_prob: the percentage of activation values that are retained.
    prediction_fn: a function to get predictions out of logits.
    spatial_squeeze: if True, logits is of shape [B, C], if false logits is of
        shape [B, 1, 1, C], where B is batch_size and C is number of classes.
    reuse: whether or not the network and its variables should be reused. To be
      able to reuse ‘scope‘ must be given.
    scope: Optional variable_scope.
    global_pool: Optional boolean flag to control the avgpooling before the
      logits layer. If false or unset, pooling is done with a fixed window
      that reduces default-sized inputs to 1x1, while larger inputs lead to
      larger outputs. If true, any input size is pooled down to 1x1.

  Returns:
    net: a Tensor with the logits (pre-softmax activations) if num_classes
      is a non-zero integer, or the non-dropped-out input to the logits layer
      if num_classes is 0 or None.
    end_points: a dictionary from components of the network to the corresponding
      activation.
  """
  # Final pooling and prediction
  with tf.variable_scope(scope, ‘InceptionV1‘, [inputs], reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):
      net, end_points = inception_v1_base(inputs, scope=scope)
      with tf.variable_scope(‘Logits‘):
        if global_pool:
          # Global average pooling.
          net = tf.reduce_mean(net, [1, 2], keep_dims=True, name=‘global_pool‘)
          end_points[‘global_pool‘] = net
        else:
          # Pooling with a fixed kernel size.
          net = slim.avg_pool2d(net, [7, 7], stride=1, scope=‘AvgPool_0a_7x7‘)
          end_points[‘AvgPool_0a_7x7‘] = net
        if not num_classes:
          return net, end_points
        net = slim.dropout(net, dropout_keep_prob, scope=‘Dropout_0b‘)
        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                             normalizer_fn=None, scope=‘Conv2d_0c_1x1‘)
        if spatial_squeeze:
          logits = tf.squeeze(logits, [1, 2], name=‘SpatialSqueeze‘)

        end_points[‘Logits‘] = logits
        end_points[‘Predictions‘] = prediction_fn(logits, scope=‘Predictions‘)
  return logits, end_points
inception_v1.default_image_size = 224

inception_v1_arg_scope = inception_utils.inception_arg_scope

View Code

返回目录

网络说明

（1）GoogLeNet采用了模块化的结构（Inception结构），方便增添和修改；

（2）网络最后采用了average pooling（平均池化）来代替全连接层，，事实证明这样可以将准确率提高0.6%。但是，实际在最后还是加了一个全连接层，主要是为了方便对输出进行灵活调整；

（3）虽然移除了全连接，但是网络中依然使用了Dropout ;

（4）辅助分类器的两个分支有什么用呢？

作用一：可以把他看做inception网络中的一个小细节，它确保了即便是隐藏单元和中间层也参与了特征计算，他们也能预测图片的类别，他在inception网络中起到一种调整的效果，并且能防止网络发生过拟合。

作用二：给定深度相对较大的网络，有效传播梯度反向通过所有层的能力是一个问题。通过将辅助分类器添加到这些中间层，可以期望较低阶段分类器的判别力。在训练期间，它们的损失以折扣权重（辅助分类器损失的权重是0.3）加到网络的整个损失上。

Inception V1的参数量=5607184，约为560w

返回目录

参考资料

《图解深度学习与神经网络：从张量到TensorFlow实现》_张平

inceptionV1-Going Deeper with Convolutions

http://noahsnail.com/2017/07/21/2017-07-21-GoogleNet%E8%AE%BA%E6%96%87%E7%BF%BB%E8%AF%91%E2%80%94%E2%80%94%E4%B8%AD%E8%8B%B1%E6%96%87%E5%AF%B9%E7%85%A7/

《深-度-学-习-核-心-技-术-与-实-践》

大话CNN经典模型：GoogLeNet（从Inception v1到v4的演进）

https://my.oschina.net/u/876354/blog/1637819

返回目录

深度学习面试题20：GoogLeNet(Inception V1)

标签：防止做了 sof puts 通过 contain ecif war 深度

原文地址：https://www.cnblogs.com/mfryf/p/11381352.html

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周排行

深度学习面试题20：GoogLeNet(Inception V1)

目录

简介

网络结构

对应代码

网络说明

参考资料

　　简介

　　网络结构

　　对应代码

　　网络说明

　　参考资料