GoogleNet詳解

一、概述

GoogleNet是2014年Google發布的深度神經網路，在ImageNet圖像識別任務中表現優異。它是Inception系列模型的第一代，以其高效的參數和計算量成為當時最先進的模型。

GoogleNet並不是一般CNN的平鋪式層次，而是通過並行的Inception模塊十分複雜地組成。Inception模塊通過採用1×1、3×3、5×5的卷積核，從不同感受野大小的採樣中得到不同網路層的特徵表達，通過concatenate將特徵圖並串起來，從而獲得更好的特徵表達能力。

二、Inception模塊

Inception模塊最核心的思想是並行計算。在不同大小感受野下通過不同大小的卷積核得到的特徵圖，然後通過concatenate並表達出完整的特徵圖，從而獲得更高維的特徵信息。

示例代碼如下：

from keras.layers import Conv2D, MaxPooling2D, concatenate

def InceptionModule(x, nb_filter):
    branch1x1 = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(x)

    branch3x3 = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(x)
    branch3x3 = Conv2D(nb_filter, (3, 3), padding='same', activation='relu')(branch3x3)

    branch5x5 = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(x)
    branch5x5 = Conv2D(nb_filter, (5, 5), padding='same', activation='relu')(branch5x5)

    branch_MaxPooling = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
    branch_MaxPooling = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(branch_MaxPooling)

    branches = [branch1x1, branch3x3, branch5x5, branch_MaxPooling]
    out = concatenate(branches, axis=-1)
    return out

三、完整模型架構

GoogleNet共22層，前15層採用常規的卷積、池化和歸一化層。其後的分類層採用了全局平均池化、dropout和softmax，使得提取的特徵圖更具魯棒性。完整模型代碼如下：

from keras.layers import Input, Dense, Dropout, Flatten, concatenate
from keras.layers.convolutional import Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model

def InceptionModule(x, nb_filter):
    branch1x1 = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(x)

    branch3x3 = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(x)
    branch3x3 = Conv2D(nb_filter, (3, 3), padding='same', activation='relu')(branch3x3)

    branch5x5 = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(x)
    branch5x5 = Conv2D(nb_filter, (5, 5), padding='same', activation='relu')(branch5x5)

    branch_MaxPooling = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
    branch_MaxPooling = Conv2D(nb_filter, (1, 1), padding='same', activation='relu')(branch_MaxPooling)

    branches = [branch1x1, branch3x3, branch5x5, branch_MaxPooling]
    out = concatenate(branches, axis=-1)
    return out

inputs = Input(shape=(224, 224, 3))

x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu')(inputs)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

x = Conv2D(64, (1, 1), strides=(1, 1), padding='same', activation='relu')(x)
x = Conv2D(192, (3, 3), strides=(1, 1), padding='same', activation='relu')(x)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

x = InceptionModule(x, 64)
x = InceptionModule(x, 120)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

x = InceptionModule(x, 128)
x = InceptionModule(x, 128)
x = InceptionModule(x, 128)
x = InceptionModule(x, 132)
x = InceptionModule(x, 208)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

x = InceptionModule(x, 208)
x = InceptionModule(x, 256)
x = AveragePooling2D(pool_size=(7, 7), strides=(7, 7), padding='same')(x)

x = Dropout(0.4)(x)
x = Flatten()(x)
outputs = Dense(1000, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

四、轉移學習

在實際應用中，考慮到數據量和算力的問題，可通過利用已有訓練好的模型進行微調，即轉移學習。將已有模型的前N層凍結，只對模型後面幾層進行微調，加速模型訓練過程，提高模型準確率。代碼如下：

from keras.applications.inception_v3 import InceptionV3
from keras.layers import Dense, GlobalAveragePooling2D
from keras.models import Model
from keras import backend as K

K.clear_session()

base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299, 299, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(1000, activation='softmax')(x)

# 凍結前面249層，只訓練後面的層
for layer in base_model.layers[:249]:
    layer.trainable = False

model = Model(inputs=base_model.input, outputs=predictions)

五、優化和預處理

在訓練GoogleNet時，常用的優化器為SGD（隨機梯度下降），使得模型參數更好的收斂。在預處理方面，ImageNet中的圖像分為ILSVRC2014-train和ILSVRC2014-val兩個數據集。在訓練時，對於ILSVRC2014-train數據集中的圖像，進行數據增強，如左右翻轉、隨機裁剪等預處理方式，以增強模型的魯棒性和泛化能力。在預測時，對於圖像進行中心化和大小正則化處理。

六、總結

GoogleNet以其高效的模型架構和並行計算Inception模塊的特點，被廣泛應用在圖像識別領域。在實際應用中，通過微調已有的訓練好的模型，可以有效提高模型的準確率和加速訓練過程。