深度学习（07）-- 经典CNN网络结构（Inception (v1-v4)）

最新推荐文章于 2025-05-14 16:24:21 发布

Zero-One-0101

最新推荐文章于 2025-05-14 16:24:21 发布

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分类专栏： ML&DL-深度学习文章标签：深度学习

本文链接：https://blog.youkuaiyun.com/wangzi11111111/article/details/88946567

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本文详细介绍Inception网络的不同版本，从Inception V1 (GoogleNet) 到 Inception V4 的演进过程，并深入探讨其结构改进及实现方法，特别是在Inception V4中引入ResNet思想后的性能提升。

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def Conv2d_BN(x, filters, kernel_size, padding='same', strides=(1,1), name=None):
    if name is not None:
        bn_name   = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name   = None
        conv_name = None

    x = Conv2D(filters=filters, kernel_size=kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
    x = BatchNormalization(axis=3, name=bn_name)(x)

    return x


def Inception(x, filters):
    branch_1x1 = Conv2d_BN(x, filters=filters, kernel_size=(1,1), padding='same', strides=(1,1), name=None)

    branch_3x3 = Conv2d_BN(x,          filters=filters, kernel_size=(1,1), padding='same', strides=(1,1), name=None)
    branch_3x3 = Conv2d_BN(branch_3x3, filters=filters, kernel_size=(3,3), padding='same', strides=(1,1), name=None)

    branch_5x5 = Conv2d_BN(x,          filters=filters, kernel_size=(1,1), padding='same', strides=(1,1), name=None)
    branch_5x5 = Conv2d_BN(branch_5x5, filters=filters, kernel_size=(5,5), padding='same', strides=(1,1), name=None)

    branch_pool = MaxPooling2D(pool_size=(3,3), strides=(1,1), padding='same')(x)
    branch_pool = Conv2d_BN(branch_pool, filters=filters, kernel_size=(1,1), padding='same', strides=(1,1), name=None)

    x = concatenate([branch_1x1, branch_3x3, branch_5x5, branch_pool], axis=3)

    return x


def Inception_v2(input_shape=(64,64,3), classes=6):
    x_input = Input(input_shape)

    "stage 1: "
    x = Conv2d_BN(x_input, filters=4, kernel_size=(5,5), strides=(1,1), padding='same')
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(x)
    x = Conv2d_BN(x, filters=8, kernel_size=(1,1), strides=(1,1), padding='same')
    x = Conv2d_BN(x, filters=8, kernel_size=(3,3), strides=(1,1), padding='same')
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(x)

    "stage 2: Inception_v2_block (*2)"
    x = Inception(x, 16)  #4*16=64
    x = Inception(x, 16)
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(x)

    "stage 3: Inception_v2_block (*5)"
    x = Inception(x, 32)
    x = Inception(x, 32)
    x = Inception(x, 32)  
    x = Inception(x, 64)
    x = Inception(x, 64)
    x = MaxPooling2D(pool_size=(3,3), strides=(2,2), padding='same')(x)  

    "stage 4: Inception_v2_block (*2)"
    x = Inception(x, 128) 
    x = Inception(x, 128)
    x = AveragePooling2D(pool_size=(4,4), strides=(1,1), padding='same')(x)

    x = Flatten()(x)
    "stage 5: fc"
    x = Dropout(0.5)(x)  
    x = Dense(6,activation='softmax')(x)  

    model = Model(inputs=x_input, outputs=x, name='Inception_v2')

    return model