目录

1.Inception介绍

1.1 Inception结构

1.2 Inception V1(GoogleNet)

Naive Inception


Inception Module

GoogLeNet

1.3 Inception V2(Batch Norm)

1.4 Inception V3（Factorization）

1.5 Inception V4（ResNet）

Inception V4 相比 V3 主要是结合了微软的 ResNet，将错误率进一步减少到 3.08%。

1.5 Inception v1~v4 总结

1.6 Inception进阶

Inception v4 、Inception ResNet v1、Inception ResNet v2

Inception V4 网络模型 Vs Inception ResNet 模型：

2.Inception实现

inceptionV2

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   = Noneconv_name = Nonex = 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 xdef 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 xdef 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=64x = 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