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# ResNeXt

・入力を分岐させて畳み込み、最後に足し合わせる
・分岐する数をcardinalityとよぶ

# 実装

```
def _resblock(n_filters1, n_filters2, strides=(1,1)):
def f(input):
x = Convolution2D(n_filters1, (1,1), strides=strides,
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Convolution2D(n_filters1, (3,3), strides=strides,
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Convolution2D(n_filters2, (1,1), strides=strides,
x = BatchNormalization()(x)

return x

return f

def resnext():

inputs = Input(shape=(32, 32, 3))
x = Convolution2D(32, (7,7), strides=(1,1),
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2,2), padding='same')(x)

residual = Convolution2D(256, 1, strides=1, padding='same')(x)
residual = BatchNormalization()(residual)

x1 = _resblock(n_filters1=4, n_filters2=256)(x)
x2 = _resblock(n_filters1=4, n_filters2=256)(x)
x3 = _resblock(n_filters1=4, n_filters2=256)(x)
x4 = _resblock(n_filters1=4, n_filters2=256)(x)
x5 = _resblock(n_filters1=4, n_filters2=256)(x)
x6 = _resblock(n_filters1=4, n_filters2=256)(x)
x7 = _resblock(n_filters1=4, n_filters2=256)(x)
x8 = _resblock(n_filters1=4, n_filters2=256)(x)
x9 = _resblock(n_filters1=4, n_filters2=256)(x)
x10 = _resblock(n_filters1=4, n_filters2=256)(x)
x11 = _resblock(n_filters1=4, n_filters2=256)(x)
x12 = _resblock(n_filters1=4, n_filters2=256)(x)
x13 = _resblock(n_filters1=4, n_filters2=256)(x)
x14 = _resblock(n_filters1=4, n_filters2=256)(x)
x15 = _resblock(n_filters1=4, n_filters2=256)(x)
x16 = _resblock(n_filters1=4, n_filters2=256)(x)
x17 = _resblock(n_filters1=4, n_filters2=256)(x)
x18 = _resblock(n_filters1=4, n_filters2=256)(x)
x19 = _resblock(n_filters1=4, n_filters2=256)(x)
x20 = _resblock(n_filters1=4, n_filters2=256)(x)
x21 = _resblock(n_filters1=4, n_filters2=256)(x)
x22 = _resblock(n_filters1=4, n_filters2=256)(x)
x23 = _resblock(n_filters1=4, n_filters2=256)(x)
x24 = _resblock(n_filters1=4, n_filters2=256)(x)
x25 = _resblock(n_filters1=4, n_filters2=256)(x)
x26 = _resblock(n_filters1=4, n_filters2=256)(x)
x27 = _resblock(n_filters1=4, n_filters2=256)(x)
x28 = _resblock(n_filters1=4, n_filters2=256)(x)
x29 = _resblock(n_filters1=4, n_filters2=256)(x)
x30 = _resblock(n_filters1=4, n_filters2=256)(x)
x31 = _resblock(n_filters1=4, n_filters2=256)(x)
x32 = _resblock(n_filters1=4, n_filters2=256)(x)

x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,
x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,
x31,x32])

x = MaxPooling2D((3, 3), strides=(2,2), padding='same')(x)
residual = Convolution2D(512, 1, strides=1, padding='same')(x)
residual = BatchNormalization()(residual)

x1 = _resblock(n_filters1=8, n_filters2=512)(x)
x2 = _resblock(n_filters1=8, n_filters2=512)(x)
x3 = _resblock(n_filters1=8, n_filters2=512)(x)
x4 = _resblock(n_filters1=8, n_filters2=512)(x)
x5 = _resblock(n_filters1=8, n_filters2=512)(x)
x6 = _resblock(n_filters1=8, n_filters2=512)(x)
x7 = _resblock(n_filters1=8, n_filters2=512)(x)
x8 = _resblock(n_filters1=8, n_filters2=512)(x)
x9 = _resblock(n_filters1=8, n_filters2=512)(x)
x10 = _resblock(n_filters1=8, n_filters2=512)(x)
x11 = _resblock(n_filters1=8, n_filters2=512)(x)
x12 = _resblock(n_filters1=8, n_filters2=512)(x)
x13 = _resblock(n_filters1=8, n_filters2=512)(x)
x14 = _resblock(n_filters1=8, n_filters2=512)(x)
x15 = _resblock(n_filters1=8, n_filters2=512)(x)
x16 = _resblock(n_filters1=8, n_filters2=512)(x)
x17 = _resblock(n_filters1=8, n_filters2=512)(x)
x18 = _resblock(n_filters1=8, n_filters2=512)(x)
x19 = _resblock(n_filters1=8, n_filters2=512)(x)
x20 = _resblock(n_filters1=8, n_filters2=512)(x)
x21 = _resblock(n_filters1=8, n_filters2=512)(x)
x22 = _resblock(n_filters1=8, n_filters2=512)(x)
x23 = _resblock(n_filters1=8, n_filters2=512)(x)
x24 = _resblock(n_filters1=8, n_filters2=512)(x)
x25 = _resblock(n_filters1=8, n_filters2=512)(x)
x26 = _resblock(n_filters1=8, n_filters2=512)(x)
x27 = _resblock(n_filters1=8, n_filters2=512)(x)
x28 = _resblock(n_filters1=8, n_filters2=512)(x)
x29 = _resblock(n_filters1=8, n_filters2=512)(x)
x30 = _resblock(n_filters1=8, n_filters2=512)(x)
x31 = _resblock(n_filters1=8, n_filters2=512)(x)
x32 = _resblock(n_filters1=8, n_filters2=512)(x)

x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,
x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,
x31,x32])

x = GlobalAveragePooling2D()(x)
x = Dense(10, kernel_initializer='he_normal', activation='softmax')(x)

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

model = resnext()

model.summary()

```
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