BN-Inception的代码

以下是BN-Inception的代码示例，供参考：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Dense, Flatten, Input, concatenate, Dropout, BatchNormalization

def inception_module(x, filters):
    f1, f3r, f3, f5r, f5, fpp = filters
    conv1 = Conv2D(f1, (1, 1), padding='same', activation='relu')(x)
    conv3r = Conv2D(f3r, (1, 1), padding='same', activation='relu')(x)
    conv3 = Conv2D(f3, (3, 3), padding='same', activation='relu')(conv3r)
    conv5r = Conv2D(f5r, (1, 1), padding='same', activation='relu')(x)
    conv5 = Conv2D(f5, (5, 5), padding='same', activation='relu')(conv5r)
    pool = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(x)
    convpp = Conv2D(fpp, (1, 1), padding='same', activation='relu')(pool)
    output = concatenate([conv1, conv3, conv5, convpp], axis=-1)
    return output

def BN_Inception():
    input_layer = Input(shape=(224, 224, 3))
    x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu')(input_layer)
    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = BatchNormalization()(x)
    x = Conv2D(64, (1, 1), padding='same', activation='relu')(x)
    x = Conv2D(192, (3, 3), padding='same', activation='relu')(x)
    x = BatchNormalization()(x)
    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = inception_module(x, [64, 96, 128, 16, 32, 32])
    x = inception_module(x, [128, 128, 192, 32, 96, 64])
    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = inception_module(x, [192, 96, 208, 16, 48, 64])
    x = inception_module(x, [160, 112, 224, 24, 64, 64])
    x = inception_module(x, [128, 128, 256, 24, 64, 64])
    x = inception_module(x, [112, 144, 288, 32, 64, 64])
    x = inception_module(x, [256, 160, 320, 32, 128, 128])
    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
    x = inception_module(x, [256, 160, 320, 32, 128, 128])
    x = inception_module(x, [384, 192, 384, 48, 128, 128])
    x = AveragePooling2D((7, 7))(x)
    x = Flatten()(x)
    x = Dropout(0.4)(x)
    output_layer = Dense(1000, activation='softmax')(x)
    model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
    return model

以上代码实现了BN-Inception的网络结构，包含多个Inception模块和批量归一化层。其中，inception_module()函数实现了Inception模块的结构，BN_Inception()函数则定义了整个网络的结构，包括输入层、多个Inception模块、输出层等。