DenseNet和Inception结合代码

以下是一个DenseNet和Inception结合的示例代码，实现了一个包含两个Dense模块和两个Inception模块的神经网络：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Concatenate, Conv2D, MaxPooling2D, Flatten, Dropout
from tensorflow.keras.models import Model

def conv_block(inputs, filters):
    x = Conv2D(filters, kernel_size=(3,3), padding='same', activation='relu')(inputs)
    x = BatchNormalization()(x)
    return x

def dense_block(inputs, n_layers, growth_rate):
    x = inputs
    for i in range(n_layers):
        conv = conv_block(x, growth_rate)
        x = Concatenate()([x, conv])
    return x

def inception_block(inputs, filters):
    tower_1 = Conv2D(filters[0], (1,1), padding='same', activation='relu')(inputs)
    tower_1 = Conv2D(filters[1], (3,3), padding='same', activation='relu')(tower_1)

    tower_2 = Conv2D(filters[2], (1,1), padding='same', activation='relu')(inputs)
    tower_2 = Conv2D(filters[3], (5,5), padding='same', activation='relu')(tower_2)

    tower_3 = MaxPooling2D((3,3), strides=(1,1), padding='same')(inputs)
    tower_3 = Conv2D(filters[4], (1,1), padding='same', activation='relu')(tower_3)

    output = Concatenate()([tower_1, tower_2, tower_3])
    return output

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

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

x = dense_block(x, 2, 32)
x = inception_block(x, [64, 96, 16, 32, 32])
x = dense_block(x, 2, 64)
x = inception_block(x, [128, 128, 32, 64, 64])

x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
outputs = Dense(10, activation='softmax')(x)

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

在这个示例代码中，我们定义了两个函数`dense_block`和`inception_block`，分别用于构建Dense模块和Inception模块。在主函数中，我们使用这两个模块来构建一个神经网络，其中包含两个Dense模块和两个Inception模块。最后，我们定义了一个包含10个输出的全连接层作为网络的输出。

这个神经网络的输入是一个大小为224x224x3的图像，输出是一个10维的向量，表示图像属于10个不同类别中的哪一个。