构建Inception_V3模型

Inception V3是Google 在2015年提出的一种卷积神经网络模型，主要用于图像分类和识别任务。下面是使用Keras构建Inception V3模型的示例代码：

from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, Dense, Flatten, concatenate, AveragePooling2D
from keras.models import Model

def inception_module(x, filters):
    branch1x1 = Conv2D(filters[0], (1, 1), padding='same', activation='relu')(x)
    branch3x3 = Conv2D(filters[1], (1, 1), padding='same', activation='relu')(x)
    branch3x3 = Conv2D(filters[2], (3, 3), padding='same', activation='relu')(branch3x3)
    branch5x5 = Conv2D(filters[3], (1, 1), padding='same', activation='relu')(x)
    branch5x5 = Conv2D(filters[4], (5, 5), padding='same', activation='relu')(branch5x5)
    branch_pool = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(x)
    branch_pool = Conv2D(filters[5], (1, 1), padding='same', activation='relu')(branch_pool)
    output = concatenate([branch1x1, branch3x3, branch5x5, branch_pool], axis=3)
    return output

input_shape = (224, 224, 3)
inputs = Input(shape=input_shape)
x = Conv2D(32, (3, 3), strides=(2, 2), padding='same', activation='relu')(inputs)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = Conv2D(80, (1, 1), padding='same', activation='relu')(x)
x = Conv2D(192, (3, 3), padding='same', activation='relu')(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 = Dropout(0.4)(x)
x = Flatten()(x)
outputs = Dense(1000, activation='softmax')(x)

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

这段代码定义了一个函数`inception_module`，用于构建Inception模块。然后，我们使用这个函数构建Inception V3模型，模型的输入是一个`224x224x3`的图像，输出是1000个类别的概率分布。在模型的最后，我们使用了一个Dropout层来防止过拟合，然后将结果展平并通过一个全连接层输出分类结果。