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### 深度可分离卷积检测算法
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Language: Python
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使用TensorFlow 深度学习框架，使用Keras会大幅缩减代码量
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训练机器：华为Atlas 200 AI开发板（或本地计算机）
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[数据集](../../../medicine-dataset)
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常用的**卷积网络模型**及在ImageNet上的准确率
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|模型|大小|Top-1准确率|Top-5准确率|参数数量|深度|
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|:---:|:---:|:---:|:---:|:---:|:---:|
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|Xception|88 MB|0.790|0.945|22,910,480|126|
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|VGG16|528 MB|0.713|0.901|138,357,544|23|
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|VGG19|549 MB|0.713|0.900|143,667,240|26|
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|ResNet50|98 MB|0.749|0.921|25,636,712|168|
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|ResNet101|171 MB|0.764|0.928|44,707,176|-|
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|ResNet152|232 MB|0.766|0.931|60,419,944|-|
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|ResNet50V2|98 MB|0.760|0.930|25,613,800|-|
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|ResNet101V2|171 MB|0.772|0.938|44,675,560|-|
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|ResNet152V2|232 MB|0.780|0.942|60,380,648|-|
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|ResNeXt50|96 MB|0.777|0.938|25,097,128|-|
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|ResNeXt101|170 MB|0.787|0.943|44,315,560|-|
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|InceptionV3|92 MB|0.779|0.937|23,851,784|159|
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|InceptionResNetV2|215 MB|0.803|0.953|55,873,736|572|
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|MobileNet|16 MB|0.704|0.895|4,253,864|88|
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|MobileNetV2|14 MB|0.713|0.901|3,538,984|88|
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|DenseNet121|33 MB| 0.750|0.923|8,062,504|121|
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|DenseNet169|57 MB| 0.762|0.932|14,307,880|169|
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|DenseNet201|80 MB|0.773|0.936|20,242,984|201|
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|NASNetMobile|23 MB|0.744|0.919|5,326,716|-|
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|NASNetLarge|343 MB|0.825|0.960|88,949,818|-|
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由于硬件条件限制，综合考虑模型的准确率、大小以及复杂度等因素，采用了**Xception模型**，
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该模型是134层（包含激活层，批标准化层等）拓扑深度的卷积网络模型。
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## 检测算法
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```python
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def Xception(include_top=True,
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weights='imagenet',
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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**kwargs)
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# 参数
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# include_top：是否保留顶层的全连接网络
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# weights：None代表随机初始化，即不加载预训练权重。'imagenet’代表加载预训练权重
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# input_tensor：可填入Keras tensor作为模型的图像输入tensor
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# input_shape：可选，仅当include_top=False有效，应为长为3的tuple，指明输入图片的shape，图片的宽高必须大于71，如(150,150,3)
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# pooling：当include_top=False时，该参数指定了池化方式。None代表不池化，最后一个卷积层的输出为4D张量。‘avg’代表全局平均池化，‘max’代表全局最大值池化。
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# classes：可选，图片分类的类别数，仅当include_top=True并且不加载预训练权重时可用
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```
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[基于Xception的模型微调,详细请参考代码](../../../medicine-model/src)
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1. 设置Xception参数
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迁移学习参数权重加载：xception_weights
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```python
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# 设置输入图像的宽高以及通道数
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img_size = (299, 299, 3)
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base_model = keras.applications.xception.Xception(include_top=False,
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weights='..\\resources\\keras-model\\xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
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input_shape=img_size,
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pooling='avg')
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# 全连接层，使用softmax激活函数计算概率值，分类大小是628
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model = keras.layers.Dense(628, activation='softmax', name='predictions')(base_model.output)
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model = keras.Model(base_model.input, model)
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# 锁定卷积层
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for layer in base_model.layers:
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layer.trainable = False
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```
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2. 全连接层训练(v1.0)
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```python
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from base_model import model
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# 设置训练集图片大小以及目录参数
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img_size = (299, 299)
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dataset_dir = '..\\dataset\\dataset'
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img_save_to_dir = 'resources\\image-traing\\'
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log_dir = 'resources\\train-log'
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model_dir = 'resources\\keras-model\\'
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# 使用数据增强
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train_datagen = keras.preprocessing.image.ImageDataGenerator(
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rescale=1. / 255,
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shear_range=0.2,
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width_shift_range=0.4,
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height_shift_range=0.4,
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rotation_range=90,
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zoom_range=0.7,
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horizontal_flip=True,
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vertical_flip=True,
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preprocessing_function=keras.applications.xception.preprocess_input)
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test_datagen = keras.preprocessing.image.ImageDataGenerator(
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preprocessing_function=keras.applications.xception.preprocess_input)
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train_generator = train_datagen.flow_from_directory(
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dataset_dir,
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save_to_dir=img_save_to_dir,
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target_size=img_size,
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class_mode='categorical')
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validation_generator = test_datagen.flow_from_directory(
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dataset_dir,
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save_to_dir=img_save_to_dir,
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target_size=img_size,
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class_mode='categorical')
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# 早停法以及动态学习率设置
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early_stop = EarlyStopping(monitor='val_loss', patience=13)
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=7, mode='auto', factor=0.2)
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tensorboard = keras.callbacks.tensorboard_v2.TensorBoard(log_dir=log_dir)
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for layer in model.layers:
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layer.trainable = False
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# 模型编译
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model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
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history = model.fit_generator(train_generator,
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steps_per_epoch=train_generator.samples // train_generator.batch_size,
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epochs=100,
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validation_data=validation_generator,
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validation_steps=validation_generator.samples // validation_generator.batch_size,
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callbacks=[early_stop, reduce_lr, tensorboard])
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# 模型导出
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model.save(model_dir + 'chinese_medicine_model_v1.0.h5')
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```
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3. 对于顶部的6层卷积层，我们使用数据集对权重参数进行微调
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```python
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# 加载模型
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model=keras.models.load_model('resources\\keras-model\\chinese_medicine_model_v2.0.h5')
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for layer in model.layers:
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layer.trainable = False
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for layer in model.layers[126:132]:
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layer.trainable = True
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history = model.fit_generator(train_generator,
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steps_per_epoch=train_generator.samples // train_generator.batch_size,
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epochs=100,
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validation_data=validation_generator,
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validation_steps=validation_generator.samples // validation_generator.batch_size,
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callbacks=[early_stop, reduce_lr, tensorboard])
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model.save(model_dir + 'chinese_medicine_model_v2.0.h5')
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```
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4. 在后端项目中，我们使用Deeplearn4j调用训练好的模型
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```
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public class CnnModelUtil {
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private static ComputationGraph CNN_MODEL = null;
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/**
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* 中药名字的编码
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*/
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private static final Map<Integer, String> MEDICINE_NAME_MAP = new HashMap<>();
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/**
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* 定义cnn model的文件夹路径
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*/
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private static final String DATA_DIR = System.getProperty("os.name")
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.toLowerCase().contains("windows") ? "D:\\data\\model\\"
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: "./data/model/";
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/**
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* 定义中药编码表的文件名
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*/
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private static final String MEDICINE_LABLE_FILE_NAME = "medicine_name-lable.txt";
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/**
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* 定义模型的文件名
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*/
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private static final String CNN_MODEL_FILE_NAME = "chinese_medicine_model.h5";
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/**
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* 图片的加载器
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*/
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private static final NativeImageLoader IMAGE_LOADER = new NativeImageLoader(299, 299, 3);
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/**
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* 初始化
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*/
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static {
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try {
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CNN_MODEL = KerasModelImport.importKerasModelAndWeights(DATA_DIR + CNN_MODEL_FILE_NAME);
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Files.readAllLines(Paths.get(DATA_DIR, MEDICINE_LABLE_FILE_NAME)).forEach(v -> {
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String[] split = v.split(",");
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MEDICINE_NAME_MAP.put(Integer.valueOf(split[1]), split[0]);
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});
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} catch (IOException | InvalidKerasConfigurationException | UnsupportedKerasConfigurationException e) {
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e.printStackTrace();
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}
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}
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/**
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* 对图像进行预测
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* 对预测的概率值进行排序处理
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* 返回值是概率值前10的中药的名字
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* @param file
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* @return
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* @throws
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*/
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public static Map<String, Float> medicineNamePredict(File file) throws IOException {
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INDArray image = IMAGE_LOADER.asMatrix(file).divi(127.5).subi(1);
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INDArray output = CNN_MODEL.outputSingle(image);
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Map<Integer, Float> resultMap = new HashMap<>();
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float[] floats = output.toFloatVector();
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for (int i = 0; i < floats.length; i++) {
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resultMap.put(i, floats[i]);
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}
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List<Map.Entry<Integer, Float>> resultList = new LinkedList<>(resultMap.entrySet());
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resultList.sort(Map.Entry.comparingByValue(Comparator.reverseOrder()));
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Map<String, Float> medicinePredict = new LinkedHashMap<>();
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resultList.stream().limit(10).forEach(v -> {
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medicinePredict.put(MEDICINE_NAME_MAP.get(v.getKey()), v.getValue());
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});
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return medicinePredict;
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}
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}
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```
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### 模型概览
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[模型详细结构](../../assets/images/model.png)
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**训练过程正确率以及损失函数可视化展示**
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