毕业设计So Easy：卷积神经网络实现中药材识别系统APP

def Xception(include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    **kwargs)
# 参数
# include_top：是否保留顶层的全连接网络
# 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
# input_tensor：可填入Keras tensor作为模型的图像输入tensor
# 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)
# 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。
# 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

# 设置输入图像的宽高以及通道数
img_size = (299, 299, 3)
base_model = keras.applications.xception.Xception(include_top=False,
                                                weights='..\\resources\\keras-model\\xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                                input_shape=img_size,
                                                pooling='avg')
# 全连接层，使用softmax激活函数计算概率值，分类大小是628
model = keras.layers.Dense(628, activation='softmax', name='predictions')(base_model.output)
model = keras.Model(base_model.input, model)
# 锁定卷积层
for layer in base_model.layers:
  layer.trainable = False

from base_model import model
# 设置训练集图片大小以及目录参数
img_size = (299, 299)
dataset_dir = '..\\dataset\\dataset'
img_save_to_dir = 'resources\\image-traing\\'
log_dir = 'resources\\train-log'
model_dir = 'resources\\keras-model\\'
# 使用数据增强
train_datagen = keras.preprocessing.image.ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    width_shift_range=0.4,
    height_shift_range=0.4,
    rotation_range=90,
    zoom_range=0.7,
    horizontal_flip=True,
    vertical_flip=True,
    preprocessing_function=keras.applications.xception.preprocess_input)
test_datagen = keras.preprocessing.image.ImageDataGenerator(
    preprocessing_function=keras.applications.xception.preprocess_input)
train_generator = train_datagen.flow_from_directory(
    dataset_dir,
    save_to_dir=img_save_to_dir,
    target_size=img_size,
    class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
    dataset_dir,
    save_to_dir=img_save_to_dir,
    target_size=img_size,
    class_mode='categorical')
# 早停法以及动态学习率设置
early_stop = EarlyStopping(monitor='val_loss', patience=13)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=7, mode='auto', factor=0.2)
tensorboard = keras.callbacks.tensorboard_v2.TensorBoard(log_dir=log_dir)
for layer in model.layers:
    layer.trainable = False
# 模型编译
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit_generator(train_generator,
                              steps_per_epoch=train_generator.samples // train_generator.batch_size,
                              epochs=100,
                              validation_data=validation_generator,
                              validation_steps=validation_generator.samples // validation_generator.batch_size,
                              callbacks=[early_stop, reduce_lr, tensorboard])
# 模型导出
model.save(model_dir + 'chinese_medicine_model_v1.0.h5')

 # 加载模型
 model=keras.models.load_model('resources\\keras-model\\chinese_medicine_model_v2.0.h5')
 for layer in model.layers:
    layer.trainable = False
 for layer in model.layers[126:132]:
    layer.trainable = True
 history = model.fit_generator(train_generator,
                               steps_per_epoch=train_generator.samples // train_generator.batch_size,
                               epochs=100,
                               validation_data=validation_generator,
                               validation_steps=validation_generator.samples // validation_generator.batch_size,
                               callbacks=[early_stop, reduce_lr, tensorboard])
 model.save(model_dir + 'chinese_medicine_model_v2.0.h5')