• xddcore

    各位神力AI的前辈,大家好。我是xddcore,目前在北京理工大学就读EE专业。(专业比较偏向于硬件,和ai还是有一丢丢距离的)。最近在研究yolov3的时候发现了神力AI,大概浏览了一下,感觉做得非常不错,理念挺好的!
    今天来分享下自己前不久训练的一个模型。

    声明数据集来源:
    丹尼尔(Kermany),丹尼尔(Daniel);张康 戈德鲍姆 ( Goldbaum),迈克尔(Michael)(2018),“标签光学相干断层扫描(OCT)和胸部X射线图像分类”, Mendeley Data,第2 版,
    http://dx.doi.org/10.17632/rscbjbr9sj.2

    百度网盘下载地址:链接:https://pan.baidu.com/s/1U-xjQHYmhHp-vDpHoScbqw
    提取码:i3f0

    开源链接:https://gitee.com/xddcore/Jetson_Nano/tree/master/example/Classification/x_ray
    TFRecord文件制作:

    import os 
    import tensorflow as tf 
    from PIL import Image  #注意Image,后面会用到
    import matplotlib.pyplot as plt 
    import numpy as np
    
    i = 0
    
    cwd='E:/AI/datasets/chest_xray/test/'
    classes=['normal','pneumonia'] #正常与肺炎
    writer= tf.io.TFRecordWriter("oct_test.tfrecords") #要生成的文件
     
    for index,name in enumerate(classes):
        class_path = cwd + name + '/'
        for img_name in os.listdir(class_path): 
            img_path=class_path+img_name #每一个图片的地址
            
            img=Image.open(img_path)
            img= img.resize((128,128))
            img_raw=img.tobytes()#将图片转化为二进制格式
            example = tf.train.Example(
            features=tf.train.Features(
            feature={
                "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
                'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
            }
            )
            ) #example对象对label和image数据进行封装
            writer.write(example.SerializeToString())  #序列化为字符串
            i = i + 1
            print("已写入"+str(i)+"张图片")
    writer.close()
    

    模型训练:

    import tensorflow as tf
    import tensorflow.keras as keras
    from tensorflow.keras import models, layers, optimizers
    import numpy as np
    import matplotlib.pyplot as plt
    
    # Create a dictionary describing the features.
    image_feature_description = {
        "label": tf.io.FixedLenFeature([], tf.int64),
        "img_raw": tf.io.FixedLenFeature([], tf.string),
    }
    #解析一条example
    def _parse_image_function(example_proto):
      # Parse the input tf.Example proto using the dictionary above.
      return tf.io.parse_single_example(example_proto, image_feature_description)
      
    def read_and_decode(filename): # 读入tfrecords
        i = 0
        img_1 ,label_1 = np.array([]), np.array([])
        img, label = np.array([]), np.array([])
        parsed_image_dataset = tf.data.TFRecordDataset(filename)
        #解析所有example
        parsed_image_dataset = parsed_image_dataset.map (_parse_image_function)
        #取出数据
        for item in parsed_image_dataset:
            #转为向量
            img_1 = np.frombuffer(item['img_raw'].numpy(),dtype=np.uint8).flatten()
            #拼接向量
            img = np.append(img,img_1)
            #转为向量
            label_1 = np.frombuffer(item['label'].numpy(),dtype=np.uint8).flatten()
            #拼接向量
            #获得标签值:0:正常 1:肺炎
            #print((label_1[np.argmax(label_1)]))
            label_1 =np.array([label_1[np.argmax(label_1)]])
            label = np.append(label,label_1)
            i = i + 1
            print("已从TFRecord加载"+str(i)+"张图片...")
        img = img.reshape(-1,128,128,1)
        print(img.shape)
        print(label.shape)
        return img, label
    
    x_train,y_train = read_and_decode("oct_train.tfrecords")
    x_test,y_test = read_and_decode("oct_test.tfrecords")
    x_valid,y_valid = read_and_decode("oct_val.tfrecords")
    
    
    #打印一张照片
    # def show_single_image(img_arr):
    #      plt.imshow(img_arr,cmap='binary')
    #      plt.show()
    # show_single_image(x_train[2])
    
    # 将模型的各层堆叠起来,以搭建 tf.keras.Sequential 模型。为训练选择优化器和损失函数:
                              
    model = models.Sequential([
                               tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu,
                               input_shape=(128,128,1)),
                               tf.keras.layers.MaxPooling2D((2, 2), strides=2),
                               tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),
                               tf.keras.layers.MaxPooling2D((2, 2), strides=2),
                               tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),
                               tf.keras.layers.MaxPooling2D((2, 2), strides=2),
                               tf.keras.layers.Flatten(),
                               tf.keras.layers.Dense(128, activation=tf.nn.relu),
                               layers.Dropout(0.4),
                               tf.keras.layers.Dense(2,  activation=tf.nn.softmax)
    ])
    #model = models.Sequential([layers.Flatten(input_shape=(28, 28)),
    #                           layers.Dense(128, activation='relu'),
    #                           layers.Dense(128, activation='relu'),
    #                           layers.Dense(128, activation='relu'),
    #                           layers.Dropout(0.5),
    #                           layers.Dense(10, activation='softmax')
    #])
    # 编译模型
    model.compile(optimizer=optimizers.SGD(lr=1e-5), loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    # model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    # 打印网络参数量
    model.summary()
    #x_train = tf.expand_dims(x_train,axis=-1)
    #print(x_train.shape)
    #x_train_1 = tf.reshape(x_train,[-1,28,28,1])
    #x_test_1 = tf.reshape(x_test,[-1,28,28,1])
    
    #x_train = x_train.reshape(-1,128,128,1)
    #x_valid = x_valid.reshape(-1,128,128,1)
    #x_test = x_test.reshape(-1,128,128,1)
    
    print(len(model.layers))
    #print(x_train[0])
    # 训练模型
    print(x_train.shape)
    print(y_train.shape)
    print(x_test.shape)
    print(y_test.shape)
    print(x_valid.shape)
    print(y_valid.shape)
    history = model.fit(x_train, y_train, epochs=500, batch_size = 16,
                      validation_data=(x_test,y_test))
                      
    # 验证模型:
    model.evaluate(x_valid,  y_valid, verbose=1)
    
    
    history_dict = history.history         # history对象有一个history成员,它是一个字典,包含训练过程中的所有数据。
    print(history_dict)
    
    # 保存模型权重和偏置
    model.save_weights('./save/1660ti_cnn/save_weights3/')
    
    #保存完整模型(含网络)
    model.save('./save/save_models/1660ti_cnn.h5')
    
    
    
    # 绘制loss曲线
    loss_values = history_dict['loss']
    val_loss_values = history_dict['val_loss']
    epochs = range(1, len(loss_values)+1)
    plt.plot(epochs, loss_values, 'bo', label='Training loss')         # bo代表蓝色圆点
    plt.plot(epochs, val_loss_values, 'b', label='Validation loss')    # bo代表蓝色实线
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()
    plt.show()
    
    # 绘制acc曲线
    acc_values = history_dict['accuracy']
    val_acc_values = history_dict['val_accuracy']
    plt.plot(epochs, acc_values, 'ro', label='Training acc')           # bo代表蓝色圆点
    plt.plot(epochs, val_acc_values, 'r', label='Validation acc')      # bo代表蓝色实线
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Acc')
    plt.legend()
    plt.show()
    

    模型加载与推断

    import tensorflow as tf
    import tensorflow.keras as keras
    from tensorflow.keras import models, layers, optimizers
    import numpy as np
    # 模型加载
    
    model = tf.keras.models.load_model('./save/save_models/1660ti_cnn.h5')
           
    image_value_5 = tf.io.read_file('./pic/n1.jpeg')
    image_value_2 = tf.io.read_file('./pic/n2.jpeg')
    image_value_0 = tf.io.read_file('./pic/p1.jpeg')
    
    #解码为tensor
    image_value_5 = tf.io.decode_jpeg(image_value_5,channels = 1)
    image_value_2 = tf.io.decode_jpeg(image_value_2,channels = 1)
    image_value_0 = tf.io.decode_jpeg(image_value_0,channels = 1)
    
    image_value_5 = tf.image.resize(image_value_5, (128,128))#改变像素值为128*128
    image_value_2 = tf.image.resize(image_value_2, (128,128))#改变像素值为128*128
    image_value_0 = tf.image.resize(image_value_0, (128,128))#改变像素值为128*128
    
    #tensor转array
    image_value_5 = image_value_5.numpy()
    image_value_2 = image_value_2.numpy()
    image_value_0 = image_value_0.numpy()
    
    #转为三维数组
    image_value_5 = image_value_5.reshape(-1,128,128,1)
    image_value_2 = image_value_2.reshape(-1,128,128,1)
    image_value_0 = image_value_0.reshape(-1,128,128,1)
    
    #输入模型进行预测
    predict_value_5 = model.predict(image_value_5,batch_size = None)
    predict_value_2 = model.predict(image_value_2,batch_size = None)
    predict_value_0 = model.predict(image_value_0,batch_size = None)
    
    print("")
    
    if np.argmax(predict_value_5) == 1:
        value_5 = '肺炎'
    else :
        value_5 = '正常'
    if np.argmax(predict_value_2) == 1:
        value_2 = '肺炎'
    else :
        value_2 = '正常'
    if np.argmax(predict_value_0) == 1:
        value_0 = '肺炎'
    else :
        value_0 = '正常'
    print("神经网络信息(ACC:76.67%):")
    model.summary()
    print("导入X光胸片标签: 正常 正常 肺炎")
    print("X光胸片已预测完成,对三张X光胸片预测值分别为: ")
    print("",value_5,value_2,value_0)
    print("武汉 加油!")
    print("-xdd_core 正月初一 25/1/2020")
    

    最后的ACC差不多80%左右,感谢大家阅读,希望以后能多多和大神们学习。

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