update

src/medicine/train_cancer/.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

src/medicine/train_cancer/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

src/medicine/train_cancer/.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="D:\Python 3.11\python.exe" project-jdk-type="Python SDK" />
+</project>

src/medicine/train_cancer/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/train_cancer.iml" filepath="$PROJECT_DIR$/.idea/train_cancer.iml" />
+    </modules>
+  </component>
+</project>

src/medicine/train_cancer/.idea/train_cancer.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

src/medicine/train_cancer/README.md

@@ -0,0 +1,49 @@
+#### *第一步、搜集数据集*
+
+- *文件保存在picture文件夹中*
+  - *benign 良性乳腺癌图片*
+  - *malignant 恶性乳腺癌图片*
+  - *normal 正常乳腺癌图片*
+- *以70%作为训练集、30%作为测试集*
+
+#### *第二步、处理数据集*
+
+- *(1) 读取图片*
+- *(2) 使用sklearn.model_selection中的train_test_split 分割数据集*
+- *(3) 使用plt打印图片*
+
+#### *第三步训练*
+- *训练模型选择*
+  - *使用Microsoft提出的DenseNet201框架进行训练*
+  - *DenseNet201包含201层卷积层和全连接层*
+  - *拥有池化操作，非常适合训练模型*
+  
+- *激活函数选择*
+  - *使用softmax作为激活函数*
+  
+  - $$
+    Softmax(z_{i} )=\frac{e^{z_{i}}}{ {\textstyle \sum_{c=1}^{c} e^{z_{c}}}}
+    其中zi为第i个节点的输出值，c为输出节点的个数
+    $$
+  
+- *损失函数选择*
+  
+  - *使用二元交叉熵给出*
+  
+  - $$
+    Loss = \frac{1}{N} \sum_{i=1}^{N}[y_{i}log(p(y_{i})) + (1-y_{i})(1 - log(p(y_{i})))]
+    $$
+  
+- *优化器选择*
+  
+  - *Nadam优化器*
+  - *该优化器综合Adam将RMSprop和动量结合起来*
+  - *优于Adam优化器*
+
+#### *第四步*测试
+
+- *导入图片*
+  - *使用PIL进行读取图片*
+  - *使用test pic进行测试*
+  - *tensorflow load_model进行模型的加载*
+  - *predict进行模型的预测*

src/medicine/train_cancer/loader_picture/change_file.py

@@ -0,0 +1,10 @@
+import os
+path = '../picture/malignant'
+dir = os.listdir(path)
+x = 211
+y = 438
+for i in range(len(dir)):
+    if str(y) in dir[i]:
+        os.rename(path + '/' + dir[i] , path + '/' + "malignant (" + str(x) + ").png")
+        x += 1
+        y += 1

src/medicine/train_cancer/loader_picture/data_loader.py

@@ -0,0 +1,83 @@
+'''
+dir 图片路径
+size 图片尺寸
+'''
+import os
+
+import cv2
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+
+
+class Loader:
+    def __init__(self):
+        # 相对路径老出问题
+        self.benign_train = np.array(self.data_loader('D:\\pro_of_program\\Python\\train_cancer\\picture\\benign', 224))
+        self.malignant_train = np.array(self.data_loader('D:\\pro_of_program\\Python\\train_cancer\\picture\\malignant', 224))
+        self.malignant_test = np.array(self.data_loader('D:\\pro_of_program\\Python\\train_cancer\\picture\\malignant', 224))
+        self.benign_test = np.array(self.data_loader('D:\\pro_of_program\\Python\\train_cancer\\picture\\benign', 224))
+
+        # 创造标签用于标记图像 0矩阵表示良性 1矩阵表示恶性
+        self.benign_train_label = np.zeros(len(self.benign_train))
+        self.benign_test_label = np.zeros(len(self.benign_test))
+        self.malignant_train_label = np.ones(len(self.malignant_train))
+        self.malignant_test_label = np.ones(len(self.malignant_test))
+
+        self.x_train = np.concatenate((self.benign_train , self.malignant_train))
+        self.y_train = np.concatenate((self.benign_train_label , self.malignant_train_label))
+        self.x_test = np.concatenate((self.benign_test , self.malignant_test))
+        self.y_test = np.concatenate((self.benign_test_label , self.malignant_test_label))
+
+
+        s = np.arange(self.x_train.shape[0])
+        np.random.shuffle(s)
+        # 随机打乱train
+        self.x_train = self.x_train[s]
+        self.y_train = self.y_train[s]
+
+        s = np.arange(self.x_train.shape[0])
+        np.random.shuffle(s)
+        # 随机打乱test
+        self.x_test = self.x_test[s]
+        self.y_test = self.y_test[s]
+
+        self.y_train = to_categorical(self.y_train , 2)
+        self.y_test = to_categorical(self.y_test , 2)
+
+        '''
+            参数
+            train_data => x_train
+            train_target => y_ train
+            test_size 样本占比：测试集占总体样本 测试集和训练集3/7分 85%
+            test_size 样本占比：测试集占总体样本 测试集和训练集2/8分 89%
+            random_state 随机种子
+        '''
+        self.train_of_x , self.val_of_x , self.train_of_y , self.val_of_y = train_test_split(
+            self.x_train , self.y_train,
+            # test_size=0.3,
+            test_size=0.2,
+            random_state=11
+        )
+
+    def data_loader(self, dir, size):
+        IMG = []  # 图片
+        # 打开该路径下的图像文件转换为RGB模式并返回numpy数组
+        read = lambda i: np.asarray(Image.open(i).convert("RGB"))
+        home_dir = sorted(os.listdir(dir))
+        n = len(home_dir)
+        for i in range(n):
+            # 获取图片路径
+            path = os.path.join(dir, home_dir[i])
+            l = os.path.split(path)
+
+            if "_mask" not in l[1]:
+                # 正常png图片
+                img = read(path)
+                # <PIL.PngImagePlugin.PngImageFile image mode=RGB size=562x471 at 0x1D3B7465150>
+                # 需要resize缩放为224x224
+                img = cv2.resize(img, (size, size))
+                IMG.append(np.array(img))
+        return IMG

src/medicine/train_cancer/loader_picture/data_single_loader.py

@@ -0,0 +1,11 @@
+import cv2
+from PIL import Image
+
+import numpy as np
+
+
+def load_single(dir , size):
+    read = lambda i: np.asarray(Image.open(i).convert("RGB"))
+    path = dir
+    img = read(path)
+    return np.array(cv2.resize(img, (size, size)))

src/medicine/train_cancer/out.txt

@@ -0,0 +1,4 @@
+node {
+  input: "root"
+  device: "_tf_keras_sequential"
+}

src/medicine/train_cancer/test.py

@@ -0,0 +1,16 @@
+def check(dir_pic , dir_model):
+    import numpy as np
+    from loader_picture import data_single_loader
+    import tensorflow as tf
+
+    pic_test = dir_pic
+    # 转换为numpy格式
+    img_test = data_single_loader.load_single(pic_test , 224)
+    # 需要（1，224，224，3）这种格式输入
+    img_test = np.expand_dims(img_test , axis=0)
+
+    # 载入模型
+    x = tf.keras.models.load_model(dir_model)
+    res = x.predict(img_test)
+    np.set_printoptions(suppress=True)
+    return "良性：" + str(res[0][0]) + "恶性：" + str(res[0][1])

src/medicine/train_cancer/test_information/train_model_and_load_picture.py

@@ -0,0 +1,30 @@
+from loader_picture import data_loader
+import numpy as np
+from matplotlib import pyplot as plt
+
+from train_model.modeling import reduce_study_rate
+from train_model.modeling import modeling
+from train_model.data_gen import data_output
+
+# 载入图片
+load = data_loader.Loader()
+# 获取模型
+models = modeling.breast_train_test()
+# 展现模型
+models.model.summary()
+# data
+data = data_output.gen_data()
+
+# 降低学习率
+reduces = reduce_study_rate.reduce()
+reduces.train()
+
+# 训练+评估
+history = models.model.fit(
+    data.tr_gen.flow(load.train_of_x , load.train_of_y , batch_size=data.batch),
+    steps_per_epoch = load.train_of_x.shape[0] / data.batch,
+    # 训练20次
+    epochs=20,
+    validation_data=(load.val_of_x , load.val_of_y),
+    callbacks=[reduces.learn_control , reduces.checkpoint]
+)

src/medicine/train_cancer/test_res.txt

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src/medicine/train_cancer/train_model/data_gen/data_output.py

@@ -0,0 +1,23 @@
+from keras.preprocessing.image import ImageDataGenerator
+
+class gen_data:
+    def __init__(self):
+        # batch 表示训练样本数
+        # 这里推测20一组为好
+        # 过大会过拟合
+        # self.batch = 20
+        # self.batch = 16
+        self.batch = 32
+        # keras 提供的数据生成器
+        '''
+        zoom_range 随机缩放的幅度
+        rotation_range 数据提升时图片随机转动的角度
+        horizontal_flip 图片随机水平翻转
+        vertical_flip 图片竖直翻转
+        '''
+        self.tr_gen = ImageDataGenerator(
+            zoom_range=2,
+            rotation_range=90,
+            horizontal_flip=True,
+            vertical_flip=True
+        )

src/medicine/train_cancer/train_model/gpu_cpu/check_cpu.py

@@ -0,0 +1,10 @@
+import tensorflow as tf
+
+a = tf.test.is_built_with_cuda()  # 判断CUDA是否可以用
+b = tf.test.is_gpu_available(
+    cuda_only=False,
+    min_cuda_compute_capability=None
+)  # 判断GPU是否可以用
+
+print(a) # 显示True表示CUDA可用
+print(b) # 显示True表示GPU可用

src/medicine/train_cancer/train_model/modeling/modeling.py

@@ -0,0 +1,45 @@
+from keras.models import Sequential
+from keras import layers
+from keras.src.applications import DenseNet201
+from keras.src.optimizers import Nadam
+
+class breast_train_test:
+    def __init__(self):
+        # 构造模型序列
+        self.model = Sequential()
+        # DenseNet201 201层的卷积神经网络
+        net = DenseNet201(
+            # 使用处理图片神经网络
+            weights='imagenet',
+            include_top=False,
+            # 224 * 224 的图片通道数为3 RGB
+            input_shape=(224, 224, 3)
+        )
+        # 学习率 0.0001
+        study_rate = 10**(-4)
+
+        self.build(net , study_rate)
+
+
+    '''
+    resnet：DenseNet201网络
+    study_rate 学习率
+    '''
+    def build(self , resnet , study_rate):
+        self.model.add(resnet)
+        # GlobalAveragePooling2D每个通道值各自加起来再求平均,只剩下个数与平均值两个维度
+        self.model.add(layers.GlobalAveragePooling2D())
+        # dropout 减少中间神经元个数 保留概率为0.5
+        self.model.add(layers.Dropout(0.5))
+        # BatchNormalization 每一个批次的数据中标准化前一层的激活项
+        self.model.add(layers.BatchNormalization())
+        # dense 全连接层 输出维度为2 activation激活函数为softmax在思路整理中给出
+        self.model.add(layers.Dense(2, activation='softmax'))
+        self.model.compile(
+            # 二元交叉熵在思路整理给出
+            loss = "binary_crossentropy",
+            # nadam作为优化器在思路整理中给出
+            optimizer=Nadam(learning_rate=study_rate),
+            # 评估函数
+            metrics=['accuracy']
+        )

src/medicine/train_cancer/train_model/modeling/reduce_study_rate.py

@@ -0,0 +1,24 @@
+# 降低学习率
+from keras.src.callbacks import ReduceLROnPlateau
+from tensorflow.python.keras.callbacks import ModelCheckpoint
+
+class reduce:
+    def train(self):
+        # 控制学习率
+        self.learn_control = ReduceLROnPlateau(
+            monitor='val_accuracy',
+            patience=5,
+            verbose=1,
+            factor=0.2,
+            min_lr=1e-7
+        )
+
+        path = "D:\\pro_of_program\\Python\\train_cancer\\train_model\\third_model"
+        # 保存模型只保存最优解
+        self.checkpoint = ModelCheckpoint(
+            filepath=path,
+            monitor='val_accuracy',
+            verbose=1,
+            save_best_only=True,
+            mode='max'
+        )

src/medicine/train_cancer/train_model/test/predict.py

@@ -0,0 +1,24 @@
+import numpy as np
+import cv2
+from PIL import Image
+import tensorflow as tf
+import sys
+
+def load_single(dir , size):
+    read = lambda i: np.asarray(Image.open(i).convert("RGB"))
+    path = dir
+    img = read(path)
+    return np.array(cv2.resize(img, (size, size)))
+
+def check(dir_pic , dir_model):
+    pic_test = dir_pic
+    # 转换为numpy格式
+    img_test = load_single(pic_test , 224)
+    # 需要（1，224，224，3）这种格式输入
+    img_test = np.expand_dims(img_test , axis=0)
+
+    # 载入模型
+    x = tf.keras.models.load_model(dir_model)
+    res = x.predict(img_test)
+    np.set_printoptions(suppress=True)
+    return f'{res[0][0] * 100 : .4f}%,{res[0][1] * 100 : .4f}%'

src/medicine/train_cancer/train_model/train/train.py

@@ -0,0 +1,30 @@
+from loader_picture import data_loader
+import numpy as np
+from matplotlib import pyplot as plt
+
+from train_model.modeling import reduce_study_rate
+from train_model.modeling import modeling
+from train_model.data_gen import data_output
+
+# 载入图片
+load = data_loader.Loader()
+# 获取模型
+models = modeling.breast_train_test()
+# 展现模型
+models.model.summary()
+# data
+data = data_output.gen_data()
+
+# 降低学习率
+reduces = reduce_study_rate.reduce()
+reduces.train()
+
+# 训练+评估
+history = models.model.fit(
+    data.tr_gen.flow(load.train_of_x , load.train_of_y , batch_size=data.batch),
+    steps_per_epoch = load.train_of_x.shape[0] / data.batch,
+    # 训练30次
+    epochs=50,
+    validation_data=(load.val_of_x , load.val_of_y),
+    callbacks=[reduces.learn_control , reduces.checkpoint]
+)

src/medicine/train_cancer/思路整理.md

@@ -0,0 +1,49 @@
+#### *第一步、搜集数据集*
+
+- *文件保存在picture文件夹中*
+  - *benign 良性乳腺癌图片*
+  - *malignant 恶性乳腺癌图片*
+  - *normal 正常乳腺癌图片*
+- *以70%作为训练集、30%作为测试集*
+
+#### *第二步、处理数据集*
+
+- *(1) 读取图片*
+- *(2) 使用sklearn.model_selection中的train_test_split 分割数据集*
+- *(3) 使用plt打印图片*
+
+#### *第三步训练*
+- *训练模型选择*
+  - *使用Microsoft提出的DenseNet201框架进行训练*
+  - *DenseNet201包含201层卷积层和全连接层*
+  - *拥有池化操作，非常适合训练模型*
+  
+- *激活函数选择*
+  - *使用softmax作为激活函数*
+  
+  - $$
+    Softmax(z_{i} )=\frac{e^{z_{i}}}{ {\textstyle \sum_{c=1}^{c} e^{z_{c}}}}
+    其中zi为第i个节点的输出值，c为输出节点的个数
+    $$
+  
+- *损失函数选择*
+  
+  - *使用二元交叉熵给出*
+  
+  - $$
+    Loss = \frac{1}{N} \sum_{i=1}^{N}[y_{i}log(p(y_{i})) + (1-y_{i})(1 - log(p(y_{i})))]
+    $$
+  
+- *优化器选择*
+  
+  - *Nadam优化器*
+  - *该优化器综合Adam将RMSprop和动量结合起来*
+  - *优于Adam优化器*
+
+#### *第四步*测试
+
+- *导入图片*
+  - *使用PIL进行读取图片*
+  - *使用test pic进行测试*
+  - *tensorflow load_model进行模型的加载*
+  - *predict进行模型的预测*