200 lines
10 KiB
Python
200 lines
10 KiB
Python
from all_models_tools.all_model_tools import call_back
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from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image
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from draw_tools.draw import plot_history, Confusion_Matrix_of_Two_Classification
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from keras import regularizers
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from Load_process.Load_Indepentend import Load_Indepentend_Data
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from _validation.ValidationTheEnterData import validation_the_enter_data
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from keras.layers import GlobalAveragePooling2D, Dense, Dropout
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from keras.applications import Xception
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from Load_process.file_processing import Process_File
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from merge_class.merge import merge
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from draw_tools.Grad_cam import Grad_CAM
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from sklearn.metrics import confusion_matrix
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from keras.models import Model
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from keras.optimizers import SGD
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from Image_Process.Image_Generator import Image_generator
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import pandas as pd
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import keras
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import numpy as np
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import time
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class experiments():
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def __init__(self, tools, status):
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'''
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parmeter:
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* validation_obj : 驗證物件
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* cut_image : 切割影像物件
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* image_processing : 讀檔與讀檔資料處理物件
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* merge : 合併物件
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* model_name: 取名，告訴我我是用哪個模型(可能是預處理模型/自己設計的模型)
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* generator_batch_size: 每一批次要讀多少檔出來
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* epoch: 訓練次數
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* train_batch_size: 訓練時要多少批次的影像為1組
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* generator_batch_size: 減少圖片數量對GPU記憶體的用量, 減少張數用的
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* experiment_name : 本次實驗名稱
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* convolution_name : Pre-train model 的最後一層Convolotion的名稱
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'''
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self.Topic_Tool = tools
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self.validation_obj = validation_the_enter_data() # 呼叫驗證物件
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self.cut_image = Load_Indepentend_Data(self.Topic_Tool.Get_Data_Label(), self.Topic_Tool.Get_OneHot_Encording_Label()) # 呼叫切割影像物件
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self.image_processing = Read_image_and_Process_image()
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self.merge = merge()
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self.model_name = "Xception" # 取名，告訴我我是用哪個模型(可能是預處理模型/自己設計的模型)
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self.experiment_name = "Xception Skin to train Normal stomach cancer"
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# self.file_name = "Remove background of Chickenpox with normal image"
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self.generator_batch_size = 50
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self.epoch = 10000
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self.train_batch_size = 128
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self.layers = 1
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self.convolution_name = self.get_layer_name(self.model_name)
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self.Grad = ""
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self.Status = status
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pass
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def processing_main(self, train, train_label, counter):
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Train, Test, Validation = self.Topic_Tool.Get_Save_Roots(self.Status) # 要換不同資料集就要改
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start = time.time()
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self.cut_image.process_main(Test, Validation) # 呼叫處理test Data與Validation Data的function
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end = time.time()
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print("讀取testing與validation資料(154)執行時間：%f 秒\n" % (end - start))
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Generator = Image_generator("", "")
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# 將處理好的test Data 與 Validation Data 丟給這個物件的變數
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self.test, self.test_label = self.cut_image.test, self.cut_image.test_label
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self.validation, self.validation_label = self.cut_image.validation, self.cut_image.validation_label
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self.Grad = Grad_CAM(self.Topic_Tool.Get_Data_Label(), self.test_label, self.experiment_name, self.convolution_name)
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cnn_model = self.construct_model() # 呼叫讀取模型的function
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# model_dir = '../save_the_best_model/Topic/Remove background with Normal image/best_model( 2023-10-17 )-2.h5' # 這是一個儲存模型權重的路徑，每一個模型都有一個自己權重儲存的檔
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# if os.path.exists(model_dir): # 如果這個檔案存在
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# cnn_model.load_weights(model_dir) # 將模型權重讀出來
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# print("讀出權重\n")
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Optimizer = SGD(learning_rate = 0.045, momentum = 0.9) # 決定優化器與學習率
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cnn_model.compile(
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loss = "binary_crossentropy",
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optimizer = Optimizer,
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metrics=
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[
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'accuracy',
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keras.metrics.Precision(name='precision'),
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keras.metrics.Recall(name='recall'),
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keras.metrics.AUC(name = 'AUC'),
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]
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)
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train_data = Generator.Generator_Content(5) # 叫入ImageGeneratorr的物件，為了要讓我訓練時可以分批次讀取資料，GPU記憶體才不會爆
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cnn_model.summary() # 顯示模型架構
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print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start))
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history = cnn_model.fit(
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train_data.flow(train, train_label, batch_size = self.generator_batch_size),
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# x = train,
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# y = train_label,
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epochs = self.epoch,
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batch_size = self.train_batch_size,
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validation_data = (self.validation, self.validation_label),
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callbacks = call_back(self.experiment_name, counter) # 呼叫 call back list
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# callbacks = call_back("best_model", self.counter) # 呼叫 call back list
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)
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Matrix = self.record_matrix_image(cnn_model, self.experiment_name, counter) # 紀錄混淆矩陣的function
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loss, accuracy, precision, recall, AUC = cnn_model.evaluate(self.test, self.test_label) # 預測結果
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# 防分母為0的時候
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if recall == 0 or precision == 0:
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f = 0
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else:
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f = (1 + 0.5 * 0.5) * ((recall * precision) / (0.5 * 0.5 * recall + precision))
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print(self.record_everyTime_test_result(loss, accuracy, precision, recall, AUC, f, counter, self.experiment_name, Matrix)) # 紀錄當前訓練完之後的預測結果，並輸出成csv檔
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plot_history(history, "train" + str(counter), self.experiment_name) # 將訓練結果化成圖，並將化出來的圖丟出去儲存
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self.Grad.process_main(cnn_model, counter, self.test)
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return loss, accuracy, precision, recall, AUC, f
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def construct_model(self):
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'''決定我這次訓練要用哪個model'''
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xception = Xception(include_top = False, weights = "imagenet", input_shape = (512, 512, 3))
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Flatten = GlobalAveragePooling2D()(xception.output)
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output = Dense(units = 1370, activation = "relu", kernel_regularizer = regularizers.L2())(Flatten)
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output = Dropout(0.6)(output)
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output = Dense(units = 2, activation = "softmax")(output)
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cnn_model = Model(inputs = xception.input, outputs = output)
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return cnn_model
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def record_matrix_image(self, cnn_model : Model, model_name, index):
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'''劃出混淆矩陣(熱力圖)'''
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result = cnn_model.predict(self.test) # 利用predict function來預測結果
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result = np.argmax(result, axis = 1) # 將預測出來的結果從one-hot encoding轉成label-encoding
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y_test = np.argmax(self.test_label, axis = 1)
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matrix = confusion_matrix(result, y_test, labels = [0, 1]) # 丟入confusion matrix的function中，以形成混淆矩陣
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Confusion_Matrix_of_Two_Classification(model_name, matrix, index) # 呼叫畫出confusion matrix的function
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return matrix.real
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def record_everyTime_test_result(self, loss, accuracy, precision, recall, auc, f, indexs, model_name, Matrix):
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'''記錄我單次的訓練結果並將它輸出到檔案中'''
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File = Process_File()
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Dataframe = pd.DataFrame(
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{
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"model_name" : str(model_name),
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"loss" : "{:.2f}".format(loss),
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"precision" : "{:.2f}%".format(precision * 100),
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"recall" : "{:.2f}%".format(recall * 100),
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"accuracy" : "{:.2f}%".format(accuracy * 100),
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"f" : "{:.2f}%".format(f * 100),
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"AUC" : "{:.2f}%".format(auc * 100)
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}, index = [indexs])
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File.Save_CSV_File("train_result", Dataframe)
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# File.Save_TXT_File("Matrix_Result : " + str(Matrix), model_name + "_train" + str(indexs))
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return Dataframe
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def get_layer_name(self, model_name):
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if(self.validation_obj.validation_string(model_name, "VGG19")):
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return "block5_conv4"
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if(self.validation_obj.validation_string(model_name, "ResNet50")):
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return "conv5_block3_3_conv"
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if(self.validation_obj.validation_string(model_name, "Xception")):
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return "block14_sepconv2"
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if(self.validation_obj.validation_string(model_name, "DenseNet121")):
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return "conv5_block16_concat"
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if(self.validation_obj.validation_string(model_name, "InceptionResNetV2")):
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return "conv_7b"
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if(self.validation_obj.validation_string(model_name, "InceptionV3")):
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return "conv2d_93"
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if(self.validation_obj.validation_string(model_name, "MobileNet")):
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return "conv_pw_13"
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if(self.validation_obj.validation_string(model_name, "MobileNetV2")):
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return "Conv_1"
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if(self.validation_obj.validation_string(model_name, "NASNetLarge")):
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return "separable_conv_2_normal_left5_18"
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if(self.validation_obj.validation_string(model_name, "ResNet101")):
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return "conv5_block3_3_conv"
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if(self.validation_obj.validation_string(model_name, "ResNet101V2")):
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return "conv5_block3_3_conv"
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if(self.validation_obj.validation_string(model_name, "ResNet152")):
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return "conv5_block3_3_conv"
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if(self.validation_obj.validation_string(model_name, "ResNet152V2")):
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return "conv5_block3_out"
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if(self.validation_obj.validation_string(model_name, "ResNet50v2")):
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return "conv5_block3_out"
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if(self.validation_obj.validation_string(model_name, "VGG16")):
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return "block5_conv3"
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