InceptionV3详细代码,带准确率和loss分析，以及ROC曲线（python）

曾经终败给现在 2023-01-03 12:55 96阅读 0赞

from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    import time
    start_time = time.time()
    import numpy as np
    import matplotlib.pyplot as plt
    from keras.callbacks import Callback,ModelCheckpoint
    from keras.models import Model
    from keras.layers import Dense, Input,Conv2D, MaxPooling2D, Dropout, Flatten, Activation, BatchNormalization,GlobalAveragePooling2D
    import keras as ks
    from sklearn.model_selection import train_test_split
    from generator import s2_generator
    from keras.utils.vis_utils import plot_model
    from sklearn.metrics import recall_score,accuracy_score
    from sklearn.metrics import precision_score,f1_score
    from sklearn.metrics import roc_curve
    from sklearn.metrics import auc
    from itertools import cycle
    from scipy import interp
    from sklearn.preprocessing import label_binarize
    import Adapt_categorical_crossentropy as ACC
    from keras.callbacks import Callback,ModelCheckpoint
    from keras.utils import multi_gpu_model
    from keras.optimizers import Adam,SGD,sgd
    from LRFinder import LRFinder
    from clr_callbackR import CyclicLR
    from step_decay import step_decay_schedule
    from keras.applications.vgg19 import VGG19
    from keras.applications.vgg16 import VGG16
    from keras.applications.xception import Xception
    from keras.applications.inception_v3 import InceptionV3
    from keras.applications.inception_resnet_v2 import InceptionResNetV2
    from keras.applications.mobilenet import MobileNet
    from keras.applications.densenet import DenseNet121,DenseNet169,DenseNet201
    from keras.applications.resnet50 import ResNet50
    from keras.applications.nasnet import NASNetLarge
    from keras.applications.nasnet import NASNetMobile
    from keras.layers.core import Reshape
    from keras.layers import multiply
    
    def build_model(nb_classes, input_shape=(299,299,3)):
        inputs_dim = Input(input_shape)
        x = InceptionV3(include_top=False,
                    weights='imagenet',
                    input_tensor=None,
                    input_shape=(299, 299, 3),
                    pooling=max)(inputs_dim)
        print(x.shape)
        # max_pooling = MaxPooling2D(10,10)(x)
        squeeze = GlobalAveragePooling2D()(x)
    
        excitation = Dense(units=2048 // 16)(squeeze)
        excitation = Activation('relu')(excitation)
        excitation = Dense(units=2048)(excitation)
        excitation = Activation('sigmoid')(excitation)
        excitation = Reshape((1, 1, 2048))(excitation)
    
    
        scale = multiply([x, excitation])
    
        x = GlobalAveragePooling2D()(scale)
        dp_1 = Dropout(0.3)(x)
        fc2 = Dense(nb_classes)(dp_1)
        fc2 = Activation('sigmoid')(fc2) #此处注意，为sigmoid函数
        model = Model(inputs=inputs_dim, outputs=fc2)
        return model
    
    class ParallelModelCheckpoint(ModelCheckpoint):
        def __init__(self, model, filepath, monitor='val_loss', verbose=0,
                     save_best_only=True, save_weights_only=False,
                     mode='auto', period=1):
            self.single_model = model
            super(ParallelModelCheckpoint, self).__init__(filepath, monitor, verbose, save_best_only, save_weights_only,
                                                          mode, period)
    
        def set_model(self, model):
            super(ParallelModelCheckpoint, self).set_model(self.single_model)
    
    
    
    if __name__ == '__main__':
        im_size1 = 299
        im_size2 = 299
        channels = 3
        nb_classes = 5
        epochs = 150
        min_lr = 1e-7
        max_lr = 1e-4
    
        X_train = np.load('../Divide_test/x_train_5_right.npy')
        Y_train = np.load('../Divide_test/y_train_5_right.npy')
        print("全部数据形状")
        print(X_train.shape)
        print(Y_train.shape)
    
        X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.1, random_state=666)
        # X_train, X_test, Y_train, Y_test = train_test_split(X_train, Y_train, test_size=0.04, random_state=66)
        X_test = np.load('../Divide_test/x_test_5_right.npy')
        Y_test = np.load('../Divide_test/y_test_5_right.npy')
        print("训练集形状")
        print(X_train.shape)
        print(Y_train.shape)
        print("验证集形状")
        print(X_valid.shape)
        print(Y_valid.shape)
        print("测试集形状")
        print(X_test.shape)
        print(Y_test.shape)
    
        Y_train = np.asarray(Y_train,np.uint8)
        Y_valid = np.asarray(Y_valid,np.uint8)
        Y_test  = np.asarray(Y_test,np.uint8)
    
        X_valid = np.asarray(X_valid, np.float32) / 255.
        X_test  = np.asarray(X_test,np.float32)  / 255.
    
    
    
        # This is the model we will train
        # single_model = NASNetMobile(weights=None, include_top=True, input_shape=(im_size1, im_size2, channels),
        # classes=nb_classes)
        single_model = build_model(nb_classes, input_shape=(im_size1, im_size2, channels))
        plot_model(single_model, to_file="../images/images/SE-InceptionV3_model.png", show_shapes=True, show_layer_names=True)
        model = multi_gpu_model(single_model, 2)
        early_stopping = ks.callbacks.EarlyStopping(monitor='val_acc', patience=5)
    
        filepath = './model/SE-InceptionV3_model.h5'
        saveBestModel = ParallelModelCheckpoint(single_model,'./model/SE-InceptionV3_model.h5',monitor='val_acc',
                                                     verbose=1, save_best_only=True, mode='auto')
    
        tensorboard = ks.callbacks.TensorBoard(log_dir=r'./RESNET_ray/SE-InceptionV3_model')
        opt = Adam(lr=1e-4)
        model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])# binary_crossentropy,categorical_crossentropy
        model.summary()
    
        batch_size=100
        a = X_train.shape[0]
        ''' 学习率设置：lr_finder：找寻最优学习速率的系统化方法 　　　　　　reduce_lr：一般设置学习率方法，验证准确率不提高就减少准确率 　clr_triangular：周期性学习率 //效果不是太好，可能参数不对 step_decay : 学习速率退火的最流行方式是「步衰减」（Step Decay），类似于reduce_lr 　 '''
        # lr_finder = LRFinder(min_lr=1e-7, max_lr=1e-4, steps_per_epoch=np.ceil(a // batch_size),
        # epochs=10)
        reduce_lr = ks.callbacks.ReduceLROnPlateau(monitor='val_acc', factor=0.3, verbose=1, patience=2,
                                                   min_lr=1e-7)
        # clr_triangular = CyclicLR(base_lr=1e-6,max_lr=1e-4,mode='triangular',step_size=10*np.ceil(a // batch_size))
        #
        # step_decay = step_decay_schedule(initial_lr=1e-4, decay_factor=0.75, step_size=2)
    
        history = model.fit_generator(generator=s2_generator(X_train, Y_train, batch_size,a),
                            steps_per_epoch=int(X_train.shape[0] / batch_size)+ 1,
                            epochs=epochs, validation_data=(X_valid, Y_valid), verbose=1,
                            callbacks=[early_stopping, saveBestModel, tensorboard,reduce_lr])
        # lr_finder.plot_loss()
        # lr_finder.plot_lr()
    
    # 訓練集和驗證機——損失函數和準確率變化曲線
        val_loss = history.history['val_loss']
        val_acc = history.history['val_acc']
        train_loss = history.history['loss']
        train_acc = history.history['acc']
    
    # 損失函數曲線
        plt.figure(figsize=(10, 4))
        plt.ylim(0, 1.5)
        plt.plot(train_loss, 'b', label='Training loss')
        plt.plot(val_loss, 'r', label='Validation loss')
        plt.xlabel('Epochs')
        plt.ylabel('Loss')
        plt.legend()
        plt.savefig("../images/ROC/SE-InceptionV3_model_5分类_loss.png")
        # plt.show()
    
    # 準確率變化曲線
        plt.figure(figsize=(10, 4))
        plt.ylim(0, 1)
        plt.plot(train_acc, 'b', label='Training Accuracy')
        plt.plot(val_acc, 'r', label='Validation Accuracy')
        plt.xlabel('Epochs')
        plt.ylabel('Accuracy')
        plt.legend()
        plt.savefig("../images/ROC/SE-InceptionV3_model_5分类_Accuracy.png")
        # plt.show()
    
    
        print("Predicting")
        Y_pred = model.predict(X_test)
        Y_pred = [np.argmax(y) for y in Y_pred]  # 取出y中元素最大值所对应的索引
        Y_test = [np.argmax(y) for y in Y_test]
    
        # Binarize the output
        Y_test = label_binarize(Y_test, classes=[i for i in range(nb_classes)])
        Y_pred = label_binarize(Y_pred, classes=[i for i in range(nb_classes)])
    
        # micro：多分类　　
        # weighted：不均衡数量的类来说，计算二分类metrics的平均
        # macro：计算二分类metrics的均值，为每个类给出相同权重的分值。
        precision = precision_score(Y_test, Y_pred, average='micro')
        recall = recall_score(Y_test, Y_pred, average='micro')
        f1_score = f1_score(Y_test, Y_pred, average='micro')
        accuracy_score = accuracy_score(Y_test, Y_pred)
        print("Precision_score:", precision)
        print("Recall_score:", recall)
        print("F1_score:", f1_score)
        print("Accuracy_score:", accuracy_score)
    
        # roc_curve:真正率（True Positive Rate , TPR）或灵敏度（sensitivity）
        # 横坐标：假正率（False Positive Rate , FPR）
    
        # Compute ROC curve and ROC area for each class
        fpr = dict()
        tpr = dict()
        roc_auc = dict()
        for i in range(nb_classes):
            fpr[i], tpr[i], _ = roc_curve(Y_test[:, i], Y_pred[:, i])
            roc_auc[i] = auc(fpr[i], tpr[i])
    
        # Compute micro-average ROC curve and ROC area
        fpr["micro"], tpr["micro"], _ = roc_curve(Y_test.ravel(), Y_pred.ravel())
        roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
    
        # Compute macro-average ROC curve and ROC area
    
        # First aggregate all false positive rates
        all_fpr = np.unique(np.concatenate([fpr[i] for i in range(nb_classes)]))
    
        # Then interpolate all ROC curves at this points
        mean_tpr = np.zeros_like(all_fpr)
        for i in range(nb_classes):
            mean_tpr += interp(all_fpr, fpr[i], tpr[i])
    
        # Finally average it and compute AUC
        mean_tpr /= nb_classes
    
        fpr["macro"] = all_fpr
        tpr["macro"] = mean_tpr
        roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
    
        # Plot all ROC curves
        lw = 2
        plt.figure()
        plt.plot(fpr["micro"], tpr["micro"],
                 label='micro-average ROC curve (area = {0:0.2f})'
                       ''.format(roc_auc["micro"]),
                 color='deeppink', linestyle=':', linewidth=4)
    
        plt.plot(fpr["macro"], tpr["macro"],
                 label='macro-average ROC curve (area = {0:0.2f})'
                       ''.format(roc_auc["macro"]),
                 color='navy', linestyle=':', linewidth=4)
    
        colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
        for i, color in zip(range(nb_classes), colors):
            plt.plot(fpr[i], tpr[i], color=color, lw=lw,
                     label='ROC curve of class {0} (area = {1:0.2f})'
                           ''.format(i, roc_auc[i]))
    
        plt.plot([0, 1], [0, 1], 'k--', lw=lw)
        plt.xlim([0.0, 1.0])
        plt.ylim([0.0, 1.05])
        plt.xlabel('False Positive Rate')
        plt.ylabel('True Positive Rate')
        plt.title('Some extension of Receiver operating characteristic to multi-class')
        plt.legend(loc="lower right")
        plt.savefig("../images/ROC/SE-InceptionV3_model_5分类.png")
        # plt.show()
    
    
    
        # plt.figure()
        # plt.plot(clr_triangular.history['iterations'], clr_triangular.history['lr'])
        # plt.xlabel('Training Iterations')
        # plt.ylabel('Learning Rate')
        # plt.title("CLR - 'exp_range' Policy")
        # plt.savefig("../images/images/CLR-'exp_range' Policy.png")
        # plt.show()
    
    
    
        #　二分类
        # roc_curve:真正率（True Positive Rate , TPR）或灵敏度（sensitivity）
        # 横坐标：假正率（False Positive Rate , FPR）
        # fpr, tpr, thresholds_keras = roc_curve(Y_test, Y_pred)
        # auc = auc(fpr, tpr)
        # print("AUC : ", auc)
        # plt.figure()
        # plt.plot([0, 1], [0, 1], 'k--')
        # plt.plot(fpr, tpr, label='Keras (area = {:.3f})'.format(auc))
        # plt.xlabel('False positive rate')
        # plt.ylabel('True positive rate')
        # plt.title('ROC curve')
        # plt.legend(loc='best')
        # plt.savefig("../images/ROC/SE-InceptionV3_model.png")
        # plt.show()
    
        print("--- %s seconds ---" % (time.time() - start_time))