深度学习——Kears调用训练好的模型并进行评估绘制混淆矩阵

刺骨的言语ヽ痛彻心扉 2024-03-22 11:55 71阅读 0赞

#### 实验代码： ####

import pickle as my_pickle
    import matplotlib.pyplot as plt
    from keras.utils import np_utils
    from sklearn.metrics import confusion_matrix
    import numpy as np
    import keras
    if __name__ == "__main__":
        # 加载数据集和标签
        T1 = my_pickle.load(open("data/dataset/1_train.p", "rb"))
        D1 = my_pickle.load(open("data/dataset/1_dev.p", "rb"))
        labels1 = my_pickle.load(open("data/dataset/4_lables_train.p", "rb"))
        labels2 = my_pickle.load(open("data/dataset/4_lables_dev.p", "rb"))
    
        Y_train = np_utils.to_categorical(labels1, 6)
        Y_test = np_utils.to_categorical(labels2, 6)
    
        # 加载训练好的模型
        model = keras.models.load_model('C1-100-0.66.hdf5')
    
        # 进行预测
        Y_pred = model.predict(D1)
        y_pred = np.argmax(Y_pred, axis=1)
        y_true = np.argmax(Y_test, axis=1)
    
        # 计算混淆矩阵
        cm = confusion_matrix(y_true, y_pred)
        classes = ['Class 0', 'Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5']
        num_classes = len(classes)
    
        # 绘制混淆矩阵
        plt.figure(figsize=(8, 6))
        plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
        plt.title('Confusion Matrix')
        plt.colorbar()
    
        tick_marks = np.arange(num_classes)
        plt.xticks(tick_marks, classes, rotation=45)
        plt.yticks(tick_marks, classes)
    
        # 在每个矩阵单元格中显示数值
        thresh = cm.max() / 2.
        for i in range(num_classes):
            for j in range(num_classes):
                plt.text(j, i, format(cm[i, j], 'd'),
                         horizontalalignment="center",
                         color="white" if cm[i, j] > thresh else "black")
    
        plt.tight_layout()
        plt.ylabel('True Label')
        plt.xlabel('Predicted Label')
        plt.savefig('confusion_matrix.png')
    
        plt.show()

#### 运行结果： ####