我正在使用以下Keras代码对mnist数据进行分类。从sklearn.metrics的confusion_matrix命令中，我得到了混淆矩阵，并且通过TruePositive= sum(numpy.diag(cm1))命令，我能够获取到真阳性。但我对如何获取真阴性、假阳性、假阴性感到困惑。我从这里阅读了解决方案，但用户评论让我感到困惑。请帮助我编写代码来获取这些参数。
from sklearn.metrics import confusion_matriximport kerasfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flattenfrom keras.layers import Conv2D, MaxPooling2Dfrom keras import backend as Kimport numpy as np(x_train, y_train), (x_test, y_test) = mnist.load_data()batch_size = 128num_classes = 10epochs = 1img_rows, img_cols = 28, 28y_test1=y_testif K.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols)else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1)x_train = x_train.astype('float32')x_test = x_test.astype('float32')x_train /= 255x_test /= 255y_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())#model.add(GlobalAveragePooling2D())#model.add(GlobalMaxPooling2D())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(num_classes, activation='softmax'))model.compile(loss=keras.losses.binary_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))pre_cls=model.predict_classes(x_test)cm1 = confusion_matrix(y_test1,pre_cls)print('Confusion Matrix : \n', cm1)TruePositive= sum(np.diag(cm1))回答：
首先，你的代码中有一些遗漏——为了运行，我需要添加以下命令：
import keras(x_train, y_train), (x_test, y_test) = mnist.load_data()添加这些后，针对混淆矩阵cm1：
array([[ 965, 0, 1, 0, 0, 2, 6, 1, 5, 0], [ 0, 1113, 4, 2, 0, 0, 3, 0, 13, 0], [ 8, 0, 963, 14, 5, 1, 7, 8, 21, 5], [ 0, 0, 3, 978, 0, 7, 0, 6, 12, 4], [ 1, 0, 4, 0, 922, 0, 9, 3, 3, 40], [ 4, 1, 1, 27, 0, 824, 6, 1, 20, 8], [ 11, 3, 1, 1, 5, 6, 925, 0, 6, 0], [ 2, 6, 17, 8, 2, 0, 1, 961, 2, 29], [ 5, 1, 2, 13, 4, 6, 2, 6, 929, 6], [ 6, 5, 0, 7, 5, 6, 1, 6, 10, 963]])以下是如何获取每个类别的请求的TP、FP、FN、TN：
真阳性只是对角线上的元素：
TruePositive = np.diag(cm1)TruePositive# array([ 965, 1113, 963, 978, 922, 824, 925, 961, 929, 963])假阳性是相应列的总和，减去对角线上的元素：
FalsePositive = []for i in range(num_classes): FalsePositive.append(sum(cm1[:,i]) - cm1[i,i])FalsePositive# [37, 16, 33, 72, 21, 28, 35, 31, 92, 92]同样，假阴性是相应行的总和，减去对角线上的元素：
FalseNegative = []for i in range(num_classes): FalseNegative.append(sum(cm1[i,:]) - cm1[i,i])FalseNegative# [15, 22, 69, 32, 60, 68, 33, 67, 45, 46]现在，真阴性的计算稍微复杂一些；首先让我们思考一下，真阴性究竟意味着什么，比如对于类别0：它意味着所有被正确识别为“不是0”的样本。因此，我们应该做的实际上是从混淆矩阵中删除相应的行和列，然后将剩余的所有元素加起来：
TrueNegative = []for i in range(num_classes): temp = np.delete(cm1, i, 0) # 删除第i行 temp = np.delete(temp, i, 1) # 删除第i列 TrueNegative.append(sum(sum(temp)))TrueNegative# [8998, 8871, 9004, 8950, 9057, 9148, 9040, 9008, 8979, 8945]让我们进行一个健全性检查：对于每个类别，TP、FP、FN和TN的总和必须等于测试集的大小（这里是10,000）：让我们确认这确实是事实：
l = len(y_test)for i in range(num_classes): print(TruePositive[i] + FalsePositive[i] + FalseNegative[i] + TrueNegative[i] == l)结果是
TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue