获取混淆矩阵时出错[重复]

Posted 2023-03-12

【中文标题】获取混淆矩阵时出错[重复]

【英文标题】：Error in getting confusion matrix [duplicate]

【发布时间】：2018-03-08 11:50:00

【问题描述】：

我想得到一个混淆矩阵，代码如下（MNIST分类）：

from sklearn.metrics import confusion_matrix
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.callbacks import TensorBoard
import numpy as np
batch_size = 128
num_classes = 10
epochs = 1

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if 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 /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_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.get_weights()
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(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_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))
y_pred=model.predict(x_test)
confusion_matrix(y_test, y_pred)

但我收到以下错误：

ValueError：无法处理多标签指示器和连续多输出的混合。我认为我错误地解释了 y_pred 的含义或计算错误。

我该如何解决这个问题？

【参考方案1】：

confusion_matrix 期望真实和预测的类标签，而不是单热/概率分布表示。将最后一行替换为以下内容：

confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))

这会将10000x10 格式转换为10000，对应于每个样本的预测类别。

【讨论】：

知道了。谢谢帮助