分类指标无法处理连续多输出和多标签指标目标的混合

Question

我创建了一个带有数字输入和单个分类输出的ANN,这是一个热门编码为19个类别中的1个.我将输出图层设置为19个单位.我现在不知道如何执行混淆矩阵,也不知道如何根据这个而不是单个二进制输出来执行classifier.predict().我不断收到错误消息称分类指标无法处理连续多输出和多标签指标目标的混合.不知道如何继续.

#Importing Datasets
dataset=pd.read_csv('Data.csv')
x = dataset.iloc[:,1:36].values # lower bound independent variable to upper bound in a matrix (in this case only 1 column 'NC')
y = dataset.iloc[:,36:].values # dependent variable vector
print(x.shape)
print(y.shape)

#One Hot Encoding fuel rail column
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_y= LabelEncoder()
y[:,0]=labelencoder_y.fit_transform(y[:,0])
onehotencoder= OneHotEncoder(categorical_features=[0])
y = onehotencoder.fit_transform(y).toarray()
print(y[:,0:])

print(x.shape)
print (y.shape)


#splitting data into Training and Test Data
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.1,random_state=0)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
#x_train = sc.fit_transform(x_train)
#x_test=sc.transform(x_test)
y_train = sc.fit_transform(y_train)
y_test=sc.transform(y_test)

# PART2 - Making ANN, deep neural network

#Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense


#Initialising ANN
classifier = Sequential()
#Adding the input layer and first hidden layer
classifier.add(Dense(activation= 'relu', input_dim =35, units=2, kernel_initializer="uniform"))#rectifier activation function, include all input with one hot encoding
#Adding second hidden layer
classifier.add(Dense(activation= 'relu', units=2, kernel_initializer="uniform")) #rectifier activation function
#Adding the Output Layer
classifier.add(Dense(activation='softmax', units=19, kernel_initializer="uniform")) 
#Compiling ANN - stochastic gradient descent
classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])#stochastic gradient descent

#Fit ANN to training set

#PART 3 - Making predictions and evaluating the model
#Fitting classifier to the training set
classifier.fit(x_train, y_train, batch_size=10, epochs=100)#original batch is 10 and epoch is 100

#Predicting the Test set rules
y_pred = classifier.predict(x_test)
y_pred = (y_pred > 0.5) #greater than 0.50 on scale 0 to 1
print(y_pred)

#Making confusion matrix that checks accuracy of the model
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

Answer 1

y_pred = (y_pred > 0.5) 

输出布尔矩阵.问题是它具有与以前相同的形状,但是当您评估准确性时,您需要一个标签矢量.

要执行此操作,请np.argmax(y_pred, axis=1)改为输出正确的标签.

Answer 2

总结一下：用这段代码你应该得到你的矩阵

y_pred=model.predict(X_test) 
y_pred=np.argmax(y_pred, axis=1)
y_test=np.argmax(y_test, axis=1)
cm = confusion_matrix(y_test, y_pred)
print(cm)