Creé una ANN con entradas numéricas y una sola salida categórica que está codificada en caliente para ser 1 de 19 categorías. Configuré mi capa de salida para tener 19 unidades. No sé cómo realizar la matriz de confusión ahora ni cómo classifier.predict() a la luz de esto en lugar de una sola salida binaria. Sigo recibiendo un error que dice que las métricas de clasificación no pueden manejar una combinación de objetivos continuos de múltiples salidas e indicadores de múltiples etiquetas. No estoy seguro de como proceder.

 #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)