title: classification metrics can't handle a mix of continuous-multioutput and multi-label-indicator targets
body: <p>I have created an ANN with numerical inputs and a single categorical output which is one hot encoded to be 1 of 19 categories. I set my output layer to have 19 units. I don't know how to perform the confusion matrix now nor how to classifier.predict() in light of this rather than a single binary output. I keep getting an error saying classification metrics can't handle a mix of continuous-multioutput and multi-label-indicator targets. Not sure how to proceed.</p>
  
  <pre><code>#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 &gt; 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)
  </code></pre>