how to implement custom metric in keras?

I get this error :

sum() got an unexpected keyword argument 'out'

when I run this code:

import pandas as pd, numpy as np
import keras
from keras.layers.core import Dense, Activation
from keras.models import Sequential

def AUC(y_true,y_pred):
    not_y_pred=np.logical_not(y_pred)
    y_int1=y_true*y_pred
    y_int0=np.logical_not(y_true)*not_y_pred
    TP=np.sum(y_pred*y_int1)
    FP=np.sum(y_pred)-TP
    TN=np.sum(not_y_pred*y_int0)
    FN=np.sum(not_y_pred)-TN
    TPR=np.float(TP)/(TP+FN)
    FPR=np.float(FP)/(FP+TN)
    return((1+TPR-FPR)/2)

# Input datasets

train_df = pd.DataFrame(np.random.rand(91,1000))
train_df.iloc[:,-2]=(train_df.iloc[:,-2]>0.8)*1


model = Sequential()
model.add(Dense(output_dim=60, input_dim=91, init="glorot_uniform"))
model.add(Activation("sigmoid"))
model.add(Dense(output_dim=1, input_dim=60, init="glorot_uniform"))
model.add(Activation("sigmoid"))

model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=[AUC])


train_df.iloc[:,-1]=np.ones(train_df.shape[0]) #bias
X=train_df.iloc[:,:-1].values
Y=train_df.iloc[:,-1].values
print X.shape,Y.shape

model.fit(X, Y, batch_size=50,show_accuracy = False, verbose = 1)

Is it possible to implement a custom metric aside from doing a loop on batches and editing the source code?

The problem is that y_pred and y_true are not NumPy arrays but either Theano or TensorFlow tensors. That's why you got this error.

You can define your custom metrics but you have to remember that its arguments are those tensors – not NumPy arrays.