from sklearn.metrics import roc_curve, auc

history = model.fit(x_train, y_train, validation_data=(
        x_test, y_test), epochs=num_of_epochs, batch_size=batch_size, verbose=1)

y_pred = model.predict(x_test).ravel()

nn_fpr_keras, nn_tpr_keras, nn_thresholds_keras = roc_curve(y_test, y_pred)
auc_keras = auc(nn_fpr_keras, nn_tpr_keras)
plt.plot(nn_fpr_keras, nn_tpr_keras, marker='.', label='Neural Network (auc = %0.3f)' % auc_keras)