python - SciKit-learn grid search own scoring object syntax -

i want search hyper-parameters in convolutional net built keras. using kerasclassifier , gridsearchcv scikit-learn in line intro given here machinelearningmastery. typically scikit-learn optimizes on 'accuracy', network runs image segmentation optimizing jaccard index. need define own scoring object grid search using make_scorer explaned here make_scorer , here defining scoring strategy. code section below shows implementation, getting error in model.compile(optimizer=optimizer, loss=eval_loss, metrics=(['eval_func']), not know specify in metrics. default 'accuracy' assume in case 'eval_func' (which works when not doing grid search) or 'score' neither of these works in case.

what right syntax?

def eval_func(y_true, y_pred):     '''evaluation function dice or jaccard, set global var jaccard=true'''     if jaccard:         return jaccard_index(y_true, y_pred)     else:         return dice_coef(y_true, y_pred)   def get_unet(batch_size=32, decay=0, dropout_rate=0.5, weight_constraint=0):     '''create u-net model'''     dim = 32          inputs = input((3, image_cols, image_rows)) # modified take 3 color channel input     conv1 = convolution2d(dim, 3, 3, activation='relu', border_mode='same', w_constraint=weight_constraint)(inputs)     conv1 = convolution2d(dim, 3, 3, activation='relu', border_mode='same', w_constraint=weight_constraint)(conv1)     pool1 = maxpooling2d(pool_size=(2, 2))(conv1)     pool1 = dropout(dropout_rate)(pool1) # dropout added layers      ... more layers ...      conv10 = convolution2d(1, 1, 1, activation='sigmoid')(conv9)      model = model(input=inputs, output=conv10)      optimizer = adam(lr=lr, decay=decay)        model.compile(optimizer=optimizer, loss=eval_loss, metrics=(['eval_func'])      return model  def run_grid_search():     '''optimize model parameters grid search'''      ... loading data ...      model = kerasclassifier(build_fn=get_unet, verbose=1, nb_epoch=num_epoch, shuffle=true)     # define grid search parameters     batch_size = [16, 32, 48]     decay = [0, 0.002, 0.004]     param_grid = dict(batch_size=batch_size, decay=decay)      # create scoring object     score = make_scorer(eval_func, greater_is_better=true)      grid = gridsearchcv(estimator=model, param_grid=param_grid, scoring=score, n_jobs=1, verbose=1)     grid_result = grid.fit(x_aug, y_aug)  

here last part of error getting both using 'eval_func' , 'score':

file "c:\program files\anaconda2\lib\site-packages\keras\metrics.py", line 216 , in return get_from_module(identifier, globals(), 'metric') file "c:\program files\anaconda2\lib\site-packages\keras\utils\generic_utils.p y", line 16, in get_from_module str(identifier)) exception: invalid metric: eval_func