Cross-validation with Scikit Flow fails

Question

I am using scikit-learn for evaluating my neural net implemented in tensorFlow and wrapped by an TensorFlow estimator:

import tensorflow.contrib.learn as skflow
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

...

def my_model(X,y):
    ...
    ...
    return skflow.models.logistic_regression(h_drop, y)

def main():
    X_train, X_test, y_train, y_test = train_test_split(data,labels,test_size=0.1, random_state=3)`
    classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=2,batch_size=64,steps=5,optimizer='Adam',learning_rate=1e-4)
    classifier.fit(X_train, y_train)
    cross_val_score(classifier, data, y=labels, cv=10)

cross_val_score results in following error:

TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator TensorFlowEstimator(steps=5, batch_size=64, continue_training=False, verbose=1, n_classes=2, learning_rate=0.0001, clip_gradients=5.0, class_weight=None, params=None, optimizer=Adam) does not.

When i define a scoring method like shown here:

from sklearn import  metrics

cross_val_score(classifier, data, y=labels, cv=10,scoring=metrics.f1_score)

following error occurs:

scoring value looks like it is a metric function rather than a scorer. A scorer should require an estimator as its first parameter. Please use make_scorer to convert a metric to a scorer.

When i use make_scorer like shown here:

cross_val_score(classifier, data, y=labels, cv=10,scoring=metrics.make_scorer(metrics.accuracy_score))

following error occurs:

new_object = klass(**new_object_params) TypeError: init() got an unexpected keyword argument 'params'

Any idea?