Triplet networks using keras for RNN

Question

I am trying to write a custom loss function for triplet loss(using keras), which takes 3 arguments anchor,positive and negative. The triplets are generated using gru layer and the arguments for model.fit is provided through data generators.

The problem I am facing is while training :

TypeError: Cannot convert a symbolic Keras input/output to a numpy array. 
This error may indicate that you're trying to pass a symbolic value to a NumPy 
call, which is not supported. Or, you may be trying to pass Keras symbolic 
inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically 
converting the API call to a lambda layer in the Functional Model.

Implementation of loss function

def batch_hard_triplet_loss(self, anchor_embeddings, pos_embeddings, neg_embeddings, margin):
    def loss(y_true, y_pred):
        '''print(anchor_embeddings)
        print(pos_embeddings)
        print(neg_embeddings)'''
        # distance between the anchor and the positive
        pos_dist = K.sum(K.square(anchor_embeddings - pos_embeddings), axis=-1)
        max_pos_dist = K.max(pos_dist)
        # distance between the anchor and the negative
        neg_dist = K.sum(K.square(anchor_embeddings - neg_embeddings), axis=-1)
        max_neg_dist = K.min(neg_dist)
        # compute loss
        basic_loss = max_pos_dist - max_neg_dist + margin
        tr_loss = K.maximum(basic_loss, 0.0)
        return tr_loss
    #return triplet_loss

    return loss

Can this be because keras is expecting array as returned loss but I am providing a scalar value?