weighted_loss_function.py

def classification_loss(self,logit,input_y_classification,class_weight):
        labels=input_y_classification
        
        weight_per_label = tf.transpose( tf.matmul(labels, tf.transpose(class_weight)) ) #shape [1, batch_size]
                                                   # this is the weight for each datapoint, depending on its label
        entropy=tf.nn.softmax_cross_entropy_with_logits(logits=logit, labels=labels, name="xent_raw")                                           
        xent = tf.multiply(weight_per_label, entropy) #shape [1, batch_size]
        cost = tf.reduce_mean(xent) #shape 1
        
        self._summaries['classification_loss'] = tf.summary.scalar('classification_loss', cost)
        
        return cost