5e039083f5736d9a8f7e060a94e9d3079598acbd,tf_unet/unet.py,Unet,__init__,#Unet#Any#Any#Any#Any#Any#Any#,179

Before Change

        if class_weights is not None:
            class_weights = tf.constant(np.array(class_weights, dtype=np.float32))
            weighted_logits = tf.mul(tf.reshape(logits, [-1, n_class]), class_weights)
            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(weighted_logits, tf.reshape(self.y, [-1, n_class])))
            
        else:
            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(tf.reshape(logits, [-1, n_class]), 
                                                                               tf.reshape(self.y, [-1, n_class])))

After Change

            class_weights = tf.constant(np.array(class_weights, dtype=np.float32))
            labels = tf.reshape(self.y, [-1, n_class])

            weight_map = tf.mul(labels, class_weights)
            weight_map = tf.reduce_sum(weight_map, axis=1)

            loss_map = tf.nn.softmax_cross_entropy_with_logits(tf.reshape(logits, [-1, n_class]), labels)
            weighted_loss = tf.mul(loss_map, weight_map)

            loss = tf.reduce_mean(weighted_loss)
            
        else:
            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(tf.reshape(logits, [-1, n_class]), 
                                                                               tf.reshape(self.y, [-1, n_class])))

Instances