net.py 文件源码

def _loss(self, labels, logits):
        float_labels = tf.cast(labels, tf.float32)

        epsilon = tf.constant(value=1e-4)
        softmax = tf.nn.softmax(logits) + epsilon
        cross_entropy = -tf.reduce_sum(float_labels * tf.log(softmax), reduction_indices=[-1])
        cross_entropy_mean = tf.reduce_mean(cross_entropy)

        total_pixels = tf.constant(value=conf.width * conf.height, dtype=tf.float32)
        valid_pixels = tf.reduce_sum(float_labels)
        loss = tf.divide(tf.multiply(cross_entropy_mean, total_pixels), valid_pixels)

        return loss