bbbc006.py 文件源码

def _add_cross_entropy(labels, logits, pref):
    """Compute average cross entropy and add to loss collection.
    Args:
        labels: Single dimension labels from distorted_inputs() or inputs().
        logits: Output map from inference().
        pref: Either 'c' or 's', for contours or segments, respectively.
    """
    with tf.variable_scope('{}_cross_entropy'.format(pref)) as scope:
        class_prop = C_CLASS_PROP if pref == 'c' else S_CLASS_PROP
        weight_per_label = tf.scalar_mul(class_prop, tf.cast(tf.equal(labels, 0),
                                                             tf.float32)) + \
                           tf.scalar_mul(1.0 - class_prop, tf.cast(tf.equal(labels, 1),
                                                                   tf.float32))
        cross_entropy = tf.losses.sparse_softmax_cross_entropy(
            labels=tf.squeeze(labels, squeeze_dims=[3]), logits=logits)
        cross_entropy_weighted = tf.multiply(weight_per_label, cross_entropy)
        cross_entropy_mean = tf.reduce_mean(cross_entropy_weighted, name=scope.name)
        tf.add_to_collection('losses', cross_entropy_mean)