请问 您在loss中加入
with tf.variable_scope("losses_avg"):
loss_moving_avg = tf.get_variable("training_loss",
initializer=100.0,
trainable=False)
lm = loss_moving_avg.assign(loss_moving_avg * 0.9 + loss * 0.1)
tf.summary.scalar("loss_avg/total_loss", lm)
with tf.control_dependencies([lm]):
loss = tf.identity(loss)
为什么要加入一个losses_avg
请问 您在loss中加入
with tf.variable_scope("losses_avg"):
loss_moving_avg = tf.get_variable("training_loss",
initializer=100.0,
trainable=False)
lm = loss_moving_avg.assign(loss_moving_avg * 0.9 + loss * 0.1)
tf.summary.scalar("loss_avg/total_loss", lm)
为什么要加入一个losses_avg