reg_loss = tf.reduce_mean([tf.reduce_mean(reg_loss)
for reg_loss in reg_losses])
loss = loss + reg_loss
tf.summary.scalar("loss",loss,[engine.logging.CONSOLE,engine.logging.LOG])
////////////////////////////////////
// This should probably be refactored into an application class
// Averages are in name_scope for Tensorboard naming; summaries are outside for console naming
After Change
for tag,avg in averaged_summaries:
tf.summary.scalar(tag, avg,[engine.logging.CONSOLE,engine.logging.LOG])
else:
console_outputs+=tower_console_outputs[0]
// Track the moving averages of all trainable variables.
with tf.name_scope("MovingAverages"):
variable_averages = tf.train.ExponentialMovingAverage(0.9)
var_averages_op = variable_averages.apply(tf.trainable_variables())
