Added summaries

parent 3af74bd5
......@@ -51,6 +51,8 @@ def mean_cross_entropy_center_loss(logits, prelogits, labels, n_classes, alpha=0
with tf.variable_scope('cross_entropy_loss'):
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels), name=tf.GraphKeys.LOSSES)
tf.summary.scalar('cross_entropy_loss', loss)
# Appending center loss
with tf.variable_scope('center_loss'):
......@@ -65,11 +67,13 @@ def mean_cross_entropy_center_loss(logits, prelogits, labels, n_classes, alpha=0
centers = tf.scatter_sub(centers, labels, diff)
center_loss = tf.reduce_mean(tf.square(prelogits - centers_batch))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, center_loss * factor)
tf.summary.scalar('center_loss', center_loss)
# Adding the regularizers in the loss
with tf.variable_scope('total_loss'):
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([loss] + regularization_losses, name=tf.GraphKeys.LOSSES)
tf.summary.scalar('total_loss', total_loss)
loss = dict()
loss['loss'] = total_loss
......
Markdown is supported
0%
or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment