def tower_loss(scope, images, labels, phase_train_placeholder, args):
logits, net_points = inference(images, phase_train=phase_train_placeholder, weight_decay=args.weight_decay)
embeddings = tf.nn.l2_normalize(logits, 1, 1e-10, name='embeddings')
# Norm for the prelogits
eps = 1e-4
prelogits_norm = tf.reduce_mean(tf.norm(tf.abs(logits) + eps, ord=1.0, axis=1))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, prelogits_norm * 5e-5)
inference_loss, logit = cos_loss(logits, labels, args.num_output)
# calculate accuracy
pred = tf.nn.softmax(logit)
correct_prediction = tf.cast(tf.equal(tf.argmax(pred, 1), tf.cast(labels, tf.int64)), tf.float32)
accuracy_op = tf.reduce_mean(correct_prediction)
tf.add_to_collection('losses', inference_loss)
# total losses
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
losses = tf.get_collection('losses', scope)
total_loss = tf.add_n(losses + regularization_losses, name='total_loss')
# tf.summary.scalar('regularization_losses', regularization_losses)
# tf.summary.scalar('inference_loss', inference_loss)
# tf.summary.scalar('total_loss', total_loss)
with tf.device('/cpu:0'):
for l in losses + [total_loss]:
loss_name = re.sub('tower_[0-9]*/', '', l.op.name)
tf.summary.scalar(loss_name, l)
return total_loss, embeddings, accuracy_op
info = '{}:{}\n'.format(key,
net_points[key].get_shape().as_list())
hd.write(info)
hd.close()
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# Norm for the prelogits
eps = 1e-4
prelogits_norm = tf.reduce_mean(
tf.norm(tf.abs(prelogits) + eps, ord=args.prelogits_norm_p,
axis=1))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_norm * args.prelogits_norm_loss_factor)
inference_loss, logit = cos_loss(prelogits, labels, args.num_output)
# inference_loss, logit = combine_loss(prelogits, labels, args.num_output)
tf.add_to_collection('losses', inference_loss)
# total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([inference_loss] + regularization_losses,
name='total_loss')
# define the learning rate schedule
learning_rate = tf.train.piecewise_constant(
epoch,
boundaries=args.lr_schedule,
values=[0.1, 0.01, 0.001, 0.0001, 0.00001],