diff --git a/bob/learn/tensorflow/loss/StyleLoss.py b/bob/learn/tensorflow/loss/StyleLoss.py index d03341e98d0080a87016990f9631f22306eca2dd..36b53b66dcd70d94693c856129f7b23a303d68ab 100644 --- a/bob/learn/tensorflow/loss/StyleLoss.py +++ b/bob/learn/tensorflow/loss/StyleLoss.py @@ -4,8 +4,8 @@ import logging import tensorflow as tf -logger = logging.getLogger("bob.learn.tensorflow") import functools +logger = logging.getLogger("bob.learn.tensorflow") def content_loss(noises, content_features): @@ -24,7 +24,7 @@ def content_loss(noises, content_features): ---------- noises: :any:`list` - A list of tf.Tensor containing all the noises convolved + A list of tf.Tensor containing all the noises convolved content_features: :any:`list` A list of numpy.array containing all the content_features convolved @@ -36,7 +36,7 @@ def content_loss(noises, content_features): content_losses.append((2 * tf.nn.l2_loss(n - c) / c.size)) return functools.reduce(tf.add, content_losses) - + def linear_gram_style_loss(noises, gram_style_features): """ @@ -89,7 +89,7 @@ def denoising_loss(noise): noise_y_size = _tensor_size(noise[:,1:,:,:]) noise_x_size = _tensor_size(noise[:,:,1:,:]) - denoise_loss = 2 * ( (tf.nn.l2_loss(noise[:,1:,:,:] - noise[:,:shape[1]-1,:,:]) / noise_y_size) + + denoise_loss = 2 * ( (tf.nn.l2_loss(noise[:,1:,:,:] - noise[:,:shape[1]-1,:,:]) / noise_y_size) + (tf.nn.l2_loss(noise[:,:,1:,:] - noise[:,:,:shape[2]-1,:]) / noise_x_size)) return denoise_loss diff --git a/bob/learn/tensorflow/loss/TripletLoss.py b/bob/learn/tensorflow/loss/TripletLoss.py index a22f8d50dc4b411117d9828ac7ce699853fc2be2..2d487cc5f89f41b44f36767a6cc05fc8a5b3d5e1 100644 --- a/bob/learn/tensorflow/loss/TripletLoss.py +++ b/bob/learn/tensorflow/loss/TripletLoss.py @@ -57,24 +57,19 @@ def triplet_loss(anchor_embedding, with tf.name_scope("TripletLoss"): # Between between_class_loss = tf.reduce_mean(d_negative) - tf.summary.scalar('between_class', between_class_loss) + tf.summary.scalar('loss_between_class', between_class_loss) tf.add_to_collection(tf.GraphKeys.LOSSES, between_class_loss) # Within within_class_loss = tf.reduce_mean(d_positive) - tf.summary.scalar('within_class', within_class_loss) + tf.summary.scalar('loss_within_class', within_class_loss) tf.add_to_collection(tf.GraphKeys.LOSSES, within_class_loss) # Total loss loss = tf.reduce_mean( tf.maximum(basic_loss, 0.0), 0, name="total_loss") tf.add_to_collection(tf.GraphKeys.LOSSES, loss) - tf.summary.scalar('loss_raw', loss) - - # Appending the regularization loss - #regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) - #loss = tf.add_n([loss] + regularization_losses, name="total_loss") - #tf.summary.scalar('loss', loss) + tf.summary.scalar('loss_triplet', loss) return loss