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bob
bob.learn.tensorflow
Commits
e155ed50
Commit
e155ed50
authored
Feb 07, 2020
by
Amir MOHAMMADI
Browse files
improve logging in losses
parent
036a308f
Changes
2
Hide whitespace changes
Inline
Side-by-side
bob/learn/tensorflow/loss/StyleLoss.py
View file @
e155ed50
...
...
@@ -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
...
...
bob/learn/tensorflow/loss/TripletLoss.py
View file @
e155ed50
...
...
@@ -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
...
...
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