Skip to content
GitLab
Menu
Projects
Groups
Snippets
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
Menu
Open sidebar
Jonathan Juhl
SortEM
Commits
8b361ec5
Commit
8b361ec5
authored
Apr 08, 2021
by
Jonathan Juhl
Browse files
add utils
parent
e5649037
Changes
1
Hide whitespace changes
Inline
Side-by-side
utils_sortem.py
View file @
8b361ec5
...
...
@@ -18,20 +18,20 @@ def loss_gen(D_logits_,predict_z,z,predict_angels,angels):
g_loss
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
D_logits_
,
tf
.
ones_like
(
D_logits_
)))
+
loss_latent
(
predict_z
,
z
)
+
loss_angels
(
predict_angels
,
angels
)
return
g_loss
def
loss_disc
(
d_h1_r
,
d_h1_f
,
d_h2_r
,
d_h2_f
,
d_h3_r
,
d_h3_f
,
d_h4_r
,
d_h4_
,
D_logits
,
D_logits_fake
,
predict_z
,
z
,
predict_angels
,
angels
):
def
loss_disc
(
d_h1_r
,
d_h1_f
,
d_h2_r
,
d_h2_f
,
d_h3_r
,
d_h3_f
,
d_h4_r
,
d_h4_f
,
D_logits
,
D_logits_fake
,
predict_z
,
z
,
predict_angels
,
angels
):
z
=
tf
.
cast
(
z
,
tf
.
float32
)
d_h1_loss
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h1_r
,
tf
.
ones_like
(
d_h1_r
)))
\
+
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h1_f
,
tf
.
zeros_like
(
d_h1_f
)))
d_h2_loss
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h2_r
,
tf
.
ones_like
(
d_h2_r
)))
\
+
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h2_f
,
tf
.
zeros_like
(
d_h2_f
)))
d_h3_loss
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h3_r
,
tf
.
ones_like
(
d_h3_r
)))
\
+
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h3_f
,
tf
.
zeros_like
(
d_h3_f
)))
d_h4_loss
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h4_r
,
tf
.
ones_like
(
d_h4_r
)))
\
+
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
d_h4_f
,
tf
.
zeros_like
(
d_h4_f
)))
d_h1_loss
=
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h1_r
,
tf
.
ones_like
(
d_h1_r
)))
\
+
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h1_f
,
tf
.
zeros_like
(
d_h1_f
)))
d_h2_loss
=
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h2_r
,
tf
.
ones_like
(
d_h2_r
)))
\
+
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h2_f
,
tf
.
zeros_like
(
d_h2_f
)))
d_h3_loss
=
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h3_r
,
tf
.
ones_like
(
d_h3_r
)))
\
+
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h3_f
,
tf
.
zeros_like
(
d_h3_f
)))
d_h4_loss
=
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h4_r
,
tf
.
ones_like
(
d_h4_r
)))
\
+
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
d_h4_f
,
tf
.
zeros_like
(
d_h4_f
)))
d_loss_real
=
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
D_logits
,
tf
.
ones_like
(
D_logits
)))
d_loss_fake
=
tf
.
reduce_mean
(
sigmoid_cross_entropy_with_logits
(
D_logits_fake
,
tf
.
zeros_like
(
D_logits_fake
)))
d_loss_real
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
D_logits
,
tf
.
ones_like
(
D_logits
)))
d_loss_fake
=
tf
.
reduce_mean
(
tf
.
nn
.
sigmoid_cross_entropy_with_logits
(
D_logits_fake
,
tf
.
zeros_like
(
D_logits_fake
)))
d_loss
=
d_h1_loss
+
d_h2_loss
+
d_h3_loss
+
d_h4_loss
+
d_loss_real
+
d_loss_fake
+
loss_latent
(
predict_z
,
z
)
+
loss_angels
(
predict_angels
,
angels
)
return
d_loss
...
...
@@ -174,10 +174,11 @@ class Instance_norm(tf.keras.layers.Layer):
def
call
(
self
,
x
):
mean
,
variance
=
tf
.
nn
.
moments
(
x
,
axes
=
[
1
,
2
]
,
keep_dims
=
True
)
mean
,
variance
=
tf
.
nn
.
moments
(
x
,
axes
=
[
1
,
2
])
epsilon
=
1e-5
inv
=
1
/
tf
.
sqrt
(
variance
+
epsilon
)
normalized
=
(
input
-
mean
)
*
inv
normalized
=
tf
.
transpose
((
tf
.
transpose
(
x
,
perm
=
[
1
,
2
,
0
,
3
])
-
mean
)
*
inv
,
perm
=
[
2
,
0
,
1
,
3
])
if
self
.
return_mean
:
return
self
.
scale
*
normalized
+
self
.
offset
,
mean
,
variance
else
:
...
...
@@ -191,29 +192,30 @@ class Spectral_norm(tf.keras.layers.Layer):
self
.
strides
=
strides
self
.
kernels
=
kernels
def
build
(
self
,
input_shape
):
self
.
w
=
self
.
add_weight
(
"
offset
"
,
self
.
w
=
self
.
add_weight
(
"
kernel
"
,
shape
=
[
self
.
kernels
,
self
.
kernels
,
input_shape
[
-
1
],
self
.
channels
])
self
.
u
=
self
.
add_weight
(
"offset"
,
shape
=
[
tf
.
math
.
reduce_prod
(
input_shape
)
*
self
.
channels
])
self
.
bias
=
self
.
add_weight
(
"offset"
,
shape
=
[
1
,
input_shape
[
-
1
]])
self
.
u
=
self
.
add_weight
(
"spec_kernel"
,
shape
=
[
1
,
self
.
channels
],
initializer
=
tf
.
keras
.
initializers
.
TruncatedNormal
(),
trainable
=
False
)
self
.
bias
=
self
.
add_weight
(
"offset"
,
shape
=
[
1
,
self
.
channels
])
self
.
inp_shape
=
input_shape
[
-
1
]
def
call
(
self
,
x
):
w_shape
=
tf
.
shape
(
x
)
x
=
tf
.
reshape
(
x
,
[
-
1
,
w_shape
[
-
1
]])
w
=
tf
.
reshape
(
self
.
w
,
[
-
1
,
self
.
channels
])
v_
=
tf
.
matmul
(
self
.
u
,
w
,
transpose_b
=
True
)
v_hat
=
tf
.
math
.
l2_normalize
(
v_
)
u_
=
tf
.
matmul
(
v_hat
,
w
)
u_hat
=
tf
.
math
.
l2_normalize
(
u_
)
v_
=
tf
.
matmul
(
self
.
u
,
tf
.
reshape
(
self
.
w
,[
-
1
]),
transpose_b
=
True
)
v_hat
=
tf
.
math
.
l2_norm
(
v_
)
u_
=
tf
.
matmul
(
v_hat
,
w
)
u_hat
=
tf
.
math
.
l2_norm
(
u_
)
print
(
v_hat
,
u_hat
);
exit
()
sigma
=
tf
.
matmul
(
tf
.
matmul
(
v_hat
,
w
),
tf
.
transpose
(
u_hat
))
w_norm
=
w
/
sigma
w_norm
=
tf
.
reshape
(
w_norm
,
w_shape
)
conv
=
tf
.
nn
.
conv2d
(
input_
,
w_norm
,
strides
=
[
1
,
self
.
strides
,
self
.
strides
,
1
],
padding
=
'SAME'
)
+
self
.
bias
w_norm
=
tf
.
reshape
(
w_norm
,
[
self
.
kernels
,
self
.
kernels
,
self
.
inp_shape
,
self
.
channels
]
)
conv
=
tf
.
nn
.
conv2d
(
x
,
w_norm
,
strides
=
[
1
,
self
.
strides
,
self
.
strides
,
1
],
padding
=
'SAME'
)
+
self
.
bias
return
conv
...
...
Write
Preview
Supports
Markdown
0%
Try again
or
attach a new file
.
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment