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David Josef Schmidt
HAP_Tutorial_2018
Commits
6fb856b5
Commit
6fb856b5
authored
7 years ago
by
JGlombitza
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add new explanations
parent
c13ebed6
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AirShower_WGAN/ShowerGAN.py
+14
-9
14 additions, 9 deletions
AirShower_WGAN/ShowerGAN.py
with
14 additions
and
9 deletions
AirShower_WGAN/ShowerGAN.py
+
14
−
9
View file @
6fb856b5
...
@@ -14,6 +14,7 @@ import tensorflow as tf
...
@@ -14,6 +14,7 @@ import tensorflow as tf
KTF
.
set_session
(
utils
.
get_session
())
# Allows 2 jobs per GPU, Please do not change this during the tutorial
KTF
.
set_session
(
utils
.
get_session
())
# Allows 2 jobs per GPU, Please do not change this during the tutorial
log_dir
=
"
.
"
log_dir
=
"
.
"
# basic trainings parameter
EPOCHS
=
10
EPOCHS
=
10
GRADIENT_PENALTY_WEIGHT
=
10
GRADIENT_PENALTY_WEIGHT
=
10
BATCH_SIZE
=
256
BATCH_SIZE
=
256
...
@@ -45,6 +46,7 @@ def build_generator(latent_size):
...
@@ -45,6 +46,7 @@ def build_generator(latent_size):
generator
.
add
(
Conv2D
(
1
,
(
3
,
3
),
padding
=
'
same
'
,
kernel_initializer
=
'
he_normal
'
,
activation
=
'
relu
'
))
generator
.
add
(
Conv2D
(
1
,
(
3
,
3
),
padding
=
'
same
'
,
kernel_initializer
=
'
he_normal
'
,
activation
=
'
relu
'
))
return
generator
return
generator
# build critic
# build critic
# Feel free to modify the critic model
# Feel free to modify the critic model
def
build_critic
():
def
build_critic
():
...
@@ -70,20 +72,19 @@ critic = build_critic()
...
@@ -70,20 +72,19 @@ critic = build_critic()
print
(
critic
.
summary
())
print
(
critic
.
summary
())
# make trainings model for generator
# make trainings model for generator
utils
.
make_trainable
(
critic
,
False
)
utils
.
make_trainable
(
critic
,
False
)
# freeze the critic during the generator training
utils
.
make_trainable
(
generator
,
True
)
utils
.
make_trainable
(
generator
,
True
)
# unfreeze the generator during the generator training
generator_training
=
utils
.
build_generator_graph
(
generator
,
critic
,
latent_size
)
generator_training
=
utils
.
build_generator_graph
(
generator
,
critic
,
latent_size
)
generator_training
.
compile
(
optimizer
=
Adam
(
0.0001
,
beta_1
=
0.5
,
beta_2
=
0.9
,
decay
=
0.0
),
loss
=
[
utils
.
wasserstein_loss
])
generator_training
.
compile
(
optimizer
=
Adam
(
0.0001
,
beta_1
=
0.5
,
beta_2
=
0.9
,
decay
=
0.0
),
loss
=
[
utils
.
wasserstein_loss
])
plot_model
(
generator_training
,
to_file
=
log_dir
+
'
/generator_training.png
'
,
show_shapes
=
True
)
plot_model
(
generator_training
,
to_file
=
log_dir
+
'
/generator_training.png
'
,
show_shapes
=
True
)
# make trainings model for critic
# make trainings model for critic
utils
.
make_trainable
(
critic
,
True
)
utils
.
make_trainable
(
critic
,
True
)
# unfreeze the critic during the critic training
utils
.
make_trainable
(
generator
,
False
)
utils
.
make_trainable
(
generator
,
False
)
# freeze the generator during the critic training
critic_training
,
averaged_batch
=
utils
.
build_critic_graph
(
generator
,
critic
,
latent_size
,
batch_size
=
BATCH_SIZE
)
critic_training
,
averaged_batch
=
utils
.
build_critic_graph
(
generator
,
critic
,
latent_size
,
batch_size
=
BATCH_SIZE
)
gradient_penalty
=
partial
(
utils
.
gradient_penalty_loss
,
averaged_batch
=
averaged_batch
,
penalty_weight
=
GRADIENT_PENALTY_WEIGHT
)
gradient_penalty
=
partial
(
utils
.
gradient_penalty_loss
,
averaged_batch
=
averaged_batch
,
penalty_weight
=
GRADIENT_PENALTY_WEIGHT
)
# construct the gradient penalty
gradient_penalty
.
__name__
=
'
gradient_penalty
'
gradient_penalty
.
__name__
=
'
gradient_penalty
'
critic_training
.
compile
(
optimizer
=
Adam
(
0.0001
,
beta_1
=
0.5
,
beta_2
=
0.9
,
decay
=
0.0
),
loss
=
[
utils
.
wasserstein_loss
,
utils
.
wasserstein_loss
,
gradient_penalty
])
critic_training
.
compile
(
optimizer
=
Adam
(
0.0001
,
beta_1
=
0.5
,
beta_2
=
0.9
,
decay
=
0.0
),
loss
=
[
utils
.
wasserstein_loss
,
utils
.
wasserstein_loss
,
gradient_penalty
])
plot_model
(
critic_training
,
to_file
=
log_dir
+
'
/critic_training.png
'
,
show_shapes
=
True
)
plot_model
(
critic_training
,
to_file
=
log_dir
+
'
/critic_training.png
'
,
show_shapes
=
True
)
...
@@ -104,15 +105,19 @@ for epoch in range(EPOCHS):
...
@@ -104,15 +105,19 @@ for epoch in range(EPOCHS):
generated_map
=
generator
.
predict_on_batch
(
np
.
random
.
randn
(
BATCH_SIZE
,
latent_size
))
generated_map
=
generator
.
predict_on_batch
(
np
.
random
.
randn
(
BATCH_SIZE
,
latent_size
))
utils
.
plot_multiple_signalmaps
(
generated_map
[:,:,:,
0
],
log_dir
=
log_dir
,
title
=
'
Generated Footprints Epoch:
'
,
epoch
=
str
(
epoch
))
utils
.
plot_multiple_signalmaps
(
generated_map
[:,:,:,
0
],
log_dir
=
log_dir
,
title
=
'
Generated Footprints Epoch:
'
,
epoch
=
str
(
epoch
))
for
iteration
in
range
(
iterations_per_epoch
):
for
iteration
in
range
(
iterations_per_epoch
):
for
j
in
range
(
NCR
):
for
j
in
range
(
NCR
):
noise_batch
=
np
.
random
.
randn
(
BATCH_SIZE
,
latent_size
)
# generate noise batch for generator
noise_batch
=
np
.
random
.
randn
(
BATCH_SIZE
,
latent_size
)
# generate noise batch for generator
shower_batch
=
shower_maps
[
BATCH_SIZE
*
(
j
+
iteration
):
BATCH_SIZE
*
(
j
++
iteration
+
1
)]
shower_batch
=
shower_maps
[
BATCH_SIZE
*
(
j
+
iteration
):
BATCH_SIZE
*
(
j
++
iteration
+
1
)]
# take batch of shower maps
critic_loss
.
append
(
critic_training
.
train_on_batch
([
noise_batch
,
shower_batch
],
[
negative_y
,
positive_y
,
dummy
]))
critic_loss
.
append
(
critic_training
.
train_on_batch
([
noise_batch
,
shower_batch
],
[
negative_y
,
positive_y
,
dummy
]))
# train the critic
print
"
critic loss:
"
,
critic_loss
[
-
1
]
print
"
critic loss:
"
,
critic_loss
[
-
1
]
noise_batch
=
np
.
random
.
randn
(
BATCH_SIZE
,
latent_size
)
# generate noise batch for generator
noise_batch
=
np
.
random
.
randn
(
BATCH_SIZE
,
latent_size
)
# generate noise batch for generator
generator_loss
.
append
(
generator_training
.
train_on_batch
([
noise_batch
],
[
positive_y
]))
generator_loss
.
append
(
generator_training
.
train_on_batch
([
noise_batch
],
[
positive_y
]))
# train the generator
print
"
generator loss:
"
,
generator_loss
[
-
1
]
print
"
generator loss:
"
,
generator_loss
[
-
1
]
# plot critic and generator loss
utils
.
plot_loss
(
critic_loss
,
name
=
"
critic
"
,
log_dir
=
log_dir
)
utils
.
plot_loss
(
critic_loss
,
name
=
"
critic
"
,
log_dir
=
log_dir
)
utils
.
plot_loss
(
generator_loss
,
name
=
"
generator
"
,
log_dir
=
log_dir
)
utils
.
plot_loss
(
generator_loss
,
name
=
"
generator
"
,
log_dir
=
log_dir
)
...
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