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体验新版 GitCode,发现更多精彩内容 >>
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fb0d80d5
编写于
11月 10, 2016
作者:
W
wangyang59
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add sample noise to demo/gan
上级
0df8af99
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
32 addition
and
17 deletion
+32
-17
demo/gan/gan_conf_image.py
demo/gan/gan_conf_image.py
+6
-1
demo/gan/gan_trainer_image.py
demo/gan/gan_trainer_image.py
+26
-16
未找到文件。
demo/gan/gan_conf_image.py
浏览文件 @
fb0d80d5
...
...
@@ -232,8 +232,13 @@ if is_discriminator_training:
sample
=
data_layer
(
name
=
"sample"
,
size
=
sample_dim
*
sample_dim
*
c_dim
)
if
is_generator_training
or
is_discriminator_training
:
sample_noise
=
data_layer
(
name
=
"sample_noise"
,
size
=
sample_dim
*
sample_dim
*
c_dim
)
label
=
data_layer
(
name
=
"label"
,
size
=
1
)
prob
=
discriminator
(
sample
)
prob
=
discriminator
(
addto_layer
([
sample
,
sample_noise
],
act
=
LinearActivation
(),
name
=
"add"
,
bias_attr
=
False
))
cost
=
cross_entropy
(
input
=
prob
,
label
=
label
)
classification_error_evaluator
(
input
=
prob
,
label
=
label
,
name
=
mode
+
'_error'
)
outputs
(
cost
)
...
...
demo/gan/gan_trainer_image.py
浏览文件 @
fb0d80d5
...
...
@@ -115,9 +115,13 @@ def get_real_samples(batch_size, data_np):
def
get_noise
(
batch_size
,
noise_dim
):
return
numpy
.
random
.
normal
(
size
=
(
batch_size
,
noise_dim
)).
astype
(
'float32'
)
def
get_sample_noise
(
batch_size
):
return
numpy
.
random
.
normal
(
size
=
(
batch_size
,
28
*
28
),
scale
=
0.1
).
astype
(
'float32'
)
def
get_fake_samples
(
generator_machine
,
batch_size
,
noise
):
gen_inputs
=
prepare_generator_data_batch
(
batch_size
,
noise
)
gen_inputs
.
resize
(
1
)
gen_inputs
=
api
.
Arguments
.
createArguments
(
1
)
gen_inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
noise
)
)
gen_outputs
=
api
.
Arguments
.
createArguments
(
0
)
generator_machine
.
forward
(
gen_inputs
,
gen_outputs
,
api
.
PASS_TEST
)
fake_samples
=
gen_outputs
.
getSlotValue
(
0
).
copyToNumpyMat
()
...
...
@@ -129,29 +133,33 @@ def get_training_loss(training_machine, inputs):
loss
=
outputs
.
getSlotValue
(
0
).
copyToNumpyMat
()
return
numpy
.
mean
(
loss
)
def
prepare_discriminator_data_batch_pos
(
batch_size
,
data_np
):
def
prepare_discriminator_data_batch_pos
(
batch_size
,
data_np
,
sample_noise
):
real_samples
=
get_real_samples
(
batch_size
,
data_np
)
labels
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
=
api
.
Arguments
.
createArguments
(
3
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
real_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNumpy
(
labels
))
inputs
.
setSlotValue
(
1
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
sample_noise
))
inputs
.
setSlotIds
(
2
,
api
.
IVector
.
createGpuVectorFromNumpy
(
labels
))
return
inputs
def
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise
):
def
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise
,
sample_noise
):
fake_samples
=
get_fake_samples
(
generator_machine
,
batch_size
,
noise
)
#print fake_samples.shape
labels
=
numpy
.
zeros
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
=
api
.
Arguments
.
createArguments
(
3
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
fake_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNumpy
(
labels
))
inputs
.
setSlotValue
(
1
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
sample_noise
))
inputs
.
setSlotIds
(
2
,
api
.
IVector
.
createGpuVectorFromNumpy
(
labels
))
return
inputs
def
prepare_generator_data_batch
(
batch_size
,
noise
):
def
prepare_generator_data_batch
(
batch_size
,
noise
,
sample_noise
):
label
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
#label = numpy.zeros(batch_size, dtype='int32')
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
=
api
.
Arguments
.
createArguments
(
3
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
noise
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNumpy
(
label
))
inputs
.
setSlotValue
(
1
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
sample_noise
))
inputs
.
setSlotIds
(
2
,
api
.
IVector
.
createGpuVectorFromNumpy
(
label
))
return
inputs
...
...
@@ -216,25 +224,27 @@ def main():
# generator_machine, batch_size, noise_dim, sample_dim)
# dis_loss = get_training_loss(dis_training_machine, data_batch_dis)
noise
=
get_noise
(
batch_size
,
noise_dim
)
sample_noise
=
get_sample_noise
(
batch_size
)
data_batch_dis_pos
=
prepare_discriminator_data_batch_pos
(
batch_size
,
data_np
)
batch_size
,
data_np
,
sample_noise
)
dis_loss_pos
=
get_training_loss
(
dis_training_machine
,
data_batch_dis_pos
)
sample_noise
=
get_sample_noise
(
batch_size
)
data_batch_dis_neg
=
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise
)
generator_machine
,
batch_size
,
noise
,
sample_noise
)
dis_loss_neg
=
get_training_loss
(
dis_training_machine
,
data_batch_dis_neg
)
dis_loss
=
(
dis_loss_pos
+
dis_loss_neg
)
/
2.0
data_batch_gen
=
prepare_generator_data_batch
(
batch_size
,
noise
)
batch_size
,
noise
,
sample_noise
)
gen_loss
=
get_training_loss
(
gen_training_machine
,
data_batch_gen
)
if
i
%
100
==
0
:
print
"d_pos_loss is %s d_neg_loss is %s"
%
(
dis_loss_pos
,
dis_loss_neg
)
print
"d_loss is %s g_loss is %s"
%
(
dis_loss
,
gen_loss
)
if
(
not
(
curr_train
==
"dis"
and
curr_strike
==
MAX_strike
))
and
((
curr_train
==
"gen"
and
curr_strike
==
MAX_strike
)
or
dis_loss
>
gen_loss
):
if
(
not
(
curr_train
==
"dis"
and
curr_strike
==
MAX_strike
))
and
((
curr_train
==
"gen"
and
curr_strike
==
MAX_strike
)
or
dis_loss
_neg
>
gen_loss
):
if
curr_train
==
"dis"
:
curr_strike
+=
1
else
:
...
...
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