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93af332e
编写于
11月 17, 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
unified cifar/mnist/uniform gan training in demo
上级
4878f078
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
80 addition
and
76 deletion
+80
-76
demo/gan/.gitignore
demo/gan/.gitignore
+1
-1
demo/gan/data/get_mnist_data.sh
demo/gan/data/get_mnist_data.sh
+3
-3
demo/gan/gan_conf.py
demo/gan/gan_conf.py
+2
-5
demo/gan/gan_conf_image.py
demo/gan/gan_conf_image.py
+1
-7
demo/gan/gan_trainer.py
demo/gan/gan_trainer.py
+9
-8
demo/gan/gan_trainer_image.py
demo/gan/gan_trainer_image.py
+64
-52
未找到文件。
demo/gan/.gitignore
浏览文件 @
93af332e
...
...
@@ -4,5 +4,5 @@ output/
.project
*.log
*.pyc
data/
raw
_data/
data/
mnist
_data/
data/cifar-10-batches-py/
demo/gan/data/get_mnist_data.sh
浏览文件 @
93af332e
...
...
@@ -2,9 +2,9 @@
# This scripts downloads the mnist data and unzips it.
set
-e
DIR
=
"
$(
cd
"
$(
dirname
"
$0
"
)
"
;
pwd
-P
)
"
rm
-rf
"
$DIR
/
raw
_data"
mkdir
"
$DIR
/
raw
_data"
cd
"
$DIR
/
raw
_data"
rm
-rf
"
$DIR
/
mnist
_data"
mkdir
"
$DIR
/
mnist
_data"
cd
"
$DIR
/
mnist
_data"
echo
"Downloading..."
...
...
demo/gan/gan_conf.py
浏览文件 @
93af332e
...
...
@@ -32,7 +32,7 @@ sample_dim = 2
settings
(
batch_size
=
128
,
learning_rate
=
1e-4
,
learning_method
=
AdamOptimizer
()
learning_method
=
AdamOptimizer
(
beta1
=
0.7
)
)
def
discriminator
(
sample
):
...
...
@@ -47,16 +47,15 @@ def discriminator(sample):
bias_attr
=
ParamAttr
(
is_static
=
is_generator_training
,
initial_mean
=
1.0
,
initial_std
=
0
)
hidden
=
fc_layer
(
input
=
sample
,
name
=
"dis_hidden"
,
size
=
hidden_dim
,
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
act
=
ReluActivation
())
#act=LinearActivation())
hidden2
=
fc_layer
(
input
=
hidden
,
name
=
"dis_hidden2"
,
size
=
hidden_dim
,
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
#act=ReluActivation())
act
=
LinearActivation
())
hidden_bn
=
batch_norm_layer
(
hidden2
,
...
...
@@ -88,12 +87,10 @@ def generator(noise):
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
act
=
ReluActivation
())
#act=LinearActivation())
hidden2
=
fc_layer
(
input
=
hidden
,
name
=
"gen_hidden2"
,
size
=
hidden_dim
,
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
#act=ReluActivation())
act
=
LinearActivation
())
hidden_bn
=
batch_norm_layer
(
hidden2
,
...
...
demo/gan/gan_conf_image.py
浏览文件 @
93af332e
...
...
@@ -113,7 +113,6 @@ def generator(noise):
size
=
s8
*
s8
*
gf_dim
*
4
,
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
#act=ReluActivation())
act
=
LinearActivation
())
h1_bn
=
batch_norm_layer
(
h1
,
...
...
@@ -235,13 +234,8 @@ 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
(
addto_layer
([
sample
,
sample_noise
],
act
=
LinearActivation
(),
name
=
"add"
,
bias_attr
=
False
))
prob
=
discriminator
(
sample
)
cost
=
cross_entropy
(
input
=
prob
,
label
=
label
)
classification_error_evaluator
(
input
=
prob
,
label
=
label
,
name
=
mode
+
'_error'
)
outputs
(
cost
)
...
...
demo/gan/gan_trainer.py
浏览文件 @
93af332e
...
...
@@ -71,7 +71,7 @@ def print_parameters(src):
print
"value is %s
\n
"
%
p
.
getBuf
(
api
.
PARAMETER_VALUE
).
copyToNumpyArray
()
def
get_real_samples
(
batch_size
,
sample_dim
):
return
numpy
.
random
.
rand
(
batch_size
,
sample_dim
).
astype
(
'float32'
)
*
10.0
-
10.0
return
numpy
.
random
.
rand
(
batch_size
,
sample_dim
).
astype
(
'float32'
)
# return numpy.random.normal(loc=100.0, scale=100.0, size=(batch_size, sample_dim)).astype('float32')
def
get_fake_samples
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
):
...
...
@@ -106,7 +106,7 @@ def prepare_discriminator_data_batch_pos(batch_size, noise_dim, sample_dim):
labels
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
real_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNumy
(
labels
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNum
p
y
(
labels
))
return
inputs
def
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
):
...
...
@@ -114,7 +114,7 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise_di
labels
=
numpy
.
zeros
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
fake_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNumy
(
labels
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNum
p
y
(
labels
))
return
inputs
def
prepare_generator_data_batch
(
batch_size
,
dim
):
...
...
@@ -122,7 +122,7 @@ def prepare_generator_data_batch(batch_size, dim):
label
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
noise
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNumy
(
label
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createGpuVectorFromNum
p
y
(
label
))
return
inputs
...
...
@@ -140,7 +140,8 @@ def get_layer_size(model_conf, layer_name):
def
main
():
api
.
initPaddle
(
'--use_gpu=1'
,
'--dot_period=100'
,
'--log_period=10000'
)
api
.
initPaddle
(
'--use_gpu=1'
,
'--dot_period=10'
,
'--log_period=100'
,
'--gpu_id=2'
)
gen_conf
=
parse_config
(
"gan_conf.py"
,
"mode=generator_training"
)
dis_conf
=
parse_config
(
"gan_conf.py"
,
"mode=discriminator_training"
)
generator_conf
=
parse_config
(
"gan_conf.py"
,
"mode=generator"
)
...
...
@@ -175,10 +176,10 @@ def main():
curr_strike
=
0
MAX_strike
=
5
for
train_pass
in
xrange
(
10
):
for
train_pass
in
xrange
(
10
0
):
dis_trainer
.
startTrainPass
()
gen_trainer
.
startTrainPass
()
for
i
in
xrange
(
1000
00
):
for
i
in
xrange
(
1000
):
# data_batch_dis = prepare_discriminator_data_batch(
# generator_machine, batch_size, noise_dim, sample_dim)
# dis_loss = get_training_loss(dis_training_machine, data_batch_dis)
...
...
@@ -199,7 +200,7 @@ def main():
if
i
%
1000
==
0
:
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
>
0.690
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
>
gen_loss
):
if
curr_train
==
"dis"
:
curr_strike
+=
1
else
:
...
...
demo/gan/gan_trainer_image.py
浏览文件 @
93af332e
...
...
@@ -13,16 +13,29 @@
# limitations under the License.
import
argparse
import
itertools
import
random
import
numpy
import
cPickle
import
sys
,
os
,
gc
import
sys
,
os
from
PIL
import
Image
from
paddle.trainer.config_parser
import
parse_config
from
paddle.trainer.config_parser
import
logger
import
py_paddle.swig_paddle
as
api
import
matplotlib.pyplot
as
plt
def
plot2DScatter
(
data
,
outputfile
):
x
=
data
[:,
0
]
y
=
data
[:,
1
]
print
"The mean vector is %s"
%
numpy
.
mean
(
data
,
0
)
print
"The std vector is %s"
%
numpy
.
std
(
data
,
0
)
heatmap
,
xedges
,
yedges
=
numpy
.
histogram2d
(
x
,
y
,
bins
=
50
)
extent
=
[
xedges
[
0
],
xedges
[
-
1
],
yedges
[
0
],
yedges
[
-
1
]]
plt
.
clf
()
plt
.
scatter
(
x
,
y
)
plt
.
savefig
(
outputfile
,
bbox_inches
=
'tight'
)
def
CHECK_EQ
(
a
,
b
):
assert
a
==
b
,
"a=%s, b=%s"
%
(
a
,
b
)
...
...
@@ -60,7 +73,6 @@ def load_mnist_data(imageFile):
# Define number of samples for train/test
if
"train"
in
imageFile
:
#n = 60000
n
=
60000
else
:
n
=
10000
...
...
@@ -89,6 +101,11 @@ def load_cifar_data(cifar_path):
data
=
data
/
255.0
*
2.0
-
1.0
return
data
# synthesize 2-D uniform data
def
load_uniform_data
():
data
=
numpy
.
random
.
rand
(
1000000
,
2
).
astype
(
'float32'
)
return
data
def
merge
(
images
,
size
):
if
images
.
shape
[
1
]
==
28
*
28
:
h
,
w
,
c
=
28
,
28
,
1
...
...
@@ -98,7 +115,6 @@ def merge(images, size):
for
idx
in
xrange
(
size
[
0
]
*
size
[
1
]):
i
=
idx
%
size
[
1
]
j
=
idx
//
size
[
1
]
#img[j*h:j*h+h, i*w:i*w+w, :] = (images[idx, :].reshape((h, w, c), order="F") + 1.0) / 2.0 * 255.0
img
[
j
*
h
:
j
*
h
+
h
,
i
*
w
:
i
*
w
+
w
,
:]
=
\
((
images
[
idx
,
:].
reshape
((
h
,
w
,
c
),
order
=
"F"
).
transpose
(
1
,
0
,
2
)
+
1.0
)
/
2.0
*
255.0
)
return
img
.
astype
(
'uint8'
)
...
...
@@ -118,13 +134,9 @@ 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
,
sample_dim
):
return
numpy
.
random
.
normal
(
size
=
(
batch_size
,
sample_dim
),
scale
=
0.01
).
astype
(
'float32'
)
def
get_fake_samples
(
generator_machine
,
batch_size
,
noise
):
gen_inputs
=
api
.
Arguments
.
createArguments
(
1
)
gen_inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
create
Gpu
DenseFromNumpy
(
noise
))
gen_inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createDenseFromNumpy
(
noise
))
gen_outputs
=
api
.
Arguments
.
createArguments
(
0
)
generator_machine
.
forward
(
gen_inputs
,
gen_outputs
,
api
.
PASS_TEST
)
fake_samples
=
gen_outputs
.
getSlotValue
(
0
).
copyToNumpyMat
()
...
...
@@ -136,33 +148,27 @@ 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
,
sample_noise
):
def
prepare_discriminator_data_batch_pos
(
batch_size
,
data_np
):
real_samples
=
get_real_samples
(
batch_size
,
data_np
)
labels
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
3
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
real_samples
))
inputs
.
setSlotValue
(
1
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
sample_noise
))
inputs
.
setSlotIds
(
2
,
api
.
IVector
.
createGpuVectorFromNumpy
(
labels
))
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createDenseFromNumpy
(
real_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createVectorFromNumpy
(
labels
))
return
inputs
def
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise
,
sample_noise
):
def
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
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
(
3
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
fake_samples
))
inputs
.
setSlotValue
(
1
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
sample_noise
))
inputs
.
setSlotIds
(
2
,
api
.
IVector
.
createGpuVectorFromNumpy
(
labels
))
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createDenseFromNumpy
(
fake_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createVectorFromNumpy
(
labels
))
return
inputs
def
prepare_generator_data_batch
(
batch_size
,
noise
,
sample_noise
):
def
prepare_generator_data_batch
(
batch_size
,
noise
):
label
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
#label = numpy.zeros(batch_size, dtype='int32')
inputs
=
api
.
Arguments
.
createArguments
(
3
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
noise
))
inputs
.
setSlotValue
(
1
,
api
.
Matrix
.
createGpuDenseFromNumpy
(
sample_noise
))
inputs
.
setSlotIds
(
2
,
api
.
IVector
.
createGpuVectorFromNumpy
(
label
))
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createDenseFromNumpy
(
noise
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createVectorFromNumpy
(
label
))
return
inputs
...
...
@@ -181,7 +187,7 @@ def get_layer_size(model_conf, layer_name):
def
main
():
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"-d"
,
"--dataSource"
,
help
=
"mnist or cifar"
)
parser
.
add_argument
(
"-d"
,
"--dataSource"
,
help
=
"mnist or cifar
or uniform
"
)
parser
.
add_argument
(
"--useGpu"
,
default
=
"1"
,
help
=
"1 means use gpu for training"
)
parser
.
add_argument
(
"--gpuId"
,
default
=
"0"
,
...
...
@@ -189,22 +195,31 @@ def main():
args
=
parser
.
parse_args
()
dataSource
=
args
.
dataSource
useGpu
=
args
.
useGpu
assert
dataSource
in
[
"mnist"
,
"cifar"
]
assert
dataSource
in
[
"mnist"
,
"cifar"
,
"uniform"
]
assert
useGpu
in
[
"0"
,
"1"
]
api
.
initPaddle
(
'--use_gpu='
+
useGpu
,
'--dot_period=10'
,
'--log_period=100'
,
'--gpu_id='
+
args
.
gpuId
)
gen_conf
=
parse_config
(
"gan_conf_image.py"
,
"mode=generator_training,data="
+
dataSource
)
dis_conf
=
parse_config
(
"gan_conf_image.py"
,
"mode=discriminator_training,data="
+
dataSource
)
generator_conf
=
parse_config
(
"gan_conf_image.py"
,
"mode=generator,data="
+
dataSource
)
if
dataSource
==
"uniform"
:
conf
=
"gan_conf.py"
num_iter
=
10000
else
:
conf
=
"gan_conf_image.py"
num_iter
=
1000
gen_conf
=
parse_config
(
conf
,
"mode=generator_training,data="
+
dataSource
)
dis_conf
=
parse_config
(
conf
,
"mode=discriminator_training,data="
+
dataSource
)
generator_conf
=
parse_config
(
conf
,
"mode=generator,data="
+
dataSource
)
batch_size
=
dis_conf
.
opt_config
.
batch_size
noise_dim
=
get_layer_size
(
gen_conf
.
model_config
,
"noise"
)
sample_dim
=
get_layer_size
(
dis_conf
.
model_config
,
"sample"
)
if
dataSource
==
"mnist"
:
data_np
=
load_mnist_data
(
"./data/
raw
_data/train-images-idx3-ubyte"
)
el
se
:
data_np
=
load_mnist_data
(
"./data/
mnist
_data/train-images-idx3-ubyte"
)
el
if
dataSource
==
"cifar"
:
data_np
=
load_cifar_data
(
"./data/cifar-10-batches-py/"
)
else
:
data_np
=
load_uniform_data
()
if
not
os
.
path
.
exists
(
"./%s_samples/"
%
dataSource
):
os
.
makedirs
(
"./%s_samples/"
%
dataSource
)
...
...
@@ -234,48 +249,44 @@ def main():
copy_shared_parameters
(
gen_training_machine
,
dis_training_machine
)
copy_shared_parameters
(
gen_training_machine
,
generator_machine
)
# constrain that either discriminator or generator can not be trained
# consecutively more than MAX_strike times
curr_train
=
"dis"
curr_strike
=
0
MAX_strike
=
10
MAX_strike
=
5
for
train_pass
in
xrange
(
100
):
dis_trainer
.
startTrainPass
()
gen_trainer
.
startTrainPass
()
for
i
in
xrange
(
1000
):
# data_batch_dis = prepare_discriminator_data_batch(
# generator_machine, batch_size, noise_dim, sample_dim)
# dis_loss = get_training_loss(dis_training_machine, data_batch_dis)
for
i
in
xrange
(
num_iter
):
noise
=
get_noise
(
batch_size
,
noise_dim
)
sample_noise
=
get_sample_noise
(
batch_size
,
sample_dim
)
data_batch_dis_pos
=
prepare_discriminator_data_batch_pos
(
batch_size
,
data_np
,
sample_noise
)
batch_size
,
data_np
)
dis_loss_pos
=
get_training_loss
(
dis_training_machine
,
data_batch_dis_pos
)
sample_noise
=
get_sample_noise
(
batch_size
,
sample_dim
)
data_batch_dis_neg
=
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise
,
sample_noise
)
generator_machine
,
batch_size
,
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
,
sample_noise
)
batch_size
,
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_neg
>
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
curr_train
==
"dis"
:
curr_strike
+=
1
else
:
curr_train
=
"dis"
curr_strike
=
1
dis_trainer
.
trainOneDataBatch
(
batch_size
,
data_batch_dis_neg
)
dis_trainer
.
trainOneDataBatch
(
batch_size
,
data_batch_dis_pos
)
# dis_loss = numpy.mean(dis_trainer.getForwardOutput()[0]["value"])
# print "getForwardOutput loss is %s" % dis_loss
dis_trainer
.
trainOneDataBatch
(
batch_size
,
data_batch_dis_pos
)
copy_shared_parameters
(
dis_training_machine
,
gen_training_machine
)
else
:
...
...
@@ -290,10 +301,11 @@ def main():
dis_trainer
.
finishTrainPass
()
gen_trainer
.
finishTrainPass
()
fake_samples
=
get_fake_samples
(
generator_machine
,
batch_size
,
noise
)
saveImages
(
fake_samples
,
"./%s_samples/train_pass%s.png"
%
(
dataSource
,
train_pass
))
if
dataSource
==
"uniform"
:
plot2DScatter
(
fake_samples
,
"./%s_samples/train_pass%s.png"
%
(
dataSource
,
train_pass
))
else
:
saveImages
(
fake_samples
,
"./%s_samples/train_pass%s.png"
%
(
dataSource
,
train_pass
))
dis_trainer
.
finishTrain
()
gen_trainer
.
finishTrain
()
...
...
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