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c159e4dd
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c159e4dd
编写于
11月 03, 2016
作者:
W
wangyang59
浏览文件
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差异文件
added get_mnist_data and demo/gan and updated the gan_conf and gan_trainer python files
上级
bd8613ac
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
182 addition
and
25 deletion
+182
-25
.gitignore
.gitignore
+1
-0
demo/gan/.gitignore
demo/gan/.gitignore
+1
-2
demo/gan/data/get_mnist_data.sh
demo/gan/data/get_mnist_data.sh
+19
-0
demo/gan/gan_conf.py
demo/gan/gan_conf.py
+52
-6
demo/gan/gan_conf_image.py
demo/gan/gan_conf_image.py
+3
-3
demo/gan/gan_trainer.py
demo/gan/gan_trainer.py
+106
-14
未找到文件。
.gitignore
浏览文件 @
c159e4dd
...
...
@@ -8,3 +8,4 @@ build/
.cproject
.pydevproject
Makefile
.test_env/
demo/gan/.gitignore
浏览文件 @
c159e4dd
...
...
@@ -2,6 +2,5 @@ output/
*.png
.pydevproject
.project
data/
trainLog.txt
data/raw_data/
demo/gan/data/get_mnist_data.sh
0 → 100644
浏览文件 @
c159e4dd
#!/usr/bin/env sh
# 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"
echo
"Downloading..."
for
fname
in
train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
do
if
[
!
-e
$fname
]
;
then
wget
--no-check-certificate
http://yann.lecun.com/exdb/mnist/
${
fname
}
.gz
gunzip
${
fname
}
.gz
fi
done
demo/gan/gan_conf.py
浏览文件 @
c159e4dd
...
...
@@ -26,11 +26,12 @@ is_discriminator = mode == "discriminator"
print
(
'mode=%s'
%
mode
)
noise_dim
=
10
hidden_dim
=
15
sample_dim
=
2
settings
(
batch_size
=
1
00
,
learning_rate
=
1e-
2
,
batch_size
=
1
28
,
learning_rate
=
1e-
4
,
learning_method
=
AdamOptimizer
()
)
...
...
@@ -44,9 +45,30 @@ def discriminator(sample):
"""
param_attr
=
ParamAttr
(
is_static
=
is_generator_training
)
bias_attr
=
ParamAttr
(
is_static
=
is_generator_training
,
initial_mean
=
0
,
initial_mean
=
1.
0
,
initial_std
=
0
)
return
fc_layer
(
input
=
sample
,
name
=
"dis_prob"
,
size
=
2
,
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
,
act
=
ReluActivation
(),
name
=
"dis_hidden_bn"
,
bias_attr
=
bias_attr
,
param_attr
=
ParamAttr
(
is_static
=
is_generator_training
,
initial_mean
=
1.0
,
initial_std
=
0.02
),
use_global_stats
=
False
)
return
fc_layer
(
input
=
hidden_bn
,
name
=
"dis_prob"
,
size
=
2
,
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
act
=
SoftmaxActivation
())
...
...
@@ -57,9 +79,33 @@ def generator(noise):
"""
param_attr
=
ParamAttr
(
is_static
=
is_discriminator_training
)
bias_attr
=
ParamAttr
(
is_static
=
is_discriminator_training
,
initial_mean
=
0
,
initial_mean
=
1.
0
,
initial_std
=
0
)
return
fc_layer
(
input
=
noise
,
hidden
=
fc_layer
(
input
=
noise
,
name
=
"gen_layer_hidden"
,
size
=
hidden_dim
,
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
,
act
=
ReluActivation
(),
name
=
"gen_layer_hidden_bn"
,
bias_attr
=
bias_attr
,
param_attr
=
ParamAttr
(
is_static
=
is_discriminator_training
,
initial_mean
=
1.0
,
initial_std
=
0.02
),
use_global_stats
=
False
)
return
fc_layer
(
input
=
hidden_bn
,
name
=
"gen_layer1"
,
size
=
sample_dim
,
bias_attr
=
bias_attr
,
...
...
demo/gan/gan_conf_image.py
浏览文件 @
c159e4dd
...
...
@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
paddle.trainer_config_helpers
import
*
from
paddle.trainer_config_helpers.layers
import
img_convTrans_layer
from
paddle.trainer_config_helpers.activations
import
LinearActivation
from
numpy.distutils.system_info
import
tmp
...
...
@@ -55,13 +54,14 @@ def convTrans_bn(input, channels, output_x, num_filters, imgSize, stride, name,
padding
=
0
convTrans
=
img_conv
Trans
_layer
(
input
,
filter_size
=
filter_size
,
convTrans
=
img_conv_layer
(
input
,
filter_size
=
filter_size
,
num_filters
=
num_filters
,
name
=
name
+
"_convt"
,
num_channels
=
channels
,
act
=
LinearActivation
(),
groups
=
1
,
stride
=
stride
,
padding
=
padding
,
bias_attr
=
bias_attr
,
param_attr
=
param_attr
,
shared_biases
=
True
,
layer_attr
=
None
,
filter_size_y
=
None
,
stride_y
=
None
,
padding_y
=
None
)
filter_size_y
=
None
,
stride_y
=
None
,
padding_y
=
None
,
trans
=
True
)
convTrans_bn
=
batch_norm_layer
(
convTrans
,
act
=
ReluActivation
(),
...
...
demo/gan/gan_trainer.py
浏览文件 @
c159e4dd
...
...
@@ -22,6 +22,23 @@ from paddle.trainer.config_parser import logger
import
py_paddle.swig_paddle
as
api
from
py_paddle
import
DataProviderConverter
import
matplotlib.pyplot
as
plt
def
plot2DScatter
(
data
,
outputfile
):
# Generate some test data
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.show()
plt
.
savefig
(
outputfile
,
bbox_inches
=
'tight'
)
def
CHECK_EQ
(
a
,
b
):
assert
a
==
b
,
"a=%s, b=%s"
%
(
a
,
b
)
...
...
@@ -32,6 +49,7 @@ def copy_shared_parameters(src, dst):
for
i
in
xrange
(
src
.
getParameterSize
())]
src_params
=
dict
([(
p
.
getName
(),
p
)
for
p
in
src_params
])
for
i
in
xrange
(
dst
.
getParameterSize
()):
dst_param
=
dst
.
getParameter
(
i
)
src_param
=
src_params
.
get
(
dst_param
.
getName
(),
None
)
...
...
@@ -42,19 +60,37 @@ def copy_shared_parameters(src, dst):
CHECK_EQ
(
len
(
src_value
),
len
(
dst_value
))
dst_value
.
copyFrom
(
src_value
)
dst_param
.
setValueUpdated
()
def
print_parameters
(
src
):
src_params
=
[
src
.
getParameter
(
i
)
for
i
in
xrange
(
src
.
getParameterSize
())]
print
"***************"
for
p
in
src_params
:
print
"Name is %s"
%
p
.
getName
()
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'
)
return
numpy
.
random
.
rand
(
batch_size
,
sample_dim
).
astype
(
'float32'
)
*
10.0
-
10.0
# return numpy.random.normal(loc=100.0, scale=100.0, size=(batch_size, sample_dim)).astype('float32')
def
prepare_discriminator_data_batch
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
):
gen_inputs
=
prepare_generator_data_batch
(
batch_size
/
2
,
noise_dim
)
def
get_fake_samples
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
):
gen_inputs
=
prepare_generator_data_batch
(
batch_size
,
noise_dim
)
gen_inputs
.
resize
(
1
)
gen_outputs
=
api
.
Arguments
.
createArguments
(
0
)
generator_machine
.
forward
(
gen_inputs
,
gen_outputs
,
api
.
PASS_TEST
)
fake_samples
=
gen_outputs
.
getSlotValue
(
0
).
copyToNumpyMat
()
return
fake_samples
def
get_training_loss
(
training_machine
,
inputs
):
outputs
=
api
.
Arguments
.
createArguments
(
0
)
training_machine
.
forward
(
inputs
,
outputs
,
api
.
PASS_TEST
)
loss
=
outputs
.
getSlotValue
(
0
).
copyToNumpyMat
()
return
numpy
.
mean
(
loss
)
def
prepare_discriminator_data_batch
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
):
fake_samples
=
get_fake_samples
(
generator_machine
,
batch_size
/
2
,
noise_dim
,
sample_dim
)
real_samples
=
get_real_samples
(
batch_size
/
2
,
sample_dim
)
all_samples
=
numpy
.
concatenate
((
fake_samples
,
real_samples
),
0
)
all_labels
=
numpy
.
concatenate
(
...
...
@@ -65,6 +101,21 @@ def prepare_discriminator_data_batch(
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createCpuVectorFromNumpy
(
all_labels
))
return
inputs
def
prepare_discriminator_data_batch_pos
(
batch_size
,
noise_dim
,
sample_dim
):
real_samples
=
get_real_samples
(
batch_size
,
sample_dim
)
labels
=
numpy
.
ones
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createCpuDenseFromNumpy
(
real_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createCpuVectorFromNumpy
(
labels
))
return
inputs
def
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
):
fake_samples
=
get_fake_samples
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
)
labels
=
numpy
.
zeros
(
batch_size
,
dtype
=
'int32'
)
inputs
=
api
.
Arguments
.
createArguments
(
2
)
inputs
.
setSlotValue
(
0
,
api
.
Matrix
.
createCpuDenseFromNumpy
(
fake_samples
))
inputs
.
setSlotIds
(
1
,
api
.
IVector
.
createCpuVectorFromNumpy
(
labels
))
return
inputs
def
prepare_generator_data_batch
(
batch_size
,
dim
):
noise
=
numpy
.
random
.
normal
(
size
=
(
batch_size
,
dim
)).
astype
(
'float32'
)
...
...
@@ -118,22 +169,63 @@ def main():
dis_trainer
.
startTrain
()
gen_trainer
.
startTrain
()
copy_shared_parameters
(
gen_training_machine
,
dis_training_machine
)
copy_shared_parameters
(
gen_training_machine
,
generator_machine
)
curr_train
=
"dis"
curr_strike
=
0
MAX_strike
=
5
for
train_pass
in
xrange
(
10
):
dis_trainer
.
startTrainPass
()
gen_trainer
.
startTrainPass
()
for
i
in
xrange
(
100000
):
copy_shared_parameters
(
gen_training_machine
,
generator_machine
)
copy_shared_parameters
(
gen_training_machine
,
dis_training_machine
)
data_batch
=
prepare_discriminator_data_batch
(
# 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)
data_batch_dis_pos
=
prepare_discriminator_data_batch_pos
(
batch_size
,
noise_dim
,
sample_dim
)
dis_loss_pos
=
get_training_loss
(
dis_training_machine
,
data_batch_dis_pos
)
data_batch_dis_neg
=
prepare_discriminator_data_batch_neg
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
)
dis_trainer
.
trainOneDataBatch
(
batch_size
,
data_batch
)
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_dim
)
gen_loss
=
get_training_loss
(
gen_training_machine
,
data_batch_gen
)
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
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
copy_shared_parameters
(
dis_training_machine
,
gen_training_machine
)
else
:
if
curr_train
==
"gen"
:
curr_strike
+=
1
else
:
curr_train
=
"gen"
curr_strike
=
1
gen_trainer
.
trainOneDataBatch
(
batch_size
,
data_batch_gen
)
copy_shared_parameters
(
gen_training_machine
,
dis_training_machine
)
copy_shared_parameters
(
gen_training_machine
,
generator_machine
)
copy_shared_parameters
(
dis_training_machine
,
gen_training_machine
)
data_batch
=
prepare_generator_data_batch
(
batch_size
,
noise_dim
)
gen_trainer
.
trainOneDataBatch
(
batch_size
,
data_batch
)
dis_trainer
.
finishTrainPass
()
gen_trainer
.
finishTrainPass
()
fake_samples
=
get_fake_samples
(
generator_machine
,
batch_size
,
noise_dim
,
sample_dim
)
plot2DScatter
(
fake_samples
,
"./train_pass%s.png"
%
train_pass
)
dis_trainer
.
finishTrain
()
gen_trainer
.
finishTrain
()
...
...
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