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added get_mnist_data and demo/gan and updated the gan_conf and gan_trainer python files

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avx_docs
wangyang59 9 years ago
parent bd8613acf8
commit c159e4dd78
6 changed files with 182 additions and 25 deletions
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1
.gitignore vendored
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@ -8,3 +8,4 @@ build/
.cproject
.pydevproject
Makefile
.test_env/

3
demo/gan/.gitignore vendored
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@ -2,6 +2,5 @@ output/
*.png
.pydevproject
.project
data/
trainLog.txt
data/raw_data/

19
demo/gan/data/get_mnist_data.sh
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@ -0,0 +1,19 @@
#!/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

58
demo/gan/gan_conf.py
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@ -26,11 +26,12 @@ is_discriminator = mode == "discriminator"
print('mode=%s' % mode)
noise_dim = 10
hidden_dim = 15
sample_dim = 2
settings(
batch_size=100,
learning_rate=1e-2,
batch_size=128,
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,

6
demo/gan/gan_conf_image.py
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@ -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_convTrans_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(),

120
demo/gan/gan_trainer.py
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@ -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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