提交 7f9a4248 编写于 作者: C cxysteven

vae demo

上级 f45b45e2
#Variational Autoencoder (VAE)
This demo implements VAE training described in the original paper (https://arxiv.org/abs/1312.6114).
In order to run the model, first download the MNIST dataset by running the shell script in ./data.
Then you can run the command below. The flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).
$python vae_train.py [--use_gpu 1]
The generated images will be stored in ./samples/
The corresponding models will be stored in ./params/
#!/usr/bin/env sh
# This script downloads the mnist data and unzips it.
set -e
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
rm -rf "$DIR/mnist_data"
mkdir "$DIR/mnist_data"
cd "$DIR/mnist_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
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
class MNISTloader():
def __init__(self,
data_path="./data/mnist_data/",
batch_size=60,
process='train'):
self.batch_size = batch_size
self.data_path = data_path
self._pointer = 0
self.image_batches = np.array([])
self.process = process
def _extract_images(self, filename, n):
f = open(filename, 'rb')
f.read(16)
data = np.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
#Mapping data into [-1, 1]
data = data / 255. * 2. - 1
data_batches = np.split(data, 60000 / self.batch_size, 0)
f.close()
return data_batches
@property
def pointer(self):
return self._pointer
def load_data(self):
TRAIN_IMAGES = '%s/train-images-idx3-ubyte' % self.data_path
TEST_IMAGES = '%s/t10k-images-idx3-ubyte' % self.data_path
if self.process == 'train':
self.image_batches = self._extract_images(TRAIN_IMAGES, 60000)
else:
self.image_batches = self._extract_images(TEST_IMAGES, 10000)
def next_batch(self):
batch = self.image_batches[self._pointer]
self._pointer = (self._pointer + 1) % (60000 / self.batch_size)
return np.array(batch)
def reset_pointer(self):
self._pointer = 0
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
import numpy as np
is_generating = get_config_arg("is_generating", bool, False)
settings(batch_size=32, learning_rate=1e-3, learning_method=AdamOptimizer())
X_dim = 28 * 28
h_dim = 128
z_dim = 100
def reparameterization(mu, logvar):
eps = ParamAttr(initial_mean=0., initial_std=1)
with mixed_layer() as sigma:
sigma += dotmul_projection(layer_math.exp(logvar) * 0.5, param_attr=eps)
return mu + sigma
def q_func(X):
"""
xavier initialization
"""
param_attr = ParamAttr(
name='share.w', initial_mean=0., initial_std=1. / np.sqrt(X_dim / 2.))
mu_param = ParamAttr(
name='mu.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
logvar_param = ParamAttr(
name='logvar.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
bias_attr = ParamAttr(name='share.bias', initial_mean=0., initial_std=0.)
mu_bias = ParamAttr(name='mu.bias', initial_mean=0., initial_std=0.)
logvar_bias = ParamAttr(name='logvar.bias', initial_mean=0., initial_std=0.)
share_layer = fc_layer(
X,
size=h_dim,
param_attr=param_attr,
bias_attr=bias_attr,
act=ReluActivation())
return (fc_layer(
share_layer,
size=z_dim,
param_attr=mu_param,
bias_attr=mu_bias,
act=LinearActivation()), fc_layer(
share_layer,
size=z_dim,
param_attr=logvar_param,
bias_attr=logvar_bias,
act=LinearActivation()))
def generator(z):
hidden_param = ParamAttr(
name='hidden.w', initial_mean=0., initial_std=1. / np.sqrt(z_dim / 2.))
hidden_bias = ParamAttr(name='hidden.bias', initial_mean=0., initial_std=0.)
prob_param = ParamAttr(
name='prob.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
prob_bias = ParamAttr(name='prob.bias', initial_mean=0., initial_std=0.)
hidden_layer = fc_layer(
z,
size=h_dim,
act=ReluActivation(),
param_attr=hidden_param,
bias_attr=hidden_bias)
prob = fc_layer(
hidden_layer,
size=X_dim,
act=SigmoidActivation(),
param_attr=prob_param,
bias_attr=prob_bias)
return prob
def reconstruct_error(prob, X):
cost = multi_binary_label_cross_entropy(input=prob, label=X)
return cost
def KL_loss(mu, logvar):
with mixed_layer() as mu_square:
mu_square += dotmul_operator(mu, mu, scale=1.)
cost = 0.5 * sum_cost(layer_math.exp(logvar) + mu_square - 1. - logvar)
return cost
if not is_generating:
x_batch = data_layer(name='x_batch', size=X_dim)
mu, logvar = q_func(x_batch)
z_samples = reparameterization(mu, logvar)
prob = generator(z_samples)
outputs(reconstruct_error(prob, x_batch) + KL_loss(mu, logvar))
else:
z_samples = data_layer(name='noise', size=z_dim)
outputs(generator(z_samples))
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import random
import numpy as np
import cPickle
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 dataloader
import matplotlib.pyplot as plt
def plot_samples(samples):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
plt.subplot(gs[i])
plt.axis('off')
plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
return fig
def CHECK_EQ(a, b):
assert a == b, "a=%s, b=%s" % (a, b)
def get_fake_samples(generator_machine, batch_size, noise):
gen_inputs = api.Arguments.createArguments(1)
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()
return fake_samples
def copy_shared_parameters(src, dst):
'''
copy the parameters from src to dst
:param src: the source of the parameters
:type src: GradientMachine
:param dst: the destination of the parameters
:type dst: GradientMachine
'''
src_params = [src.getParameter(i) 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)
if src_param is None:
continue
src_value = src_param.getBuf(api.PARAMETER_VALUE)
dst_value = dst_param.getBuf(api.PARAMETER_VALUE)
CHECK_EQ(len(src_value), len(dst_value))
dst_value.copyFrom(src_value)
dst_param.setValueUpdated()
def find(iterable, cond):
for item in iterable:
if cond(item):
return item
return None
def get_layer_size(model_conf, layer_name):
layer_conf = find(model_conf.layers, lambda x: x.name == layer_name)
assert layer_conf is not None, "Cannot find '%s' layer" % layer_name
return layer_conf.size
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--use_gpu", default="1", help="1 means use gpu for training")
parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
args = parser.parse_args()
use_gpu = args.use_gpu
assert use_gpu in ["0", "1"]
if not os.path.exists("./samples/"):
os.makedirs("./samples/")
if not os.path.exists("./params/"):
os.makedirs("./params/")
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
'--log_period=1000', '--gpu_id=' + args.gpu_id,
'--save_dir=' + "./params/")
conf = "vae_conf.py"
trainer_conf = parse_config(conf, "is_generating=False")
gener_conf = parse_config(conf, "is_generating=True")
batch_size = trainer_conf.opt_config.batch_size
noise_dim = get_layer_size(gener_conf.model_config, "noise")
mnist = dataloader.MNISTloader(batch_size=batch_size)
mnist.load_data()
training_machine = api.GradientMachine.createFromConfigProto(
trainer_conf.model_config)
generator_machine = api.GradientMachine.createFromConfigProto(
gener_conf.model_config)
trainer = api.Trainer.create(trainer_conf, training_machine)
trainer.startTrain()
for train_pass in xrange(100):
trainer.startTrainPass()
mnist.reset_pointer()
i = 0
it = 0
while mnist.pointer != 0 or i == 0:
X = mnist.next_batch().astype('float32')
inputs = api.Arguments.createArguments(1)
inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(X))
trainer.trainOneDataBatch(batch_size, inputs)
if it % 1000 == 0:
outputs = api.Arguments.createArguments(0)
training_machine.forward(inputs, outputs, api.PASS_TEST)
loss = np.mean(outputs.getSlotValue(0).copyToNumpyMat())
print "\niter: {}".format(str(it).zfill(3))
print "VAE loss: {}".format(str(loss).zfill(3))
#Sync parameters between networks (GradientMachine) at the beginning
copy_shared_parameters(training_machine, generator_machine)
z_samples = np.random.randn(batch_size,
noise_dim).astype('float32')
samples = get_fake_samples(generator_machine, batch_size,
z_samples)
#Generating the first 16 images for a picture.
figure = plot_samples(samples[:16])
plt.savefig(
"./samples/{}_{}.png".format(
str(train_pass).zfill(3), str(i).zfill(3)),
bbox_inches='tight')
plt.close(figure)
i += 1
it += 1
trainer.finishTrainPass()
trainer.finishTrain()
if __name__ == '__main__':
main()
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