提交 1adc6a28 编写于 作者: G gangliao 提交者: GitHub

Merge pull request #829 from reyoung/feature/travis_pre_commit_checks

Feature/travis pre commit checks
......@@ -8,10 +8,13 @@ os:
env:
- JOB=DOCS
- JOB=BUILD_AND_TEST
- JOB=PRE_COMMIT
matrix:
exclude:
- os: osx
env: JOB=DOCS # Only generate documentation in linux
env: JOB=DOCS # Only generate documentation in linux.
- os: osx
env: JOB=PRE_COMMIT # Only check pre-commit hook in linux
addons:
apt:
......@@ -39,6 +42,7 @@ addons:
- lcov
- graphviz
- swig
- clang-format-3.8
before_install:
- |
if [ ${JOB} == "BUILD_AND_TEST" ]; then
......@@ -53,7 +57,8 @@ before_install:
fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
- if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
- pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme
- if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
- pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme pre-commit
script:
- paddle/scripts/travis/main.sh
notifications:
......
# External dependency to Google protobuf.
http_archive(
name = "protobuf",
url = "http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
sha256 = "0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
strip_prefix = "protobuf-3.1.0",
)
name="protobuf",
url="http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
sha256="0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
strip_prefix="protobuf-3.1.0", )
# External dependency to gtest 1.7.0. This method comes from
# https://www.bazel.io/versions/master/docs/tutorial/cpp.html.
new_http_archive(
name = "gtest",
url = "https://github.com/google/googletest/archive/release-1.7.0.zip",
sha256 = "b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
build_file = "third_party/gtest.BUILD",
strip_prefix = "googletest-release-1.7.0",
)
name="gtest",
url="https://github.com/google/googletest/archive/release-1.7.0.zip",
sha256="b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
build_file="third_party/gtest.BUILD",
strip_prefix="googletest-release-1.7.0", )
......@@ -25,4 +25,3 @@ test 4 2 256 512
test 4 2 512 128
test 4 2 512 256
test 4 2 512 512
......@@ -10,4 +10,4 @@ Then you can run the command below. The flag -d specifies the training data (cif
$python gan_trainer.py -d cifar --use_gpu 1
The generated images will be stored in ./cifar_samples/
The corresponding models will be stored in ./cifar_params/
\ No newline at end of file
The corresponding models will be stored in ./cifar_params/
......@@ -15,4 +15,3 @@ set -e
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar zxf cifar-10-python.tar.gz
rm cifar-10-python.tar.gz
......@@ -15,5 +15,3 @@ do
gunzip ${fname}.gz
fi
done
......@@ -14,10 +14,9 @@
from paddle.trainer_config_helpers import *
mode = get_config_arg("mode", str, "generator")
assert mode in set(["generator",
"discriminator",
"generator_training",
"discriminator_training"])
assert mode in set([
"generator", "discriminator", "generator_training", "discriminator_training"
])
is_generator_training = mode == "generator_training"
is_discriminator_training = mode == "discriminator_training"
......@@ -38,8 +37,8 @@ sample_dim = 2
settings(
batch_size=128,
learning_rate=1e-4,
learning_method=AdamOptimizer(beta1=0.5)
)
learning_method=AdamOptimizer(beta1=0.5))
def discriminator(sample):
"""
......@@ -50,70 +49,87 @@ def discriminator(sample):
of the sample is from real data.
"""
param_attr = ParamAttr(is_static=is_generator_training)
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())
hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
bias_attr=bias_attr,
param_attr=param_attr,
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())
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())
hidden2 = fc_layer(
input=hidden,
name="dis_hidden2",
size=hidden_dim,
bias_attr=bias_attr,
param_attr=param_attr,
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())
def generator(noise):
"""
generator generates a sample given noise
"""
param_attr = ParamAttr(is_static=is_discriminator_training)
bias_attr = ParamAttr(is_static=is_discriminator_training,
initial_mean=1.0,
initial_std=0)
hidden = fc_layer(input=noise,
name="gen_layer_hidden",
size=hidden_dim,
bias_attr=bias_attr,
param_attr=param_attr,
act=ReluActivation())
hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
bias_attr=bias_attr,
param_attr=param_attr,
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,
param_attr=param_attr,
act=LinearActivation())
bias_attr = ParamAttr(
is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
hidden = fc_layer(
input=noise,
name="gen_layer_hidden",
size=hidden_dim,
bias_attr=bias_attr,
param_attr=param_attr,
act=ReluActivation())
hidden2 = fc_layer(
input=hidden,
name="gen_hidden2",
size=hidden_dim,
bias_attr=bias_attr,
param_attr=param_attr,
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,
param_attr=param_attr,
act=LinearActivation())
if is_generator_training:
noise = data_layer(name="noise", size=noise_dim)
......@@ -126,7 +142,8 @@ if is_generator_training or is_discriminator_training:
label = data_layer(name="label", size=1)
prob = discriminator(sample)
cost = cross_entropy(input=prob, label=label)
classification_error_evaluator(input=prob, label=label, name=mode+'_error')
classification_error_evaluator(
input=prob, label=label, name=mode + '_error')
outputs(cost)
if is_generator:
......
......@@ -15,10 +15,9 @@ from paddle.trainer_config_helpers import *
mode = get_config_arg("mode", str, "generator")
dataSource = get_config_arg("data", str, "mnist")
assert mode in set(["generator",
"discriminator",
"generator_training",
"discriminator_training"])
assert mode in set([
"generator", "discriminator", "generator_training", "discriminator_training"
])
is_generator_training = mode == "generator_training"
is_discriminator_training = mode == "discriminator_training"
......@@ -36,24 +35,33 @@ noise_dim = 100
gf_dim = 64
df_dim = 64
if dataSource == "mnist":
sample_dim = 28 # image dim
c_dim = 1 # image color
sample_dim = 28 # image dim
c_dim = 1 # image color
else:
sample_dim = 32
c_dim = 3
s2, s4 = int(sample_dim/2), int(sample_dim/4),
s8, s16 = int(sample_dim/8), int(sample_dim/16)
s2, s4 = int(sample_dim / 2), int(sample_dim / 4),
s8, s16 = int(sample_dim / 8), int(sample_dim / 16)
settings(
batch_size=128,
learning_rate=2e-4,
learning_method=AdamOptimizer(beta1=0.5)
)
learning_method=AdamOptimizer(beta1=0.5))
def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
param_attr, bias_attr, param_attr_bn, bn, trans=False,
act=ReluActivation()):
def conv_bn(input,
channels,
imgSize,
num_filters,
output_x,
stride,
name,
param_attr,
bias_attr,
param_attr_bn,
bn,
trans=False,
act=ReluActivation()):
"""
conv_bn is a utility function that constructs a convolution/deconv layer
with an optional batch_norm layer
......@@ -63,10 +71,10 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
:param trans: whether to use conv (False) or deconv (True)
:type trans: bool
"""
# calculate the filter_size and padding size based on the given
# imgSize and ouput size
tmp = imgSize - (output_x - 1) * stride
tmp = imgSize - (output_x - 1) * stride
if tmp <= 1 or tmp > 5:
raise ValueError("conv input-output dimension does not fit")
elif tmp <= 3:
......@@ -76,111 +84,134 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
filter_size = tmp
padding = 0
print (imgSize, output_x, stride, filter_size, padding)
print(imgSize, output_x, stride, filter_size, padding)
if trans:
nameApx = "_conv"
else:
nameApx = "_convt"
if bn:
conv = img_conv_layer(input, filter_size=filter_size,
num_filters=num_filters,
name=name + nameApx, 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,
trans=trans)
conv_bn = batch_norm_layer(conv,
act=act,
name=name + nameApx + "_bn",
bias_attr=bias_attr,
param_attr=param_attr_bn,
use_global_stats=False)
conv = img_conv_layer(
input,
filter_size=filter_size,
num_filters=num_filters,
name=name + nameApx,
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,
trans=trans)
conv_bn = batch_norm_layer(
conv,
act=act,
name=name + nameApx + "_bn",
bias_attr=bias_attr,
param_attr=param_attr_bn,
use_global_stats=False)
return conv_bn
else:
conv = img_conv_layer(input, filter_size=filter_size,
num_filters=num_filters,
name=name + nameApx, num_channels=channels,
act=act, 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,
trans=trans)
conv = img_conv_layer(
input,
filter_size=filter_size,
num_filters=num_filters,
name=name + nameApx,
num_channels=channels,
act=act,
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,
trans=trans)
return conv
def generator(noise):
"""
generator generates a sample given noise
"""
param_attr = ParamAttr(is_static=is_discriminator_training,
initial_mean=0.0,
initial_std=0.02)
bias_attr = ParamAttr(is_static=is_discriminator_training,
initial_mean=0.0,
initial_std=0.0)
param_attr_bn=ParamAttr(is_static=is_discriminator_training,
initial_mean=1.0,
initial_std=0.02)
h1 = fc_layer(input=noise,
name="gen_layer_h1",
size=s8 * s8 * gf_dim * 4,
bias_attr=bias_attr,
param_attr=param_attr,
act=LinearActivation())
h1_bn = batch_norm_layer(h1,
act=ReluActivation(),
name="gen_layer_h1_bn",
bias_attr=bias_attr,
param_attr=param_attr_bn,
use_global_stats=False)
h2_bn = conv_bn(h1_bn,
channels=gf_dim*4,
output_x=s8,
num_filters=gf_dim*2,
imgSize=s4,
stride=2,
name="gen_layer_h2",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True,
trans=True)
h3_bn = conv_bn(h2_bn,
channels=gf_dim*2,
output_x=s4,
num_filters=gf_dim,
imgSize=s2,
stride=2,
name="gen_layer_h3",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True,
trans=True)
return conv_bn(h3_bn,
channels=gf_dim,
output_x=s2,
num_filters=c_dim,
imgSize=sample_dim,
stride=2,
name="gen_layer_h4",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=False,
trans=True,
act=TanhActivation())
param_attr = ParamAttr(
is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.02)
bias_attr = ParamAttr(
is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.0)
param_attr_bn = ParamAttr(
is_static=is_discriminator_training, initial_mean=1.0, initial_std=0.02)
h1 = fc_layer(
input=noise,
name="gen_layer_h1",
size=s8 * s8 * gf_dim * 4,
bias_attr=bias_attr,
param_attr=param_attr,
act=LinearActivation())
h1_bn = batch_norm_layer(
h1,
act=ReluActivation(),
name="gen_layer_h1_bn",
bias_attr=bias_attr,
param_attr=param_attr_bn,
use_global_stats=False)
h2_bn = conv_bn(
h1_bn,
channels=gf_dim * 4,
output_x=s8,
num_filters=gf_dim * 2,
imgSize=s4,
stride=2,
name="gen_layer_h2",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True,
trans=True)
h3_bn = conv_bn(
h2_bn,
channels=gf_dim * 2,
output_x=s4,
num_filters=gf_dim,
imgSize=s2,
stride=2,
name="gen_layer_h3",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True,
trans=True)
return conv_bn(
h3_bn,
channels=gf_dim,
output_x=s2,
num_filters=c_dim,
imgSize=sample_dim,
stride=2,
name="gen_layer_h4",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=False,
trans=True,
act=TanhActivation())
def discriminator(sample):
......@@ -191,58 +222,60 @@ def discriminator(sample):
of the sample is from generator and dimension 1 is the probabblity
of the sample is from real data.
"""
param_attr = ParamAttr(is_static=is_generator_training,
initial_mean=0.0,
initial_std=0.02)
bias_attr = ParamAttr(is_static=is_generator_training,
initial_mean=0.0,
initial_std=0.0)
param_attr_bn=ParamAttr(is_static=is_generator_training,
initial_mean=1.0,
initial_std=0.02)
h0 = conv_bn(sample,
channels=c_dim,
imgSize=sample_dim,
num_filters=df_dim,
output_x=s2,
stride=2,
name="dis_h0",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=False)
h1_bn = conv_bn(h0,
channels=df_dim,
imgSize=s2,
num_filters=df_dim*2,
output_x=s4,
stride=2,
name="dis_h1",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True)
h2_bn = conv_bn(h1_bn,
channels=df_dim*2,
imgSize=s4,
num_filters=df_dim*4,
output_x=s8,
stride=2,
name="dis_h2",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True)
return fc_layer(input=h2_bn, name="dis_prob", size=2,
bias_attr=bias_attr,
param_attr=param_attr,
act=SoftmaxActivation())
param_attr = ParamAttr(
is_static=is_generator_training, initial_mean=0.0, initial_std=0.02)
bias_attr = ParamAttr(
is_static=is_generator_training, initial_mean=0.0, initial_std=0.0)
param_attr_bn = ParamAttr(
is_static=is_generator_training, initial_mean=1.0, initial_std=0.02)
h0 = conv_bn(
sample,
channels=c_dim,
imgSize=sample_dim,
num_filters=df_dim,
output_x=s2,
stride=2,
name="dis_h0",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=False)
h1_bn = conv_bn(
h0,
channels=df_dim,
imgSize=s2,
num_filters=df_dim * 2,
output_x=s4,
stride=2,
name="dis_h1",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True)
h2_bn = conv_bn(
h1_bn,
channels=df_dim * 2,
imgSize=s4,
num_filters=df_dim * 4,
output_x=s8,
stride=2,
name="dis_h2",
param_attr=param_attr,
bias_attr=bias_attr,
param_attr_bn=param_attr_bn,
bn=True)
return fc_layer(
input=h2_bn,
name="dis_prob",
size=2,
bias_attr=bias_attr,
param_attr=param_attr,
act=SoftmaxActivation())
if is_generator_training:
......@@ -250,13 +283,14 @@ if is_generator_training:
sample = generator(noise)
if is_discriminator_training:
sample = data_layer(name="sample", size=sample_dim * sample_dim*c_dim)
sample = data_layer(name="sample", size=sample_dim * sample_dim * c_dim)
if is_generator_training or is_discriminator_training:
label = data_layer(name="label", size=1)
prob = discriminator(sample)
cost = cross_entropy(input=prob, label=label)
classification_error_evaluator(input=prob, label=label, name=mode+'_error')
classification_error_evaluator(
input=prob, label=label, name=mode + '_error')
outputs(cost)
if is_generator:
......
......@@ -16,7 +16,7 @@ import argparse
import random
import numpy
import cPickle
import sys,os
import sys, os
from PIL import Image
from paddle.trainer.config_parser import parse_config
......@@ -24,6 +24,7 @@ from paddle.trainer.config_parser import logger
import py_paddle.swig_paddle as api
import matplotlib.pyplot as plt
def plot2DScatter(data, outputfile):
'''
Plot the data as a 2D scatter plot and save to outputfile
......@@ -41,9 +42,11 @@ def plot2DScatter(data, outputfile):
plt.scatter(x, y)
plt.savefig(outputfile, bbox_inches='tight')
def CHECK_EQ(a, b):
assert a == b, "a=%s, b=%s" % (a, b)
def copy_shared_parameters(src, dst):
'''
copy the parameters from src to dst
......@@ -52,11 +55,9 @@ def copy_shared_parameters(src, dst):
:param dst: the destination of the parameters
:type dst: GradientMachine
'''
src_params = [src.getParameter(i)
for i in xrange(src.getParameterSize())]
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)
......@@ -67,15 +68,17 @@ 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())]
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()
print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray(
)
def load_mnist_data(imageFile):
f = open(imageFile, "rb")
......@@ -86,33 +89,36 @@ def load_mnist_data(imageFile):
n = 60000
else:
n = 10000
data = numpy.fromfile(f, 'ubyte', count=n*28*28).reshape((n, 28*28))
data = numpy.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
data = data / 255.0 * 2.0 - 1.0
f.close()
return data.astype('float32')
def load_cifar_data(cifar_path):
batch_size = 10000
data = numpy.zeros((5*batch_size, 32*32*3), dtype = "float32")
data = numpy.zeros((5 * batch_size, 32 * 32 * 3), dtype="float32")
for i in range(1, 6):
file = cifar_path + "/data_batch_" + str(i)
fo = open(file, 'rb')
dict = cPickle.load(fo)
fo.close()
data[(i - 1)*batch_size:(i*batch_size), :] = dict["data"]
data[(i - 1) * batch_size:(i * batch_size), :] = dict["data"]
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:
if images.shape[1] == 28 * 28:
h, w, c = 28, 28, 1
else:
h, w, c = 32, 32, 3
......@@ -124,6 +130,7 @@ def merge(images, size):
((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
return img.astype('uint8')
def save_images(images, path):
merged_img = merge(images, [8, 8])
if merged_img.shape[2] == 1:
......@@ -131,14 +138,17 @@ def save_images(images, path):
else:
im = Image.fromarray(merged_img, mode="RGB")
im.save(path)
def get_real_samples(batch_size, data_np):
return data_np[numpy.random.choice(data_np.shape[0], batch_size,
replace=False),:]
return data_np[numpy.random.choice(
data_np.shape[0], batch_size, replace=False), :]
def get_noise(batch_size, noise_dim):
return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
def get_fake_samples(generator_machine, batch_size, noise):
gen_inputs = api.Arguments.createArguments(1)
gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
......@@ -147,12 +157,14 @@ def get_fake_samples(generator_machine, batch_size, noise):
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_pos(batch_size, data_np):
real_samples = get_real_samples(batch_size, data_np)
labels = numpy.ones(batch_size, dtype='int32')
......@@ -161,6 +173,7 @@ def prepare_discriminator_data_batch_pos(batch_size, data_np):
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
return inputs
def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
fake_samples = get_fake_samples(generator_machine, batch_size, noise)
labels = numpy.zeros(batch_size, dtype='int32')
......@@ -169,6 +182,7 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
return inputs
def prepare_generator_data_batch(batch_size, noise):
label = numpy.ones(batch_size, dtype='int32')
inputs = api.Arguments.createArguments(2)
......@@ -193,10 +207,9 @@ def get_layer_size(model_conf, layer_name):
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
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")
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()
data_source = args.data_source
use_gpu = args.use_gpu
......@@ -208,30 +221,32 @@ def main():
if not os.path.exists("./%s_params/" % data_source):
os.makedirs("./%s_params/" % data_source)
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10', '--log_period=100',
'--gpu_id=' + args.gpu_id, '--save_dir=' + "./%s_params/" % data_source)
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
'--log_period=100', '--gpu_id=' + args.gpu_id,
'--save_dir=' + "./%s_params/" % data_source)
if data_source == "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=" + data_source)
dis_conf = parse_config(conf, "mode=discriminator_training,data=" + data_source)
dis_conf = parse_config(conf,
"mode=discriminator_training,data=" + data_source)
generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
batch_size = dis_conf.opt_config.batch_size
noise_dim = get_layer_size(gen_conf.model_config, "noise")
if data_source == "mnist":
data_np = load_mnist_data("./data/mnist_data/train-images-idx3-ubyte")
elif data_source == "cifar":
data_np = load_cifar_data("./data/cifar-10-batches-py/")
else:
data_np = load_uniform_data()
# this creates a gradient machine for discriminator
dis_training_machine = api.GradientMachine.createFromConfigProto(
dis_conf.model_config)
......@@ -244,26 +259,24 @@ def main():
logger.info(str(generator_conf.model_config))
generator_machine = api.GradientMachine.createFromConfigProto(
generator_conf.model_config)
dis_trainer = api.Trainer.create(
dis_conf, dis_training_machine)
gen_trainer = api.Trainer.create(
gen_conf, gen_training_machine)
dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
dis_trainer.startTrain()
gen_trainer.startTrain()
# Sync parameters between networks (GradientMachine) at the beginning
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 = 5
for train_pass in xrange(100):
dis_trainer.startTrainPass()
gen_trainer.startTrainPass()
......@@ -272,23 +285,25 @@ def main():
noise = get_noise(batch_size, noise_dim)
data_batch_dis_pos = prepare_discriminator_data_batch_pos(
batch_size, data_np)
dis_loss_pos = get_training_loss(dis_training_machine, data_batch_dis_pos)
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)
dis_loss_neg = get_training_loss(dis_training_machine, data_batch_dis_neg)
dis_loss_neg = get_training_loss(dis_training_machine,
data_batch_dis_neg)
dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
# Do forward pass in generator to get the gen_loss
data_batch_gen = prepare_generator_data_batch(
batch_size, noise)
data_batch_gen = prepare_generator_data_batch(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_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)
# Decide which network to train based on the training history
# And the relative size of the loss
if (not (curr_train == "dis" and curr_strike == MAX_strike)) and \
......@@ -297,11 +312,12 @@ def main():
curr_strike += 1
else:
curr_train = "dis"
curr_strike = 1
curr_strike = 1
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
copy_shared_parameters(dis_training_machine, gen_training_machine)
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
copy_shared_parameters(dis_training_machine,
gen_training_machine)
else:
if curr_train == "gen":
curr_strike += 1
......@@ -311,19 +327,23 @@ def main():
gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
# TODO: add API for paddle to allow true parameter sharing between different GradientMachines
# so that we do not need to copy shared parameters.
copy_shared_parameters(gen_training_machine, dis_training_machine)
copy_shared_parameters(gen_training_machine,
dis_training_machine)
copy_shared_parameters(gen_training_machine, generator_machine)
dis_trainer.finishTrainPass()
gen_trainer.finishTrainPass()
# At the end of each pass, save the generated samples/images
fake_samples = get_fake_samples(generator_machine, batch_size, noise)
if data_source == "uniform":
plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
(data_source, train_pass))
else:
save_images(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
save_images(fake_samples, "./%s_samples/train_pass%s.png" %
(data_source, train_pass))
dis_trainer.finishTrain()
gen_trainer.finishTrain()
if __name__ == '__main__':
main()
......@@ -13,7 +13,6 @@
# 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.
"""
This configuration is a demonstration of how to implement the stacked LSTM
with residual connections, i.e. an LSTM layer takes the sum of the hidden states
......@@ -46,11 +45,12 @@ is_predict = get_config_arg('is_predict', bool, False)
trn = 'data/train.list' if not is_predict else None
tst = 'data/test.list' if not is_predict else 'data/pred.list'
process = 'process' if not is_predict else 'process_predict'
define_py_data_sources2(train_list=trn,
test_list=tst,
module="dataprovider_emb",
obj=process,
args={"dictionary": word_dict})
define_py_data_sources2(
train_list=trn,
test_list=tst,
module="dataprovider_emb",
obj=process,
args={"dictionary": word_dict})
batch_size = 128 if not is_predict else 1
settings(
......@@ -58,10 +58,9 @@ settings(
learning_rate=2e-3,
learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25
)
gradient_clipping_threshold=25)
bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
data = data_layer(name="word", size=len(word_dict))
emb = embedding_layer(input=data, size=128)
......@@ -73,17 +72,15 @@ for i in range(3):
# The input to the current layer is the sum of the hidden state
# and input of the previous layer.
current_input = addto_layer(input=[previous_input, previous_hidden_state])
hidden_state = simple_lstm(input=current_input, size=128,
lstm_cell_attr=ExtraAttr(drop_rate=0.1))
hidden_state = simple_lstm(
input=current_input, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
previous_input, previous_hidden_state = current_input, hidden_state
lstm = previous_hidden_state
lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
output = fc_layer(input=lstm_last, size=2,
bias_attr=bias_attr,
act=SoftmaxActivation())
output = fc_layer(
input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
if is_predict:
maxid = maxid_layer(output)
......
......@@ -33,7 +33,7 @@ def extract_dict_features(pair_file, feature_file):
ctx_n1 = sentence_list[verb_index - 1]
else:
ctx_n1 = 'bos'
if verb_index > 1:
mark[verb_index - 2] = 1
ctx_n2 = sentence_list[verb_index - 2]
......@@ -48,7 +48,7 @@ def extract_dict_features(pair_file, feature_file):
ctx_p1 = sentence_list[verb_index + 1]
else:
ctx_p1 = 'eos'
if verb_index < len(labels_list) - 3:
mark[verb_index + 2] = 1
ctx_p2 = sentence_list[verb_index + 2]
......@@ -69,7 +69,6 @@ def extract_dict_features(pair_file, feature_file):
feature_out.write(feature_str + '\n')
if __name__ == '__main__':
usage = '-p pair_file -f feature_file'
......
......@@ -66,8 +66,8 @@ def transform_labels(sentences, labels):
else:
verb_list = []
for x in labels[i][0]:
if x !='-':
verb_list.append(x)
if x != '-':
verb_list.append(x)
for j in xrange(1, len(labels[i])):
label_list = labels[i][j]
......@@ -93,7 +93,7 @@ def transform_labels(sentences, labels):
is_in_bracket = True
else:
print 'error:', ll
sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq))
sen_lab_pair.append((sentences[i], verb_list[j - 1], label_seq))
return sen_lab_pair
......@@ -103,7 +103,7 @@ def write_file(sen_lab_pair, output_file):
sentence = x[0]
label_seq = ' '.join(x[2])
assert len(sentence.split()) == len(x[2])
fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n')
fout.write(sentence + '\t' + x[1] + '\t' + label_seq + '\n')
if __name__ == '__main__':
......
......@@ -21,7 +21,7 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
settings.word_dict = word_dict
settings.label_dict = label_dict
settings.predicate_dict = predicate_dict
#all inputs are integral and sequential type
settings.slots = [
integer_value_sequence(len(word_dict)),
......@@ -29,25 +29,28 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)),
integer_value_sequence(2),
integer_value_sequence(len(word_dict)),
integer_value_sequence(len(predicate_dict)), integer_value_sequence(2),
integer_value_sequence(len(label_dict))
]
def get_batch_size(yeild_data):
return len(yeild_data[0])
@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
@provider(
init_hook=hook,
should_shuffle=True,
calc_batch_size=get_batch_size,
can_over_batch_size=False,
cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line in fdata:
sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = \
line.strip().split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
......
......@@ -20,7 +20,7 @@ from paddle.trainer_config_helpers import *
#file paths
word_dict_file = './data/wordDict.txt'
label_dict_file = './data/targetDict.txt'
predicate_file= './data/verbDict.txt'
predicate_file = './data/verbDict.txt'
train_list_file = './data/train.list'
test_list_file = './data/test.list'
......@@ -47,7 +47,6 @@ if not is_predict:
w = line.strip()
predicate_dict[w] = i
if is_test:
train_list_file = None
......@@ -57,9 +56,11 @@ if not is_predict:
test_list=test_list_file,
module='dataprovider',
obj='process',
args={'word_dict': word_dict,
'label_dict': label_dict,
'predicate_dict': predicate_dict })
args={
'word_dict': word_dict,
'label_dict': label_dict,
'predicate_dict': predicate_dict
})
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
......@@ -77,24 +78,16 @@ mark_dim = 5
hidden_dim = 512
depth = 8
########################### Optimizer #######################################
settings(
batch_size=150,
learning_method=MomentumOptimizer(momentum=0),
learning_rate=2e-2,
regularization=L2Regularization(8e-4),
is_async=False,
model_average=ModelAverage(average_window=0.5,
max_average_window=10000),
)
model_average=ModelAverage(
average_window=0.5, max_average_window=10000), )
####################################### network ##############################
#8 features and 1 target
......@@ -108,22 +101,28 @@ ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
mark = data_layer(name='mark_data', size=mark_dict_len)
if not is_predict:
target = data_layer(name='target', size=label_dict_len)
default_std=1/math.sqrt(hidden_dim)/3.0
default_std = 1 / math.sqrt(hidden_dim) / 3.0
emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
std_0 = ParameterAttribute(initial_std=0.)
std_default = ParameterAttribute(initial_std=default_std)
predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std))
mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
word_input=[word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [embedding_layer(size=word_dim, input=x, param_attr=emb_para) for x in word_input]
std_default = ParameterAttribute(initial_std=default_std)
predicate_embedding = embedding_layer(
size=word_dim,
input=predicate,
param_attr=ParameterAttribute(
name='vemb', initial_std=default_std))
mark_embedding = embedding_layer(
name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
embedding_layer(
size=word_dim, input=x, param_attr=emb_para) for x in word_input
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
......@@ -131,84 +130,89 @@ hidden_0 = mixed_layer(
name='hidden0',
size=hidden_dim,
bias_attr=std_default,
input=[ full_matrix_projection(input=emb, param_attr=std_default ) for emb in emb_layers ])
input=[
full_matrix_projection(
input=emb, param_attr=std_default) for emb in emb_layers
])
mix_hidden_lr = 1e-3
lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = lstmemory(name='lstm0',
input=hidden_0,
act=ReluActivation(),
gate_act=SigmoidActivation(),
state_act=SigmoidActivation(),
bias_attr=std_0,
param_attr=lstm_para_attr)
hidden_para_attr = ParameterAttribute(
initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = lstmemory(
name='lstm0',
input=hidden_0,
act=ReluActivation(),
gate_act=SigmoidActivation(),
state_act=SigmoidActivation(),
bias_attr=std_0,
param_attr=lstm_para_attr)
#stack L-LSTM and R-LSTM with direct edges
input_tmp = [hidden_0, lstm_0]
for i in range(1, depth):
mix_hidden = mixed_layer(name='hidden'+str(i),
size=hidden_dim,
bias_attr=std_default,
input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
]
)
lstm = lstmemory(name='lstm'+str(i),
input=mix_hidden,
act=ReluActivation(),
gate_act=SigmoidActivation(),
state_act=SigmoidActivation(),
reverse=((i % 2)==1),
bias_attr=std_0,
param_attr=lstm_para_attr)
mix_hidden = mixed_layer(
name='hidden' + str(i),
size=hidden_dim,
bias_attr=std_default,
input=[
full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
])
lstm = lstmemory(
name='lstm' + str(i),
input=mix_hidden,
act=ReluActivation(),
gate_act=SigmoidActivation(),
state_act=SigmoidActivation(),
reverse=((i % 2) == 1),
bias_attr=std_0,
param_attr=lstm_para_attr)
input_tmp = [mix_hidden, lstm]
feature_out = mixed_layer(name='output',
size=label_dict_len,
bias_attr=std_default,
input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
],
)
feature_out = mixed_layer(
name='output',
size=label_dict_len,
bias_attr=std_default,
input=[
full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
], )
if not is_predict:
crf_l = crf_layer( name = 'crf',
size = label_dict_len,
input = feature_out,
label = target,
param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr)
)
crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
size = label_dict_len,
input = feature_out,
label = target,
param_attr=ParameterAttribute(name='crfw')
)
crf_l = crf_layer(
name='crf',
size=label_dict_len,
input=feature_out,
label=target,
param_attr=ParameterAttribute(
name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
crf_dec_l = crf_decoding_layer(
name='crf_dec_l',
size=label_dict_len,
input=feature_out,
label=target,
param_attr=ParameterAttribute(name='crfw'))
eval = sum_evaluator(input=crf_dec_l)
outputs(crf_l)
else:
crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
size = label_dict_len,
input = feature_out,
param_attr=ParameterAttribute(name='crfw')
)
crf_dec_l = crf_decoding_layer(
name='crf_dec_l',
size=label_dict_len,
input=feature_out,
param_attr=ParameterAttribute(name='crfw'))
outputs(crf_dec_l)
......@@ -26,7 +26,8 @@ UNK_IDX = 0
class Prediction():
def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file):
def __init__(self, train_conf, dict_file, model_dir, label_file,
predicate_dict_file):
"""
train_conf: trainer configure.
dict_file: word dictionary file name.
......@@ -35,7 +36,7 @@ class Prediction():
self.dict = {}
self.labels = {}
self.predicate_dict={}
self.predicate_dict = {}
self.labels_reverse = {}
self.load_dict_label(dict_file, label_file, predicate_dict_file)
......@@ -44,25 +45,18 @@ class Prediction():
len_pred = len(self.predicate_dict)
conf = parse_config(
train_conf,
'dict_len=' + str(len_dict) +
',label_len=' + str(len_label) +
',pred_len=' + str(len_pred) +
',is_predict=True')
train_conf, 'dict_len=' + str(len_dict) + ',label_len=' +
str(len_label) + ',pred_len=' + str(len_pred) + ',is_predict=True')
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
self.network.loadParameters(model_dir)
slots = [
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_dict),
integer_value_sequence(len_pred),
integer_value_sequence(2)
]
integer_value_sequence(len_dict), integer_value_sequence(len_dict),
integer_value_sequence(len_dict), integer_value_sequence(len_dict),
integer_value_sequence(len_dict), integer_value_sequence(len_dict),
integer_value_sequence(len_pred), integer_value_sequence(2)
]
self.converter = DataProviderConverter(slots)
def load_dict_label(self, dict_file, label_file, predicate_dict_file):
......@@ -78,6 +72,7 @@ class Prediction():
for line_count, line in enumerate(open(predicate_dict_file, 'r')):
self.predicate_dict[line.strip()] = line_count
def get_data(self, data_file):
"""
Get input data of paddle format.
......@@ -88,9 +83,10 @@ class Prediction():
).split('\t')
words = sentence.split()
sen_len = len(words)
word_slot = [self.dict.get(w, UNK_IDX) for w in words]
predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len
predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)
] * sen_len
ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len
......@@ -99,7 +95,7 @@ class Prediction():
marks = mark.split()
mark_slot = [int(w) for w in marks]
yield word_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, predicate_slot, mark_slot
......@@ -123,8 +119,9 @@ class Prediction():
def option_parser():
usage = ("python predict.py -c config -w model_dir "
"-d word dictionary -l label_file -i input_file -p pred_dict_file")
usage = (
"python predict.py -c config -w model_dir "
"-d word dictionary -l label_file -i input_file -p pred_dict_file")
parser = OptionParser(usage="usage: %s [options]" % usage)
parser.add_option(
"-c",
......@@ -187,8 +184,9 @@ def main():
output_file = options.output_file
swig_paddle.initPaddle("--use_gpu=0")
predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file)
predict.predict(data_file,output_file)
predict = Prediction(train_conf, dict_file, model_path, label_file,
predict_dict_file)
predict.predict(data_file, output_file)
if __name__ == '__main__':
......
......@@ -71,9 +71,7 @@ class SentimentPrediction():
transform word into integer index according to the dictionary.
"""
words = data.strip().split()
word_slot = [
self.word_dict[w] for w in words if w in self.word_dict
]
word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
return word_slot
def batch_predict(self, data_batch):
......@@ -85,8 +83,8 @@ class SentimentPrediction():
if self.label is None:
print("predicting label is %d" % (lab[0]))
else:
print("predicting label is %s" %
(self.label[lab[0]]))
print("predicting label is %s" % (self.label[lab[0]]))
def option_parser():
usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
......@@ -143,9 +141,10 @@ def main():
batch.append([predict.get_index(line)])
if len(batch) == batch_size:
predict.batch_predict(batch)
batch=[]
batch = []
if len(batch) > 0:
predict.batch_predict(batch)
if __name__ == '__main__':
main()
......@@ -306,4 +306,4 @@ I1116 09:10:18.019069 50 ParameterClient2.cpp:122] pserver 2 192.168.223.143:
I1116 09:10:18.019492 50 ParameterClient2.cpp:122] pserver 3 192.168.223.143:7165
I1116 09:10:18.019716 50 ParameterClient2.cpp:122] pserver 4 192.168.129.71:7164
I1116 09:10:18.019836 50 ParameterClient2.cpp:122] pserver 5 192.168.129.71:7165
```
\ No newline at end of file
```
......@@ -40,4 +40,4 @@ spec:
- name: jobpath
mountPath: /home/jobpath
restartPolicy: Never
\ No newline at end of file
......@@ -19,7 +19,6 @@ import socket
import os
import argparse
# configuration for cluster
API = "/api/v1/namespaces/"
JOBSELECTOR = "labelSelector=job-name="
......@@ -145,8 +144,8 @@ def startPaddle(idMap={}, train_args_dict=None):
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog="start_paddle.py",
description='simple tool for k8s')
parser = argparse.ArgumentParser(
prog="start_paddle.py", description='simple tool for k8s')
args, train_args_list = parser.parse_known_args()
train_args = refine_unknown_args(train_args_list)
train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2]))
......
情感分析教程
===========================
.. toctree::
:maxdepth: 3
:glob:
情感分析教程
===========================
.. toctree::
:maxdepth: 3
:glob:
Training Locally <sentiment_analysis.md>
\ No newline at end of file
......@@ -28,4 +28,4 @@ $(document).ready(function(){
$('.doc-menu-vertical').find('li.current').last().addClass('active');
$('.doc-menu-vertical').perfectScrollbar();
});
\ No newline at end of file
});
......@@ -15,8 +15,8 @@ limitations under the License. */
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
#include "Internal.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
std::vector<int> GradientMachine::defaultParamTypes = {
PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM};
......
......@@ -16,14 +16,13 @@ limitations under the License. */
#include "PaddleAPI.h"
#include <vector>
#include <algorithm>
#include <vector>
template <typename T1, typename T2>
void staticCastVector(std::vector<T2>* dest, const std::vector<T1>& src) {
dest->resize(src.size());
std::transform(src.begin(),
src.end(),
dest->begin(),
[](T1 t) { return static_cast<T2>(t); });
std::transform(src.begin(), src.end(), dest->begin(), [](T1 t) {
return static_cast<T2>(t);
});
}
......@@ -12,12 +12,12 @@ 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. */
#include "PaddleAPI.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/math/CpuSparseMatrix.h"
#include <iostream>
#include <cstring>
#include <iostream>
#include "PaddleAPI.h"
#include "paddle/math/CpuSparseMatrix.h"
#include "paddle/math/SparseMatrix.h"
struct MatrixPrivate {
std::shared_ptr<paddle::Matrix> mat;
......
......@@ -16,8 +16,8 @@ limitations under the License. */
#include <stddef.h>
#include <stdint.h>
#include <string>
#include <stdexcept>
#include <string>
#include <vector>
#include "paddle/utils/GlobalConstants.h"
#include "paddle/utils/TypeDefs.h"
......
......@@ -12,8 +12,8 @@ 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. */
#include "PaddleAPI.h"
#include "paddle/parameter/Parameter.h"
#include "PaddleAPI.h"
struct ParameterPrivate {
std::shared_ptr<paddle::Parameter> sharedPtr;
......
......@@ -12,11 +12,11 @@ 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. */
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
#include "paddle/parameter/ParameterOptimizer.h"
#include "Internal.h"
#include <algorithm>
#include "Internal.h"
#include "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
struct ParameterOptimizerPrivate {
std::unique_ptr<paddle::ParameterOptimizer> optimizer;
......@@ -36,16 +36,13 @@ struct ParameterTraverseCallbackPrivate {
size_t sparseId) {
std::vector<paddle::VectorPtr> real_vecs;
real_vecs.resize(vecs.size());
std::transform(vecs.begin(),
vecs.end(),
real_vecs.begin(),
[](Vector* v) {
if (v) {
return *(paddle::VectorPtr*)(v->getSharedPtr());
} else {
return paddle::VectorPtr();
}
});
std::transform(vecs.begin(), vecs.end(), real_vecs.begin(), [](Vector* v) {
if (v) {
return *(paddle::VectorPtr*)(v->getSharedPtr());
} else {
return paddle::VectorPtr();
}
});
paddle::ParameterConfig& real_conf =
*(paddle::ParameterConfig*)(const_cast<ParameterConfig&>(conf)
......
......@@ -12,14 +12,14 @@ 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. */
#include <algorithm>
#include <iterator>
#include <sstream>
#include <vector>
#include "PaddleAPI.h"
#include "paddle/gserver/gradientmachines/GradientMachine.h"
#include "paddle/parameter/Argument.h"
#include "paddle/utils/Flags.h"
#include <vector>
#include <sstream>
#include <algorithm>
#include <iterator>
// used to represent partial sequence
struct Path {
......
......@@ -16,12 +16,12 @@ limitations under the License. */
#include "PaddleAPIPrivate.h"
#include <stdlib.h>
#include <memory>
#include <atomic>
#include <memory>
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
#include "paddle/trainer/ParamUtil.h"
#include "paddle/trainer/Trainer.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
#include "paddle/trainer/TrainerInternal.h"
#include "paddle/utils/Flags.h"
......
......@@ -14,16 +14,16 @@ limitations under the License. */
#include "PaddleAPI.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/Excepts.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/utils/Excepts.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Util.h"
#include <fenv.h>
#include <algorithm>
#include <iostream>
#include <iterator>
#include <algorithm>
void initPaddle(int argc, char** argv) {
paddle::initMain(argc, argv);
......
......@@ -282,7 +282,7 @@ FloatArray Vector::getData() const {
}
void Vector::copyFrom(Vector* src) throw(RangeError) {
if (src->m->vec->getSize() != m->vec->getSize()) {
if (src->m->vec->getSize() != m->vec->getSize()) {
throw RangeError();
}
m->vec->copyFrom(*src->m->vec);
......
......@@ -100,11 +100,12 @@ class TestMatrix(unittest.TestCase):
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
def test_numpy(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
self.assertEqual((int(m.getHeight()), int(m.getWidth())), numpy_mat.shape)
self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
......
......@@ -26,17 +26,17 @@ class TestIVector(unittest.TestCase):
self.assertEqual(m[i], 0)
m[i] = i
self.assertEqual(m[i], i)
m = swig_paddle.IVector.createZero(10)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(m.getData(), [0]*10)
self.assertEqual(m.getData(), [0] * 10)
def test_create(self):
m = swig_paddle.IVector.create(range(10), False)
self.assertIsNotNone(m)
for i in xrange(10):
self.assertEqual(m[i], i)
m = swig_paddle.IVector.create(range(10))
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(m.getData(), range(10))
......@@ -69,7 +69,7 @@ class TestIVector(unittest.TestCase):
expect_vec = range(0, 10)
expect_vec[4] = 7
self.assertEqual(vec.getData(), expect_vec)
def test_numpy(self):
vec = np.array([1, 3, 4, 65, 78, 1, 4], dtype="int32")
iv = swig_paddle.IVector.createVectorFromNumpy(vec)
......@@ -85,10 +85,10 @@ class TestVector(unittest.TestCase):
self.assertTrue(util.doubleEqual(v[i], 0))
v[i] = i
self.assertTrue(util.doubleEqual(v[i], i))
v = swig_paddle.Vector.createZero(10)
self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(v.getData(), [0]*10)
self.assertEqual(v.getData(), [0] * 10)
def testCreate(self):
v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)], False)
......@@ -96,14 +96,13 @@ class TestVector(unittest.TestCase):
for i in xrange(len(v)):
self.assertTrue(util.doubleEqual(v[i], i / 100.0))
self.assertEqual(100, len(v))
v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)])
self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu())
self.assertEqual(100, len(v))
vdata = v.getData()
for i in xrange(len(v)):
self.assertTrue(util.doubleEqual(vdata[i], i / 100.0))
def testCpuNumpy(self):
numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
......@@ -128,7 +127,7 @@ class TestVector(unittest.TestCase):
for i in xrange(1, len(numpy_3)):
util.doubleEqual(numpy_3[i], vec[i])
def testNumpy(self):
numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
vec = swig_paddle.Vector.createVectorFromNumpy(numpy_arr)
......@@ -136,7 +135,6 @@ class TestVector(unittest.TestCase):
vecData = vec.getData()
for n, v in zip(numpy_arr, vecData):
self.assertTrue(util.doubleEqual(n, v))
def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1, False)
......
......@@ -223,9 +223,9 @@ typedef struct {
#ifdef __NVCC__
#include "paddle/utils/Logging.h"
#include "hl_cuda.h"
#include "cuda_runtime.h"
#include "hl_cuda.h"
#include "paddle/utils/Logging.h"
extern __thread bool g_sync_flag;
extern __thread cudaStream_t default_stream;
......
......@@ -16,8 +16,8 @@ limitations under the License. */
#define HL_DSO_LOADER_H_
#include <dlfcn.h>
#include <string>
#include <memory>
#include <string>
#include "hl_base.h"
/**
......
......@@ -15,28 +15,28 @@ limitations under the License. */
#ifndef HL_GPU_H_
#define HL_GPU_H_
#include "hl_aggregate.h"
#include "hl_base.h"
#include "hl_cnn.h"
#include "hl_cuda.h"
#include "hl_cuda_cublas.h"
#include "hl_cuda_cudnn.h"
#include "hl_matrix.h"
#include "hl_aggregate.h"
#include "hl_cnn.h"
#include "hl_sparse.h"
#include "hl_lstm.h"
#include "hl_matrix.h"
#include "hl_sequence.h"
#include "hl_sparse.h"
#include "hl_warpctc_wrap.h"
#ifdef HPPL_STUB_FUNC
#include "stub/hl_cuda_stub.h"
#include "stub/hl_cuda_cublas_stub.h"
#include "stub/hl_cuda_cudnn_stub.h"
#include "stub/hl_matrix_stub.h"
#include "stub/hl_aggregate_stub.h"
#include "stub/hl_cnn_stub.h"
#include "stub/hl_sparse_stub.h"
#include "stub/hl_cuda_cublas_stub.h"
#include "stub/hl_cuda_cudnn_stub.h"
#include "stub/hl_cuda_stub.h"
#include "stub/hl_lstm_stub.h"
#include "stub/hl_matrix_stub.h"
#include "stub/hl_sequence_stub.h"
#include "stub/hl_sparse_stub.h"
#endif
#endif /* HL_GPU_H_ */
......@@ -14,7 +14,7 @@ limitations under the License. */
#ifndef HL_TIME_H_
#define HL_TIME_H_
#include <cstdint>
/**
* @brief High resolution timer.
*
......
......@@ -12,12 +12,12 @@ 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. */
#include "hl_cuda_cublas.h"
#include <sys/time.h>
#include <mutex>
#include "hl_cuda.h"
#include "hl_cuda_cublas.h"
#include "hl_thread.ph"
#include "hl_dso_loader.h"
#include "hl_thread.ph"
#include "paddle/utils/Logging.h"
namespace dynload {
......
......@@ -12,14 +12,14 @@ 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. */
#include "hl_cuda_cudnn.h"
#include <cudnn.h>
#include <mutex>
#include "hl_cuda_cudnn.h"
#include "hl_cuda_cudnn.ph"
#include "hl_thread.ph"
#include "hl_dso_loader.h"
#include "paddle/utils/Logging.h"
#include "hl_thread.ph"
#include "paddle/utils/CommandLineParser.h"
#include "paddle/utils/Logging.h"
P_DEFINE_int32(cudnn_conv_workspace_limit_in_mb,
4096,
......
......@@ -12,13 +12,13 @@ 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. */
#include "hl_cuda.h"
#include <cuda_profiler_api.h>
#include <string.h>
#include <sys/syscall.h>
#include <sys/time.h>
#include <unistd.h>
#include <mutex>
#include "hl_cuda.h"
#include "hl_cuda.ph"
#include "hl_dso_loader.h"
#include "hl_thread.ph"
......
......@@ -14,8 +14,8 @@ limitations under the License. */
#ifdef PADDLE_USE_DSO
#include <mutex>
#include <cuda_runtime.h>
#include <mutex>
#include "hl_dso_loader.h"
/**
......
......@@ -12,10 +12,11 @@ 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. */
#include <chrono>
#include "hl_time.h"
#include <stdlib.h>
#include <chrono>
#include <cstdint>
#include <iostream>
#include "hl_time.h"
using std::chrono::high_resolution_clock;
......
......@@ -12,8 +12,8 @@ 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. */
#include <mutex>
#include "hl_warpctc_wrap.h"
#include <mutex>
#include "hl_dso_loader.h"
#include "paddle/utils/Logging.h"
......
......@@ -15,13 +15,13 @@ limitations under the License. */
#include "ActivationFunction.h"
#include <algorithm>
#include <memory>
#include <iostream>
#include <type_traits>
#include <memory>
#include <string>
#include <thread>
#include "paddle/utils/ClassRegistrar.h"
#include <type_traits>
#include "paddle/parameter/Argument.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Logging.h"
......
......@@ -14,12 +14,12 @@ limitations under the License. */
#include "DataProvider.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Logging.h"
#include <algorithm>
#include <unistd.h>
#include <algorithm>
#include "ProtoDataProvider.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
namespace paddle {
......
......@@ -14,28 +14,28 @@ limitations under the License. */
#pragma once
#include <vector>
#include <memory>
#include <mutex>
#include <iostream>
#include <fstream>
#include <stdint.h>
#include <string.h>
#include <stdlib.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <fstream>
#include <iostream>
#include <memory>
#include <mutex>
#include <vector>
#include "DataConfig.pb.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/math/Vector.h"
#include "paddle/parameter/Argument.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Queue.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/ThreadLocal.h"
#include "paddle/utils/TypeDefs.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/utils/Util.h"
#include "paddle/math/Vector.h"
#include "DataConfig.pb.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/parameter/Argument.h"
namespace paddle {
/**
......
......@@ -12,10 +12,10 @@ 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. */
#include "paddle/utils/Util.h"
#include "MultiDataProvider.h"
#include "paddle/utils/Logging.h"
#include <algorithm>
#include "paddle/utils/Logging.h"
#include "paddle/utils/Util.h"
namespace paddle {
......
......@@ -13,14 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "ProtoDataProvider.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/StringUtil.h"
#include <algorithm>
#include <fstream>
#include <istream>
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/Logging.h"
#include "DataProviderGroup.h"
#include "paddle/utils/Logging.h"
P_DEFINE_double(memory_threshold_on_load_data,
1.0,
......@@ -562,16 +562,16 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
auto mat = cpuArguments[slot].value;
mat->resize(size, dim);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
->copyFrom(dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseNonValueData.data(),
HPPL_STREAM_1);
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseNonValueData.data(),
HPPL_STREAM_1);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
->copyFrom(dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseNonValueData.data());
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseNonValueData.data());
} else {
LOG(FATAL) << "Not Supported";
}
......@@ -598,16 +598,16 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
auto mat = cpuArguments[slot].value;
mat->resize(size, dim);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
->copyFrom(dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseFloatValueData.data(),
HPPL_STREAM_1);
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseFloatValueData.data(),
HPPL_STREAM_1);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
->copyFrom(dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseFloatValueData.data());
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
dataPos.data(),
slots_[slot].indices.data(),
slots_[slot].sparseFloatValueData.data());
} else {
LOG(FATAL) << "Not Supported";
}
......
......@@ -16,8 +16,8 @@ limitations under the License. */
#include <vector>
#include "paddle/utils/Stat.h"
#include "DataFormat.pb.h"
#include "paddle/utils/Stat.h"
#include "DataProvider.h"
#include "ProtoReader.h"
......
......@@ -16,10 +16,10 @@ limitations under the License. */
#include <memory>
#include <google/protobuf/message_lite.h>
#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/io/gzip_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/message_lite.h>
namespace paddle {
......
......@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "PyDataProvider.h"
#include "paddle/utils/PythonUtil.h"
#include <fenv.h>
#include "paddle/utils/Util.h"
#include "paddle/utils/Excepts.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Util.h"
namespace paddle {
......@@ -316,16 +316,16 @@ void PyDataProvider::handleSparseNonValueSlot(
auto mat = cpuArguments[slotIndex].value;
mat->resize(slot.sampleNum, dim, slot.sampleNum, NO_VALUE, SPARSE_CSR);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
->copyFrom(slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseNonValueData.data(),
HPPL_STREAM_1);
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseNonValueData.data(),
HPPL_STREAM_1);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
->copyFrom(slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseNonValueData.data());
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseNonValueData.data());
} else {
LOG(FATAL) << "Not Supported";
}
......@@ -347,16 +347,16 @@ void PyDataProvider::handleSparseValueSlot(
auto mat = cpuArguments[slotIndex].value;
mat->resize(slot.sampleNum, dim, slot.sampleNum, FLOAT_VALUE, SPARSE_CSR);
if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
->copyFrom(slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseFloatValueData.data(),
HPPL_STREAM_DEFAULT);
std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseFloatValueData.data(),
HPPL_STREAM_DEFAULT);
} else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
->copyFrom(slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseFloatValueData.data());
std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
slot.sampleSequenceIdVec.data(),
slot.indices.data(),
slot.sparseFloatValueData.data());
} else {
LOG(FATAL) << "Not Supported";
}
......
......@@ -15,18 +15,18 @@ limitations under the License. */
#ifndef PADDLE_NO_PYTHON
#include <Python.h>
#include <numpy/numpyconfig.h>
#include <stdio.h>
#include <stdlib.h>
#include <unordered_set>
#include <list>
#include <numpy/numpyconfig.h>
#include <unordered_set>
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include <numpy/ndarrayobject.h>
#include "DataProvider.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/PythonUtil.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......@@ -400,10 +400,9 @@ private:
if (this->loadThread_) { // wait poolActualSize < poolSize;
std::unique_lock<std::mutex> l(mtx_);
pushCV_.wait(l,
[this, additionalBatchSize] {
return this->poolActualSize_ < poolSize_;
});
pushCV_.wait(l, [this, additionalBatchSize] {
return this->poolActualSize_ < poolSize_;
});
}
{
......@@ -529,12 +528,10 @@ public:
// but, loading from cache, cache object should ensure
// data pool ready.
std::unique_lock<std::mutex> l(mtx_);
pullCV_.wait(l,
[this, &size] {
return this->poolActualSize_ >=
std::max(size, this->minPoolSize_) ||
callingContexts_.empty();
});
pullCV_.wait(l, [this, &size] {
return this->poolActualSize_ >= std::max(size, this->minPoolSize_) ||
callingContexts_.empty();
});
if (unittest::OnPoolFilled) {
(*unittest::OnPoolFilled)(this->poolActualSize_);
......
......@@ -12,8 +12,8 @@ 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. */
#include "paddle/utils/Stat.h"
#include "paddle/gserver/evaluators/Evaluator.h"
#include "paddle/utils/Stat.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
......@@ -842,9 +842,9 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) {
auto start = predictArray.begin();
while (start != predictArray.end()) {
auto end = std::find_if(
start + 1,
predictArray.end(),
[=](const PredictionResult& x) { return x.queryid != start->queryid; });
start + 1, predictArray.end(), [=](const PredictionResult& x) {
return x.queryid != start->queryid;
});
CHECK(end != start);
stat(start - predictArray.begin(),
end - predictArray.begin(),
......
......@@ -14,11 +14,11 @@ limitations under the License. */
#pragma once
#include "paddle/pserver/ParameterClient2.h"
#include "paddle/utils/ClassRegistrar.h"
#include <fstream>
#include "ModelConfig.pb.h"
#include "paddle/parameter/Argument.h"
#include <fstream>
#include "paddle/pserver/ParameterClient2.h"
#include "paddle/utils/ClassRegistrar.h"
namespace paddle {
......
......@@ -14,16 +14,16 @@ limitations under the License. */
#include "GradientMachine.h"
#include "paddle/utils/Logging.h"
#include <fstream>
#include "paddle/utils/Logging.h"
#include "hl_gpu.h"
#include "NeuralNetwork.h"
#include "ParallelNeuralNetwork.h"
#include "GradientMachineMode.h"
#include "MultiGradientMachine.h"
#include "NeuralNetwork.h"
#include "MultiNetwork.h"
#include "GradientMachineMode.h"
#include "NeuralNetwork.h"
#include "NeuralNetwork.h"
#include "ParallelNeuralNetwork.h"
#include "hl_gpu.h"
namespace paddle {
......
......@@ -17,15 +17,15 @@ limitations under the License. */
#include <iostream>
#include <vector>
#include "paddle/math/Matrix.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/parameter/ParameterUpdaterBase.h"
#include "paddle/utils/Thread.h"
#include "TrainerConfig.pb.h"
#include "ModelConfig.pb.h"
#include "TrainerConfig.pb.h"
#include "paddle/gserver/dataproviders/DataProvider.h"
#include "paddle/gserver/evaluators/Evaluator.h"
#include "paddle/gserver/layers/Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/parameter/ParameterUpdaterBase.h"
#include "paddle/utils/Thread.h"
namespace paddle {
/**
......
......@@ -18,9 +18,9 @@ limitations under the License. */
#include "GradientMachine.h"
#include "paddle/utils/Queue.h"
#include "paddle/utils/Locks.h"
#include "hl_gpu.h"
#include "paddle/utils/Locks.h"
#include "paddle/utils/Queue.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include <algorithm>
#include "paddle/utils/Stat.h"
#include "paddle/utils/Util.h"
#include <algorithm>
#include "MultiNetwork.h"
......
......@@ -14,15 +14,15 @@ limitations under the License. */
#include "paddle/utils/Util.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/CustomStackTrace.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "hl_gpu.h"
#include "MultiNetwork.h"
#include "NeuralNetwork.h"
#include "RecurrentGradientMachine.h"
#include "MultiNetwork.h"
#include "hl_gpu.h"
#include "paddle/gserver/layers/AgentLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
void parameterInitNN(int paramId,
......
......@@ -14,18 +14,18 @@ limitations under the License. */
#pragma once
#include <memory>
#include <map>
#include <functional>
#include <map>
#include <memory>
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/parameter/Parameter.h"
#include "ModelConfig.pb.h"
#include "paddle/gserver/dataproviders/DataProvider.h"
#include "paddle/gserver/gradientmachines/GradientMachine.h"
#include "paddle/gserver/layers/CostLayer.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/gserver/dataproviders/DataProvider.h"
#include "paddle/gserver/layers/Layer.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/utils/ClassRegistrar.h"
namespace paddle {
/*
......@@ -57,14 +57,13 @@ void parameterInitNN(int paramId,
class NeuralNetwork : public GradientMachine {
public:
virtual void init(
const ModelConfig& config,
ParamInitCallback callback = nullptr,
const std::vector<ParameterType>&
parameterTypes = std::vector<ParameterType>{PARAMETER_VALUE,
PARAMETER_GRADIENT,
PARAMETER_MOMENTUM},
bool useGpu = FLAGS_use_gpu);
virtual void init(const ModelConfig& config,
ParamInitCallback callback = nullptr,
const std::vector<ParameterType>& parameterTypes =
std::vector<ParameterType>{PARAMETER_VALUE,
PARAMETER_GRADIENT,
PARAMETER_MOMENTUM},
bool useGpu = FLAGS_use_gpu);
/**
* Connect two submodels and
......
......@@ -37,14 +37,13 @@ public:
NeuralNetwork *rootNetwork = nullptr)
: NeuralNetwork(subModelName, rootNetwork) {}
virtual void init(
const ModelConfig &config,
ParamInitCallback callback = nullptr,
const std::vector<ParameterType>
&parameterTypes = std::vector<ParameterType>{PARAMETER_VALUE,
PARAMETER_GRADIENT,
PARAMETER_MOMENTUM},
bool useGpu = FLAGS_use_gpu);
virtual void init(const ModelConfig &config,
ParamInitCallback callback = nullptr,
const std::vector<ParameterType> &parameterTypes =
std::vector<ParameterType>{PARAMETER_VALUE,
PARAMETER_GRADIENT,
PARAMETER_MOMENTUM},
bool useGpu = FLAGS_use_gpu);
virtual void forward(const std::vector<Argument> &inArgs,
std::vector<Argument> *outArgs,
......
......@@ -12,17 +12,17 @@ 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. */
#include "paddle/utils/Stat.h"
#include "paddle/utils/Util.h"
#include "paddle/utils/Flags.h"
#include "RecurrentGradientMachine.h"
#include <dlfcn.h>
#include <algorithm>
#include <cmath>
#include <functional>
#include <dlfcn.h>
#include <limits>
#include <cmath>
#include "RecurrentGradientMachine.h"
#include "NeuralNetwork.h"
#include "paddle/gserver/layers/AgentLayer.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/Stat.h"
#include "paddle/utils/Util.h"
P_DEFINE_string(diy_beam_search_prob_so, "", "the diy beam search cost so");
......@@ -78,20 +78,22 @@ static inline SymbolType loadDiySymbol(const char* symbolName) {
return reinterpret_cast<SymbolType>(sym);
}
static InitFunction __init__diy_prob_method([] {
std::string soName = FLAGS_diy_beam_search_prob_so;
if (!soName.empty()) {
gDiyProbHandle = dlopen(soName.c_str(), RTLD_LAZY);
CHECK(gDiyProbHandle) << "Cannot Open DIY Prob So " << soName;
atexit(exit_diy_prob);
gDiyProbMethod =
loadDiySymbol<decltype(gDiyProbMethod)>(DIY_CALC_PROB_SYMBOL_NAME);
gDiyProbStart =
loadDiySymbol<decltype(gDiyProbStart)>(DIY_START_CALC_PROB_SYMBOL_NAME);
gDiyProbStop =
loadDiySymbol<decltype(gDiyProbStop)>(DIY_FINISH_CALC_PROB_SYMBOL_NAME);
}
}, std::numeric_limits<int>::max());
static InitFunction __init__diy_prob_method(
[] {
std::string soName = FLAGS_diy_beam_search_prob_so;
if (!soName.empty()) {
gDiyProbHandle = dlopen(soName.c_str(), RTLD_LAZY);
CHECK(gDiyProbHandle) << "Cannot Open DIY Prob So " << soName;
atexit(exit_diy_prob);
gDiyProbMethod =
loadDiySymbol<decltype(gDiyProbMethod)>(DIY_CALC_PROB_SYMBOL_NAME);
gDiyProbStart = loadDiySymbol<decltype(gDiyProbStart)>(
DIY_START_CALC_PROB_SYMBOL_NAME);
gDiyProbStop = loadDiySymbol<decltype(gDiyProbStop)>(
DIY_FINISH_CALC_PROB_SYMBOL_NAME);
}
},
std::numeric_limits<int>::max());
class BeamSearchControlCallbacks {
public:
......@@ -1281,10 +1283,9 @@ void RecurrentGradientMachine::beamSearch(size_t batchSize) {
std::vector<std::vector<int>*> prefixes;
prefixes.resize(paths.size());
std::transform(
paths.begin(),
paths.end(),
prefixes.begin(),
[](const Path& p) { return const_cast<std::vector<int>*>(&p.ids); });
paths.begin(), paths.end(), prefixes.begin(), [](const Path& p) {
return const_cast<std::vector<int>*>(&p.ids);
});
beamSearchCtrlCallbacks_->beamSearchCandidateAdjust(
prefixes, frames_[machineCur].get(), i);
}
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include <functional>
#include "GradientMachine.h"
#include "NeuralNetwork.h"
#include <functional>
#include "paddle/utils/Locks.h"
......
......@@ -12,10 +12,10 @@ 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. */
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "BatchNormBaseLayer.h"
#include "BatchNormalizationLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
#ifndef PADDLE_ONLY_CPU
#include "CudnnBatchNormLayer.h"
#endif
......
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include "Layer.h"
#include "BatchNormBaseLayer.h"
#include "Layer.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "Projection.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include "paddle/utils/Stat.h"
#include "ContextProjection.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Logging.h"
#include "ConvBaseLayer.h"
#include "paddle/math/MathUtils.h"
#include "paddle/utils/Logging.h"
namespace paddle {
bool ConvBaseLayer::init(const LayerMap& layerMap,
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/math/Matrix.h"
#include "paddle/math/MathUtils.h"
#include "Operator.h"
#include "paddle/math/MathUtils.h"
#include "paddle/math/Matrix.h"
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include "paddle/utils/Stat.h"
#include "ConvProjection.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Logging.h"
#include "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Logging.h"
#include "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Logging.h"
#include "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,11 +12,11 @@ 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. */
#include <memory>
#include "CostLayer.h"
#include <algorithm>
#include "paddle/utils/Logging.h"
#include <cmath>
#include "CostLayer.h"
#include <memory>
#include "paddle/utils/Logging.h"
#include "paddle/math/SparseMatrix.h"
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "CudnnBatchNormLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include "paddle/utils/Stat.h"
#include "Layer.h"
#include "BatchNormBaseLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "CudnnConvLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "CudnnConvLayer.h"
namespace paddle {
......
......@@ -14,10 +14,10 @@ limitations under the License. */
#pragma once
#include <vector>
#include "ConvBaseLayer.h"
#include "paddle/math/Matrix.h"
#include "Projection.h"
#include <vector>
#include "paddle/math/Matrix.h"
namespace paddle {
......
......@@ -12,10 +12,10 @@ 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. */
#include "CudnnPoolLayer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "paddle/math/Matrix.h"
#include "CudnnPoolLayer.h"
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include "paddle/utils/Logging.h"
#include "Layer.h"
#include "paddle/utils/Logging.h"
namespace paddle {
/**
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include <vector>
#include "ConvBaseLayer.h"
#include "paddle/math/Matrix.h"
#include <vector>
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "ExpandConvLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "ExpandConvLayer.h"
namespace paddle {
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include "paddle/math/Matrix.h"
#include <vector>
#include "ExpandConvBaseLayer.h"
#include "paddle/math/Matrix.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "ExpandConvTransLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "ExpandConvTransLayer.h"
/* The implementation of the convTransLayer is basically a swap of forward and
* backward of the original convLayer.
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include "paddle/math/Matrix.h"
#include <vector>
#include "ExpandConvBaseLayer.h"
#include "paddle/math/Matrix.h"
namespace paddle {
......
......@@ -13,11 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "FullyConnectedLayer.h"
#include <algorithm>
#include <vector>
#include "paddle/math/SparseMatrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "paddle/math/SparseMatrix.h"
#include <vector>
#include <algorithm>
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include "Layer.h"
#include "GatedRecurrentLayer.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......@@ -386,8 +386,9 @@ void GatedRecurrentLayer::backwardBatch(int batchSize, MatrixPtr inputGrad) {
{
batchSize = outputGradTmp->getHeight();
gruValue.prevOutValue =
(n == 0 ? nullptr : (batchValue_->getBatchValue(n - 1, batchSize))
->getData());
(n == 0
? nullptr
: (batchValue_->getBatchValue(n - 1, batchSize))->getData());
gruGrad.prevOutGrad =
(n == 0 ? nullptr
: (batchGrad_->getBatchValue(n - 1, batchSize))->getData());
......
......@@ -14,10 +14,10 @@ limitations under the License. */
#pragma once
#include "paddle/math/Matrix.h"
#include "SequenceToBatch.h"
#include "GruCompute.h"
#include "Layer.h"
#include "SequenceToBatch.h"
#include "paddle/math/Matrix.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Util.h"
#include "GruCompute.h"
#include "hl_recurrent_apply.cuh"
#include "paddle/utils/Util.h"
namespace paddle {
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include "paddle/utils/TypeDefs.h"
#include "ModelConfig.pb.h"
#include "hl_gpu.h"
#include "paddle/utils/TypeDefs.h"
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include "Layer.h"
#include "GruCompute.h"
#include "Layer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include "paddle/utils/Stat.h"
#include "Projection.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Logging.h"
#include "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
......
......@@ -14,15 +14,15 @@ limitations under the License. */
#include "paddle/utils/Util.h"
#include "paddle/utils/Logging.h"
#include "paddle/math/SparseMatrix.h"
#include "paddle/utils/Logging.h"
#include "AddtoLayer.h"
#include "CRFLayer.h"
#include "CosSimLayer.h"
#include "CostLayer.h"
#include "ExpandConvLayer.h"
#include "CRFLayer.h"
#include "DataLayer.h"
#include "ExpandConvLayer.h"
#include "FullyConnectedLayer.h"
#include "HierarchicalSigmoidLayer.h"
#include "MaxLayer.h"
......
......@@ -14,18 +14,18 @@ limitations under the License. */
#pragma once
#include <memory>
#include <functional>
#include <paddle/parameter/Argument.h>
#include "paddle/utils/ClassRegistrar.h"
#include <functional>
#include <memory>
#include "ModelConfig.pb.h"
#include "paddle/math/CpuSparseMatrix.h"
#include "paddle/parameter/Parameter.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Util.h"
#include "ModelConfig.pb.h"
#include "paddle/gserver/activations/ActivationFunction.h"
#include <paddle/parameter/ParallelParameter.h>
#include <paddle/parameter/Weight.h>
#include "paddle/gserver/activations/ActivationFunction.h"
/// Macro for registering a layer type.
/// Example: REGISTER_LAYER(crf_error, CRFDecodingErrorLayer);
......
......@@ -12,8 +12,8 @@ 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. */
#include <algorithm>
#include "LinearChainCRF.h"
#include <algorithm>
namespace paddle {
......
......@@ -12,8 +12,8 @@ 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. */
#include <math.h>
#include "LinearChainCTC.h"
#include <math.h>
#include <limits>
namespace paddle {
......
......@@ -12,9 +12,9 @@ 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. */
#include "paddle/utils/Util.h"
#include "hl_recurrent_apply.cuh"
#include "LstmCompute.h"
#include "hl_recurrent_apply.cuh"
#include "paddle/utils/Util.h"
namespace paddle {
......
......@@ -14,9 +14,9 @@ limitations under the License. */
#pragma once
#include "paddle/utils/TypeDefs.h"
#include "ModelConfig.pb.h"
#include "hl_gpu.h"
#include "paddle/utils/TypeDefs.h"
namespace paddle {
......
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