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提交 b15b2637 编写于 作者: T Tao Luo 提交者： GitHub
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Merge pull request #2375 from luotao1/v1_demo 

remove duplicated examples, and rename demo to v1_api_demo
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demo/image_classification/.gitignore 已删除 100644 → 0
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data/cifar-10-batches-py
data/cifar-out
cifar_vgg_model/*
plot.png
train.log
image_provider_copy_1.py
*pyc
train.list
test.list
demo/image_classification/api_v2_resnet.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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 paddle.v2 as paddle
__all__ = ['resnet_cifar10']
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
active_type=paddle.activation.Relu(),
ch_in=None):
tmp = paddle.layer.img_conv(
input=input,
filter_size=filter_size,
num_channels=ch_in,
num_filters=ch_out,
stride=stride,
padding=padding,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.batch_norm(input=tmp, act=active_type)
def shortcut(ipt, n_in, n_out, stride):
if n_in != n_out:
return conv_bn_layer(ipt, n_out, 1, stride, 0,
paddle.activation.Linear())
else:
return ipt
def basicblock(ipt, ch_out, stride):
ch_in = ch_out * 2
tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear())
short = shortcut(ipt, ch_in, ch_out, stride)
return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu())
def layer_warp(block_func, ipt, features, count, stride):
tmp = block_func(ipt, features, stride)
for i in range(1, count):
tmp = block_func(tmp, features, 1)
return tmp
def resnet_cifar10(ipt, depth=32):
# depth should be one of 20, 32, 44, 56, 110, 1202
assert (depth - 2) % 6 == 0
n = (depth - 2) / 6
nStages = {16, 64, 128}
conv1 = conv_bn_layer(
ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, n, 1)
res2 = layer_warp(basicblock, res1, 32, n, 2)
res3 = layer_warp(basicblock, res2, 64, n, 2)
pool = paddle.layer.img_pool(
input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
return pool
demo/image_classification/api_v2_train.py 已删除 100644 → 0
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# 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 sys
import paddle.v2 as paddle
from api_v2_vgg import vgg_bn_drop
def main():
datadim = 3 * 32 * 32
classdim = 10
# PaddlePaddle init
paddle.init(use_gpu=False, trainer_count=1)
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(datadim))
# Add neural network config
# option 1. resnet
# net = resnet_cifar10(image, depth=32)
# option 2. vgg
net = vgg_bn_drop(image)
out = paddle.layer.fc(input=net,
size=classdim,
act=paddle.activation.Softmax())
lbl = paddle.layer.data(
name="label", type=paddle.data_type.integer_value(classdim))
cost = paddle.layer.classification_cost(input=out, label=lbl)
# Create parameters
parameters = paddle.parameters.create(cost)
# Create optimizer
momentum_optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
learning_rate=0.1 / 128.0,
learning_rate_decay_a=0.1,
learning_rate_decay_b=50000 * 100,
learning_rate_schedule='discexp',
batch_size=128)
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.batch(
paddle.dataset.cifar.test10(), batch_size=128),
feeding={'image': 0,
'label': 1})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# Create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=momentum_optimizer)
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=50000),
batch_size=128),
num_passes=5,
event_handler=event_handler,
feeding={'image': 0,
'label': 1})
if __name__ == '__main__':
main()
demo/image_classification/api_v2_vgg.py 已删除 100644 → 0
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# 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 paddle.v2 as paddle
__all__ = ['vgg_bn_drop']
def vgg_bn_drop(input):
def conv_block(ipt, num_filter, groups, dropouts, num_channels=None):
return paddle.networks.img_conv_group(
input=ipt,
num_channels=num_channels,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * groups,
conv_filter_size=3,
conv_act=paddle.activation.Relu(),
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type=paddle.pooling.Max())
conv1 = conv_block(input, 64, 2, [0.3, 0], 3)
conv2 = conv_block(conv1, 128, 2, [0.4, 0])
conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = paddle.layer.dropout(input=conv5, dropout_rate=0.5)
fc1 = paddle.layer.fc(input=drop, size=512, act=paddle.activation.Linear())
bn = paddle.layer.batch_norm(
input=fc1,
act=paddle.activation.Relu(),
layer_attr=paddle.attr.Extra(drop_rate=0.5))
fc2 = paddle.layer.fc(input=bn, size=512, act=paddle.activation.Linear())
return fc2
demo/image_classification/data/download_cifar.sh 已删除 100755 → 0
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#!/bin/bash
# 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.
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
rm -rf cifar-out/*
echo Converting CIFAR data to images.....
python process_cifar.py ./cifar-10-batches-py ./cifar-out
demo/image_classification/data/process_cifar.py 已删除 100644 → 0
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# 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
import sys
import os
import PIL.Image as Image
"""
Usage: python process_cifar input_dir output_dir
"""
def mkdir_not_exist(path):
"""
Make dir if the path does not exist.
path: the path to be created.
"""
if not os.path.exists(path):
os.mkdir(path)
def create_dir_structure(output_dir):
"""
Create the directory structure for the directory.
output_dir: the direcotry structure path.
"""
mkdir_not_exist(os.path.join(output_dir))
mkdir_not_exist(os.path.join(output_dir, "train"))
mkdir_not_exist(os.path.join(output_dir, "test"))
def convert_batch(batch_path, label_set, label_map, output_dir, data_split):
"""
Convert CIFAR batch to the structure of Paddle format.
batch_path: the batch to be converted.
label_set: the set of labels.
output_dir: the output path.
data_split: whether it is training or testing data.
"""
data = np.load(batch_path)
for data, label, filename in zip(data['data'], data['labels'],
data['filenames']):
data = data.reshape((3, 32, 32))
data = np.transpose(data, (1, 2, 0))
label = label_map[label]
output_dir_this = os.path.join(output_dir, data_split, str(label))
output_filename = os.path.join(output_dir_this, filename)
if not label in label_set:
label_set[label] = True
mkdir_not_exist(output_dir_this)
Image.fromarray(data).save(output_filename)
if __name__ == '__main__':
input_dir = sys.argv[1]
output_dir = sys.argv[2]
num_batch = 5
create_dir_structure(output_dir)
label_map = {
0: "airplane",
1: "automobile",
2: "bird",
3: "cat",
4: "deer",
5: "dog",
6: "frog",
7: "horse",
8: "ship",
9: "truck"
}
labels = {}
for i in range(1, num_batch + 1):
convert_batch(
os.path.join(input_dir, "data_batch_%d" % i), labels, label_map,
output_dir, "train")
convert_batch(
os.path.join(input_dir, "test_batch"), {}, label_map, output_dir,
"test")
demo/image_classification/image_provider.py 已删除 100644 → 0
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# 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 io
import random
import paddle.utils.image_util as image_util
from paddle.trainer.PyDataProvider2 import *
#
# {'img_size': 32,
# 'settings': a global object,
# 'color': True,
# 'mean_img_size': 32,
# 'meta': './data/cifar-out/batches/batches.meta',
# 'num_classes': 10,
# 'file_list': ('./data/cifar-out/batches/train_batch_000',),
# 'use_jpeg': True}
def hook(settings, img_size, mean_img_size, num_classes, color, meta, use_jpeg,
is_train, **kwargs):
settings.mean_img_size = mean_img_size
settings.img_size = img_size
settings.num_classes = num_classes
settings.color = color
settings.is_train = is_train
if settings.color:
settings.img_raw_size = settings.img_size * settings.img_size * 3
else:
settings.img_raw_size = settings.img_size * settings.img_size
settings.meta_path = meta
settings.use_jpeg = use_jpeg
settings.img_mean = image_util.load_meta(settings.meta_path,
settings.mean_img_size,
settings.img_size, settings.color)
settings.logger.info('Image size: %s', settings.img_size)
settings.logger.info('Meta path: %s', settings.meta_path)
settings.input_types = {
'image': dense_vector(settings.img_raw_size),
'label': integer_value(settings.num_classes)
}
settings.logger.info('DataProvider Initialization finished')
@provider(init_hook=hook, min_pool_size=0)
def processData(settings, file_list):
"""
The main function for loading data.
Load the batch, iterate all the images and labels in this batch.
file_list: the batch file list.
"""
with open(file_list, 'r') as fdata:
lines = [line.strip() for line in fdata]
random.shuffle(lines)
for file_name in lines:
with io.open(file_name.strip(), 'rb') as file:
data = cPickle.load(file)
indexes = list(range(len(data['images'])))
if settings.is_train:
random.shuffle(indexes)
for i in indexes:
if settings.use_jpeg == 1:
img = image_util.decode_jpeg(data['images'][i])
else:
img = data['images'][i]
img_feat = image_util.preprocess_img(
img, settings.img_mean, settings.img_size,
settings.is_train, settings.color)
label = data['labels'][i]
yield {
'image': img_feat.astype('float32'),
'label': int(label)
}
demo/image_classification/image_util.py 已删除 100644 → 0
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# 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
from PIL import Image
from cStringIO import StringIO
def resize_image(img, target_size):
"""
Resize an image so that the shorter edge has length target_size.
img: the input image to be resized.
target_size: the target resized image size.
"""
percent = (target_size / float(min(img.size[0], img.size[1])))
resized_size = int(round(img.size[0] * percent)), int(
round(img.size[1] * percent))
img = img.resize(resized_size, Image.ANTIALIAS)
return img
def flip(im):
"""
Return the flipped image.
Flip an image along the horizontal direction.
im: input image, (H x W x K) ndarrays
"""
if len(im.shape) == 3:
return im[:, :, ::-1]
else:
return im[:, ::-1]
def crop_img(im, inner_size, color=True, test=True):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
im: (K x H x W) ndarrays
inner_size: the cropped image size.
color: whether it is color image.
test: whether in test mode.
If False, does random cropping and flipping.
If True, crop the center of images.
"""
if color:
height, width = max(inner_size, im.shape[1]), max(inner_size,
im.shape[2])
padded_im = np.zeros((3, height, width))
startY = (height - im.shape[1]) / 2
startX = (width - im.shape[2]) / 2
endY, endX = startY + im.shape[1], startX + im.shape[2]
padded_im[:, startY:endY, startX:endX] = im
else:
im = im.astype('float32')
height, width = max(inner_size, im.shape[0]), max(inner_size,
im.shape[1])
padded_im = np.zeros((height, width))
startY = (height - im.shape[0]) / 2
startX = (width - im.shape[1]) / 2
endY, endX = startY + im.shape[0], startX + im.shape[1]
padded_im[startY:endY, startX:endX] = im
if test:
startY = (height - inner_size) / 2
startX = (width - inner_size) / 2
else:
startY = np.random.randint(0, height - inner_size + 1)
startX = np.random.randint(0, width - inner_size + 1)
endY, endX = startY + inner_size, startX + inner_size
if color:
pic = padded_im[:, startY:endY, startX:endX]
else:
pic = padded_im[startY:endY, startX:endX]
if (not test) and (np.random.randint(2) == 0):
pic = flip(pic)
return pic
def decode_jpeg(jpeg_string):
np_array = np.array(Image.open(StringIO(jpeg_string)))
if len(np_array.shape) == 3:
np_array = np.transpose(np_array, (2, 0, 1))
return np_array
def preprocess_img(im, img_mean, crop_size, is_train, color=True):
"""
Does data augmentation for images.
If is_train is false, cropping the center region from the image.
If is_train is true, randomly crop a region from the image,
and randomy does flipping.
im: (K x H x W) ndarrays
"""
im = im.astype('float32')
test = not is_train
pic = crop_img(im, crop_size, color, test)
pic -= img_mean
return pic.flatten()
def load_meta(meta_path, mean_img_size, crop_size, color=True):
"""
Return the loaded meta file.
Load the meta image, which is the mean of the images in the dataset.
The mean image is subtracted from every input image so that the expected mean
of each input image is zero.
"""
mean = np.load(meta_path)['data_mean']
border = (mean_img_size - crop_size) / 2
if color:
assert (mean_img_size * mean_img_size * 3 == mean.shape[0])
mean = mean.reshape(3, mean_img_size, mean_img_size)
mean = mean[:, border:border + crop_size, border:border +
crop_size].astype('float32')
else:
assert (mean_img_size * mean_img_size == mean.shape[0])
mean = mean.reshape(mean_img_size, mean_img_size)
mean = mean[border:border + crop_size, border:border +
crop_size].astype('float32')
return mean
def load_image(img_path, is_color=True):
"""
Load image and return.
img_path: image path.
is_color: is color image or not.
"""
img = Image.open(img_path)
img.load()
return img
def oversample(img, crop_dims):
"""
image : iterable of (H x W x K) ndarrays
crop_dims: (height, width) tuple for the crops.
Returned data contains ten crops of input image, namely,
four corner patches and the center patch as well as their
horizontal reflections.
"""
# Dimensions and center.
im_shape = np.array(img[0].shape)
crop_dims = np.array(crop_dims)
im_center = im_shape[:2] / 2.0
# Make crop coordinates
h_indices = (0, im_shape[0] - crop_dims[0])
w_indices = (0, im_shape[1] - crop_dims[1])
crops_ix = np.empty((5, 4), dtype=int)
curr = 0
for i in h_indices:
for j in w_indices:
crops_ix[curr] = (i, j, i + crop_dims[0], j + crop_dims[1])
curr += 1
crops_ix[4] = np.tile(im_center, (1, 2)) + np.concatenate(
[-crop_dims / 2.0, crop_dims / 2.0])
crops_ix = np.tile(crops_ix, (2, 1))
# Extract crops
crops = np.empty(
(10 * len(img), crop_dims[0], crop_dims[1], im_shape[-1]),
dtype=np.float32)
ix = 0
for im in img:
for crop in crops_ix:
crops[ix] = im[crop[0]:crop[2], crop[1]:crop[3], :]
ix += 1
crops[ix - 5:ix] = crops[ix - 5:ix, :, ::-1, :] # flip for mirrors
return crops
class ImageTransformer:
def __init__(self,
transpose=None,
channel_swap=None,
mean=None,
is_color=True):
self.transpose = transpose
self.channel_swap = None
self.mean = None
self.is_color = is_color
def set_transpose(self, order):
if self.is_color:
assert 3 == len(order)
self.transpose = order
def set_channel_swap(self, order):
if self.is_color:
assert 3 == len(order)
self.channel_swap = order
def set_mean(self, mean):
# mean value, may be one value per channel
if mean.ndim == 1:
mean = mean[:, np.newaxis, np.newaxis]
else:
# elementwise mean
if self.is_color:
assert len(mean.shape) == 3
self.mean = mean
def transformer(self, data):
if self.transpose is not None:
data = data.transpose(self.transpose)
if self.channel_swap is not None:
data = data[self.channel_swap, :, :]
if self.mean is not None:
data -= self.mean
return data
demo/image_classification/predict.sh 已删除 100755 → 0
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#!/bin/bash
# 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.
set -e
model=cifar_vgg_model/pass-00299/
image=data/cifar-out/test/airplane/seaplane_s_000978.png
use_gpu=1
python prediction.py $model $image $use_gpu
demo/image_classification/prediction.py 已删除 100755 → 0
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# 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 os, sys
import numpy as np
import logging
from PIL import Image
from optparse import OptionParser
import paddle.utils.image_util as image_util
from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import dense_vector
from paddle.trainer.config_parser import parse_config
logging.basicConfig(
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
logging.getLogger().setLevel(logging.INFO)
class ImageClassifier():
def __init__(self,
train_conf,
use_gpu=True,
model_dir=None,
resize_dim=None,
crop_dim=None,
mean_file=None,
oversample=False,
is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
gpu = 1 if use_gpu else 0
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (gpu))
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [dense_vector(data_size)]
self.converter = DataProviderConverter(slots)
def get_data(self, img_path):
"""
1. load image from img_path.
2. resize or oversampling.
3. transformer data: transpose, sub mean.
return K x H x W ndarray.
img_path: image path.
"""
image = image_util.load_image(img_path, self.is_color)
if self.oversample:
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros(
(1, image.shape[0], image.shape[1], 3), dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros(
(1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
for img in input:
img = self.transformer.transformer(img).flatten()
data_in.append([img.tolist()])
return data_in
def forward(self, input_data):
in_arg = self.converter(input_data)
return self.network.forwardTest(in_arg)
def forward(self, data, output_layer):
"""
input_data: py_paddle input data.
output_layer: specify the name of probability, namely the layer with
softmax activation.
return: the predicting probability of each label.
"""
input = self.converter(data)
self.network.forwardTest(input)
output = self.network.getLayerOutputs(output_layer)
# For oversampling, average predictions across crops.
# If not, the shape of output[name]: (1, class_number),
# the mean is also applicable.
return output[output_layer]['value'].mean(0)
def predict(self, image=None, output_layer=None):
assert isinstance(image, basestring)
assert isinstance(output_layer, basestring)
data = self.get_data(image)
prob = self.forward(data, output_layer)
lab = np.argsort(-prob)
logging.info("Label of %s is: %d", image, lab[0])
if __name__ == '__main__':
image_size = 32
crop_size = 32
multi_crop = True
config = "vgg_16_cifar.py"
output_layer = "__fc_layer_1__"
mean_path = "data/cifar-out/batches/batches.meta"
model_path = sys.argv[1]
image = sys.argv[2]
use_gpu = bool(int(sys.argv[3]))
obj = ImageClassifier(
train_conf=config,
model_dir=model_path,
resize_dim=image_size,
crop_dim=crop_size,
mean_file=mean_path,
use_gpu=use_gpu,
oversample=multi_crop)
obj.predict(image, output_layer)
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# 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.utils.preprocess_img import ImageClassificationDatasetCreater
from optparse import OptionParser
def option_parser():
parser = OptionParser(usage="usage: python preprcoess.py "\
"-i data_dir [options]")
parser.add_option(
"-i",
"--input",
action="store",
dest="input",
help="Input data directory.")
parser.add_option(
"-s",
"--size",
action="store",
dest="size",
help="Processed image size.")
parser.add_option(
"-c",
"--color",
action="store",
dest="color",
help="whether to use color images.")
return parser.parse_args()
if __name__ == '__main__':
options, args = option_parser()
data_dir = options.input
processed_image_size = int(options.size)
color = options.color == "1"
data_creator = ImageClassificationDatasetCreater(
data_dir, processed_image_size, color)
data_creator.train_list_name = "train.txt"
data_creator.test_list_name = "test.txt"
data_creator.num_per_batch = 1000
data_creator.overwrite = True
data_creator.create_batches()
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#!/bin/bash
# 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.
set -e
data_dir=./data/cifar-out
python preprocess.py -i $data_dir -s 32 -c 1
echo "data/cifar-out/batches/train.txt" > train.list
echo "data/cifar-out/batches/test.txt" > test.list
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#!/bin/bash
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
config=vgg_16_cifar.py
output=./cifar_vgg_model
log=train.log
paddle train \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=1 \
--trainer_count=1 \
--num_passes=300 \
--save_dir=$output \
2>&1 | tee $log
paddle usage -l $log -e $? -n "image_classification_train" >/dev/null 2>&1
python -m paddle.utils.plotcurve -i $log > plot.png
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# 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 *
is_predict = get_config_arg("is_predict", bool, False)
####################Data Configuration ##################
if not is_predict:
data_dir = 'data/cifar-out/batches/'
meta_path = data_dir + 'batches.meta'
args = {
'meta': meta_path,
'mean_img_size': 32,
'img_size': 32,
'num_classes': 10,
'use_jpeg': 1,
'color': "color"
}
define_py_data_sources2(
train_list="train.list",
test_list="train.list",
module='image_provider',
obj='processData',
args=args)
######################Algorithm Configuration #############
settings(
batch_size=128,
learning_rate=0.1 / 128.0,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * 128))
#######################Network Configuration #############
data_size = 3 * 32 * 32
label_size = 10
img = data_layer(name='image', size=data_size)
# small_vgg is predefined in trainer_config_helpers.networks
predict = small_vgg(input_image=img, num_channels=3, num_classes=label_size)
if not is_predict:
lbl = data_layer(name="label", size=label_size)
outputs(classification_cost(input=predict, label=lbl))
else:
outputs(predict)
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dataprovider.pyc
empty.list
train.log
output
train.list
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This folder contains scripts used in PaddlePaddle introduction.
- use `bash train.sh` to train a simple linear regression model
- use `python evaluate_model.py` to read model parameters. You can see that `w` and `b` are very close to [2, 0.3].
demo/introduction/api_train_v2.py 已删除 100644 → 0
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import paddle.v2 as paddle
import paddle.v2.dataset.uci_housing as uci_housing
def main():
# init
paddle.init(use_gpu=False, trainer_count=1)
# network config
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x,
param_attr=paddle.attr.Param(name='w'),
size=1,
act=paddle.activation.Linear(),
bias_attr=paddle.attr.Param(name='b'))
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.mse_cost(input=y_predict, label=y)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
optimizer = paddle.optimizer.Momentum(momentum=0)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer)
# event_handler to print training and testing info
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(
reader=paddle.batch(
uci_housing.test(), batch_size=2),
feeding={'x': 0,
'y': 1})
print "Test %d, %.2f" % (event.pass_id, result.cost)
# training
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
uci_housing.train(), buf_size=500),
batch_size=2),
feeding={'x': 0,
'y': 1},
event_handler=event_handler,
num_passes=30)
if __name__ == '__main__':
main()
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# 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.PyDataProvider2 import *
import random
# define data types of input: 2 real numbers
@provider(
input_types={'x': dense_vector(1),
'y': dense_vector(1)}, use_seq=False)
def process(settings, input_file):
for i in xrange(2000):
x = random.random()
yield {'x': [x], 'y': [2 * x + 0.3]}
demo/introduction/evaluate_model.py 已删除 100755 → 0
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#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# 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.
"""
Print model parameters in last model
Usage:
python evaluate_model.py
"""
import numpy as np
import os
def load(file_name):
with open(file_name, 'rb') as f:
f.read(16) # skip header for float type.
return np.fromfile(f, dtype=np.float32)
def main():
print 'w=%.6f, b=%.6f from pass 29' % (load('output/pass-00029/w'),
load('output/pass-00029/b'))
if __name__ == '__main__':
main()
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#!/bin/bash
# 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.
set -e
paddle train \
--config=trainer_config.py \
--save_dir=./output \
--num_passes=30 \
2>&1 |tee 'train.log'
paddle usage -l "train.log" -e $? -n "introduction" >/dev/null 2>&1
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# 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 *
# 1. read data. Suppose you saved above python code as dataprovider.py
define_py_data_sources2(
train_list=['no_matter.txt'],
test_list=None,
module='dataprovider',
obj='process',
args={})
# 2. learning algorithm
settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
# 3. Network configuration
x = data_layer(name='x', size=1)
y = data_layer(name='y', size=1)
y_predict = fc_layer(
input=x,
param_attr=ParamAttr(name='w'),
size=1,
act=LinearActivation(),
bias_attr=ParamAttr(name='b'))
cost = mse_cost(input=y_predict, label=y)
outputs(cost)
demo/mnist/api_train_v2.py 已删除 100644 → 0
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import paddle.v2 as paddle
import gzip
def softmax_regression(img):
predict = paddle.layer.fc(input=img,
size=10,
act=paddle.activation.Softmax())
return predict
def multilayer_perceptron(img):
# The first fully-connected layer
hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu())
# The second fully-connected layer and the according activation function
hidden2 = paddle.layer.fc(input=hidden1,
size=64,
act=paddle.activation.Relu())
# The thrid fully-connected layer, note that the hidden size should be 10,
# which is the number of unique digits
predict = paddle.layer.fc(input=hidden2,
size=10,
act=paddle.activation.Softmax())
return predict
def convolutional_neural_network(img):
# first conv layer
conv_pool_1 = paddle.networks.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
num_channel=1,
pool_size=2,
pool_stride=2,
act=paddle.activation.Tanh())
# second conv layer
conv_pool_2 = paddle.networks.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
num_channel=20,
pool_size=2,
pool_stride=2,
act=paddle.activation.Tanh())
# The first fully-connected layer
fc1 = paddle.layer.fc(input=conv_pool_2,
size=128,
act=paddle.activation.Tanh())
# The softmax layer, note that the hidden size should be 10,
# which is the number of unique digits
predict = paddle.layer.fc(input=fc1,
size=10,
act=paddle.activation.Softmax())
return predict
def main():
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
images = paddle.layer.data(
name='pixel', type=paddle.data_type.dense_vector(784))
label = paddle.layer.data(
name='label', type=paddle.data_type.integer_value(10))
# Here we can build the prediction network in different ways. Please
# choose one by uncomment corresponding line.
predict = softmax_regression(images)
#predict = multilayer_perceptron(images)
#predict = convolutional_neural_network(images)
cost = paddle.layer.classification_cost(input=predict, label=label)
try:
with gzip.open('params.tar.gz', 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
except IOError:
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1 / 128.0,
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer)
lists = []
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1000 == 0:
print "Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
with gzip.open('params.tar.gz', 'w') as f:
parameters.to_tar(f)
elif isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=paddle.batch(
paddle.dataset.mnist.test(), batch_size=128))
print "Test with Pass %d, Cost %f, %s\n" % (
event.pass_id, result.cost, result.metrics)
lists.append((event.pass_id, result.cost,
result.metrics['classification_error_evaluator']))
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=128),
event_handler=event_handler,
num_passes=100)
# find the best pass
best = sorted(lists, key=lambda list: float(list[1]))[0]
print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
test_creator = paddle.dataset.mnist.test()
test_data = []
for item in test_creator():
test_data.append((item[0], ))
if len(test_data) == 100:
break
# output is a softmax layer. It returns probabilities.
# Shape should be (100, 10)
probs = paddle.infer(
output_layer=predict, parameters=parameters, input=test_data)
print probs.shape
if __name__ == '__main__':
main()
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log.txt
data/meta.bin
data/ml-1m
data/ratings.dat.train
data/ratings.dat.test
data/train.list
data/test.list
dataprovider_copy_1.py
*.pyc
output
demo/recommendation/api_train_v2.py 已删除 100644 → 0
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import paddle.v2 as paddle
import cPickle
import copy
def main():
paddle.init(use_gpu=False)
movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
uid = paddle.layer.data(
name='user_id',
type=paddle.data_type.integer_value(
paddle.dataset.movielens.max_user_id() + 1))
usr_emb = paddle.layer.embedding(input=uid, size=32)
usr_gender_id = paddle.layer.data(
name='gender_id', type=paddle.data_type.integer_value(2))
usr_gender_emb = paddle.layer.embedding(input=usr_gender_id, size=16)
usr_age_id = paddle.layer.data(
name='age_id',
type=paddle.data_type.integer_value(
len(paddle.dataset.movielens.age_table)))
usr_age_emb = paddle.layer.embedding(input=usr_age_id, size=16)
usr_job_id = paddle.layer.data(
name='job_id',
type=paddle.data_type.integer_value(paddle.dataset.movielens.max_job_id(
) + 1))
usr_job_emb = paddle.layer.embedding(input=usr_job_id, size=16)
usr_combined_features = paddle.layer.fc(
input=[usr_emb, usr_gender_emb, usr_age_emb, usr_job_emb],
size=200,
act=paddle.activation.Tanh())
mov_id = paddle.layer.data(
name='movie_id',
type=paddle.data_type.integer_value(
paddle.dataset.movielens.max_movie_id() + 1))
mov_emb = paddle.layer.embedding(input=mov_id, size=32)
mov_categories = paddle.layer.data(
name='category_id',
type=paddle.data_type.sparse_binary_vector(
len(paddle.dataset.movielens.movie_categories())))
mov_categories_hidden = paddle.layer.fc(input=mov_categories, size=32)
mov_title_id = paddle.layer.data(
name='movie_title',
type=paddle.data_type.integer_value_sequence(len(movie_title_dict)))
mov_title_emb = paddle.layer.embedding(input=mov_title_id, size=32)
mov_title_conv = paddle.networks.sequence_conv_pool(
input=mov_title_emb, hidden_size=32, context_len=3)
mov_combined_features = paddle.layer.fc(
input=[mov_emb, mov_categories_hidden, mov_title_conv],
size=200,
act=paddle.activation.Tanh())
inference = paddle.layer.cos_sim(
a=usr_combined_features, b=mov_combined_features, size=1, scale=5)
cost = paddle.layer.mse_cost(
input=inference,
label=paddle.layer.data(
name='score', type=paddle.data_type.dense_vector(1)))
parameters = paddle.parameters.create(cost)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=paddle.optimizer.Adam(
learning_rate=1e-4))
feeding = {
'user_id': 0,
'gender_id': 1,
'age_id': 2,
'job_id': 3,
'movie_id': 4,
'category_id': 5,
'movie_title': 6,
'score': 7
}
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d Batch %d Cost %.2f" % (
event.pass_id, event.batch_id, event.cost)
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=256),
event_handler=event_handler,
feeding=feeding,
num_passes=1)
user_id = 234
movie_id = 345
user = paddle.dataset.movielens.user_info()[user_id]
movie = paddle.dataset.movielens.movie_info()[movie_id]
feature = user.value() + movie.value()
def reader():
yield feature
infer_dict = copy.copy(feeding)
del infer_dict['score']
prediction = paddle.infer(
output=inference,
parameters=parameters,
reader=paddle.batch(
reader, batch_size=32),
feeding=infer_dict)
print(prediction + 5) / 2
if __name__ == '__main__':
main()
demo/recommendation/common_utils.py 已删除 100755 → 0
浏览文件 @ e36e24d4
# 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.PyDataProvider2 import *
def meta_to_header(meta, name):
metas = meta[name]['__meta__']['raw_meta']
for each_meta in metas:
slot_name = each_meta.get('name', '%s_id' % name)
if each_meta['type'] == 'id':
yield slot_name, integer_value(each_meta['max'])
elif each_meta['type'] == 'embedding':
is_seq = each_meta['seq'] == 'sequence'
yield slot_name, integer_value(
len(each_meta['dict']),
seq_type=SequenceType.SEQUENCE
if is_seq else SequenceType.NO_SEQUENCE)
elif each_meta['type'] == 'one_hot_dense':
yield slot_name, dense_vector(len(each_meta['dict']))
demo/recommendation/data/config.json 已删除 100644 → 0
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{
"user": {
"file": {
"name": "users.dat",
"delimiter": "::"
},
"fields": ["id", "gender", "age", "occupation"]
},
"movie": {
"file": {
"name": "movies.dat",
"delimiter": "::"
},
"fields": ["id", "title", "genres"]
}
}
demo/recommendation/data/config_generator.py 已删除 100644 → 0
浏览文件 @ e36e24d4
#!/bin/env python2
# 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.
"""
config_generator.py
Usage:
./config_generator.py <config_file> [--output_format=<output_format>]
./config_generator.py -h | --help
Options:
-h --help Show this screen.
--output_format=<output_format> Output Config format(json or yaml) [default: json].
"""
import json
import docopt
import copy
DEFAULT_FILE = {"type": "split", "delimiter": ","}
DEFAULT_FIELD = {
"id": {
"type": "id"
},
"gender": {
"name": "gender",
"type": "embedding",
"dict": {
"type": "char_based"
}
},
"age": {
"name": "age",
"type": "embedding",
"dict": {
"type": "whole_content",
"sort": True
}
},
"occupation": {
"name": "occupation",
"type": "embedding",
"dict": {
"type": "whole_content",
"sort": "true"
}
},
"title": {
"regex": {
"pattern": r"^(.*)\((\d+)\)$",
"group_id": 1,
"strip": True
},
"name": "title",
"type": {
"name": "embedding",
"seq_type": "sequence",
},
"dict": {
"type": "char_based"
}
},
"genres": {
"type": "one_hot_dense",
"dict": {
"type": "split",
"delimiter": "|"
},
"name": "genres"
}
}
def merge_dict(master_dict, slave_dict):
return dict(((k, master_dict.get(k) or slave_dict.get(k))
for k in set(slave_dict) | set(master_dict)))
def main(filename, fmt):
with open(filename, 'r') as f:
conf = json.load(f)
obj = dict()
for k in conf:
val = conf[k]
file_dict = val['file']
file_dict = merge_dict(file_dict, DEFAULT_FILE)
fields = []
for pos, field_key in enumerate(val['fields']):
assert isinstance(field_key, basestring)
field = copy.deepcopy(DEFAULT_FIELD[field_key])
field['pos'] = pos
fields.append(field)
obj[k] = {"file": file_dict, "fields": fields}
meta = {"meta": obj}
# print meta
if fmt == 'json':
def formatter(x):
import json
return json.dumps(x, indent=2)
elif fmt == 'yaml':
def formatter(x):
import yaml
return yaml.safe_dump(x, default_flow_style=False)
else:
raise NotImplementedError("Dump format %s is not implemented" % fmt)
print formatter(meta)
if __name__ == '__main__':
args = docopt.docopt(__doc__, version="0.1.0")
main(args["<config_file>"], args["--output_format"])
demo/recommendation/data/meta_config.json 已删除 100644 → 0
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{
"meta": {
"movie": {
"fields": [
{
"type": "id",
"pos": 0
},
{
"regex": {
"pattern": "^(.*)\\((\\d+)\\)$",
"group_id": 1,
"strip": true
},
"type": {
"seq_type": "sequence",
"name": "embedding"
},
"dict": {
"type": "char_based"
},
"name": "title",
"pos": 1
},
{
"type": "one_hot_dense",
"dict": {
"delimiter": "|",
"type": "split"
},
"name": "genres",
"pos": 2
}
],
"file": {
"delimiter": "::",
"type": "split",
"name": "movies.dat"
}
},
"user": {
"fields": [
{
"type": "id",
"pos": 0
},
{
"type": "embedding",
"dict": {
"type": "char_based"
},
"name": "gender",
"pos": 1
},
{
"type": "embedding",
"dict": {
"sort": true,
"type": "whole_content"
},
"name": "age",
"pos": 2
},
{
"type": "embedding",
"dict": {
"sort": "true",
"type": "whole_content"
},
"name": "occupation",
"pos": 3
}
],
"file": {
"delimiter": "::",
"type": "split",
"name": "users.dat"
}
}
}
}
demo/recommendation/data/meta_generator.py 已删除 100644 → 0
浏览文件 @ e36e24d4
#!/bin/env python2
# 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.
"""
Preprocess Movielens dataset, to get movie/user object.
Usage:
./preprocess.py <dataset_dir> <binary_filename> [--config=<config_file>]
./preprocess.py -h | --help
Options:
-h --help Show this screen.
--version Show version.
--config=<config_file> Get MetaData config file [default: config.json].
"""
import docopt
import os
import sys
import re
import collections
try:
import cPickle as pickle
except ImportError:
import pickle
class UniqueIDGenerator(object):
def __init__(self):
self.pool = collections.defaultdict(self.__next_id__)
self.next_id = 0
def __next_id__(self):
tmp = self.next_id
self.next_id += 1
return tmp
def __call__(self, k):
return self.pool[k]
def to_list(self):
ret_val = [None] * len(self.pool)
for k in self.pool.keys():
ret_val[self.pool[k]] = k
return ret_val
class SortedIDGenerator(object):
def __init__(self):
self.__key_set__ = set()
self.dict = None
def scan(self, key):
self.__key_set__.add(key)
def finish_scan(self, compare=None, key=None, reverse=False):
self.__key_set__ = sorted(
list(self.__key_set__), cmp=compare, key=key, reverse=reverse)
self.dict = dict()
for idx, each_key in enumerate(self.__key_set__):
self.dict[each_key] = idx
def __call__(self, key):
return self.dict[key]
def to_list(self):
return self.__key_set__
class SplitFileReader(object):
def __init__(self, work_dir, config):
assert isinstance(config, dict)
self.filename = config['name']
self.delimiter = config.get('delimiter', ',')
self.work_dir = work_dir
def read(self):
with open(os.path.join(self.work_dir, self.filename), 'r') as f:
for line in f:
line = line.strip()
if isinstance(self.delimiter, unicode):
self.delimiter = str(self.delimiter)
yield line.split(self.delimiter)
@staticmethod
def create(work_dir, config):
assert isinstance(config, dict)
if config['type'] == 'split':
return SplitFileReader(work_dir, config)
class IFileReader(object):
READERS = [SplitFileReader]
def read(self):
raise NotImplementedError()
@staticmethod
def create(work_dir, config):
for reader_cls in IFileReader.READERS:
val = reader_cls.create(work_dir, config)
if val is not None:
return val
class IDFieldParser(object):
TYPE = 'id'
def __init__(self, config):
self.__max_id__ = -sys.maxint - 1
self.__min_id__ = sys.maxint
self.__id_count__ = 0
def scan(self, line):
idx = int(line)
self.__max_id__ = max(self.__max_id__, idx)
self.__min_id__ = min(self.__min_id__, idx)
self.__id_count__ += 1
def parse(self, line):
return int(line)
def meta_field(self):
return {
"is_key": True,
'max': self.__max_id__,
'min': self.__min_id__,
'count': self.__id_count__,
'type': 'id'
}
class SplitEmbeddingDict(object):
def __init__(self, delimiter):
self.__id__ = UniqueIDGenerator()
self.delimiter = delimiter
def scan(self, multi):
for val in multi.split(self.delimiter):
self.__id__(val)
def parse(self, multi):
return map(self.__id__, multi.split(self.delimiter))
def meta_field(self):
return self.__id__.to_list()
class EmbeddingFieldParser(object):
TYPE = 'embedding'
NO_SEQUENCE = "no_sequence"
SEQUENCE = "sequence"
class CharBasedEmbeddingDict(object):
def __init__(self, is_seq=True):
self.__id__ = UniqueIDGenerator()
self.is_seq = is_seq
def scan(self, s):
for ch in s:
self.__id__(ch)
def parse(self, s):
return map(self.__id__, s) if self.is_seq else self.__id__(s[0])
def meta_field(self):
return self.__id__.to_list()
class WholeContentDict(object):
def __init__(self, need_sort=True):
assert need_sort
self.__id__ = SortedIDGenerator()
self.__has_finished__ = False
def scan(self, txt):
self.__id__.scan(txt)
def meta_field(self):
if not self.__has_finished__:
self.__id__.finish_scan()
self.__has_finished__ = True
return self.__id__.to_list()
def parse(self, txt):
return self.__id__(txt)
def __init__(self, config):
try:
self.seq_type = config['type']['seq_type']
except TypeError:
self.seq_type = EmbeddingFieldParser.NO_SEQUENCE
if config['dict']['type'] == 'char_based':
self.dict = EmbeddingFieldParser.CharBasedEmbeddingDict(
self.seq_type == EmbeddingFieldParser.SEQUENCE)
elif config['dict']['type'] == 'split':
self.dict = SplitEmbeddingDict(config['dict'].get('delimiter', ','))
elif config['dict']['type'] == 'whole_content':
self.dict = EmbeddingFieldParser.WholeContentDict(config['dict'][
'sort'])
else:
print config
assert False
self.name = config['name']
def scan(self, s):
self.dict.scan(s)
def meta_field(self):
return {
'name': self.name,
'dict': self.dict.meta_field(),
'type': 'embedding',
'seq': self.seq_type
}
def parse(self, s):
return self.dict.parse(s)
class OneHotDenseFieldParser(object):
TYPE = 'one_hot_dense'
def __init__(self, config):
if config['dict']['type'] == 'split':
self.dict = SplitEmbeddingDict(config['dict']['delimiter'])
self.name = config['name']
def scan(self, s):
self.dict.scan(s)
def meta_field(self):
# print self.dict.meta_field()
return {
'dict': self.dict.meta_field(),
'name': self.name,
'type': 'one_hot_dense'
}
def parse(self, s):
ids = self.dict.parse(s)
retv = [0.0] * len(self.dict.meta_field())
for idx in ids:
retv[idx] = 1.0
# print retv
return retv
class FieldParserFactory(object):
PARSERS = [IDFieldParser, EmbeddingFieldParser, OneHotDenseFieldParser]
@staticmethod
def create(config):
if isinstance(config['type'], basestring):
config_type = config['type']
elif isinstance(config['type'], dict):
config_type = config['type']['name']
assert config_type is not None
for each_parser_cls in FieldParserFactory.PARSERS:
if config_type == each_parser_cls.TYPE:
return each_parser_cls(config)
print config
class CompositeFieldParser(object):
def __init__(self, parser, extractor):
self.extractor = extractor
self.parser = parser
def scan(self, *args, **kwargs):
self.parser.scan(self.extractor.extract(*args, **kwargs))
def parse(self, *args, **kwargs):
return self.parser.parse(self.extractor.extract(*args, **kwargs))
def meta_field(self):
return self.parser.meta_field()
class PositionContentExtractor(object):
def __init__(self, pos):
self.pos = pos
def extract(self, line):
assert isinstance(line, list)
return line[self.pos]
class RegexPositionContentExtractor(PositionContentExtractor):
def __init__(self, pos, pattern, group_id, strip=True):
PositionContentExtractor.__init__(self, pos)
pattern = pattern.strip()
self.pattern = re.compile(pattern)
self.group_id = group_id
self.strip = strip
def extract(self, line):
line = PositionContentExtractor.extract(self, line)
match = self.pattern.match(line)
# print line, self.pattern.pattern, match
assert match is not None
txt = match.group(self.group_id)
if self.strip:
txt.strip()
return txt
class ContentExtractorFactory(object):
def extract(self, line):
pass
@staticmethod
def create(config):
if 'pos' in config:
if 'regex' not in config:
return PositionContentExtractor(config['pos'])
else:
extra_args = config['regex']
return RegexPositionContentExtractor(
pos=config['pos'], **extra_args)
class MetaFile(object):
def __init__(self, work_dir):
self.work_dir = work_dir
self.obj = dict()
def parse(self, config):
config = config['meta']
ret_obj = dict()
for key in config.keys():
val = config[key]
assert 'file' in val
reader = IFileReader.create(self.work_dir, val['file'])
assert reader is not None
assert 'fields' in val and isinstance(val['fields'], list)
fields_config = val['fields']
field_parsers = map(MetaFile.__field_config_mapper__, fields_config)
for each_parser in field_parsers:
assert each_parser is not None
for each_block in reader.read():
for each_parser in field_parsers:
each_parser.scan(each_block)
metas = map(lambda x: x.meta_field(), field_parsers)
# print metas
key_index = filter(
lambda x: x is not None,
map(lambda (idx, meta): idx if 'is_key' in meta and meta['is_key'] else None,
enumerate(metas)))[0]
key_map = []
for i in range(min(key_index, len(metas))):
key_map.append(i)
for i in range(key_index + 1, len(metas)):
key_map.append(i)
obj = {'__meta__': {'raw_meta': metas, 'feature_map': key_map}}
for each_block in reader.read():
idx = field_parsers[key_index].parse(each_block)
val = []
for i, each_parser in enumerate(field_parsers):
if i != key_index:
val.append(each_parser.parse(each_block))
obj[idx] = val
ret_obj[key] = obj
self.obj = ret_obj
return ret_obj
@staticmethod
def __field_config_mapper__(conf):
assert isinstance(conf, dict)
extrator = ContentExtractorFactory.create(conf)
field_parser = FieldParserFactory.create(conf)
assert extrator is not None
assert field_parser is not None
return CompositeFieldParser(field_parser, extrator)
def dump(self, fp):
pickle.dump(self.obj, fp, pickle.HIGHEST_PROTOCOL)
def preprocess(binary_filename, dataset_dir, config, **kwargs):
assert isinstance(config, str)
with open(config, 'r') as config_file:
file_loader = None
if config.lower().endswith('.yaml'):
import yaml
file_loader = yaml
elif config.lower().endswith('.json'):
import json
file_loader = json
config = file_loader.load(config_file)
meta = MetaFile(dataset_dir)
meta.parse(config)
with open(binary_filename, 'wb') as outf:
meta.dump(outf)
if __name__ == '__main__':
args = docopt.docopt(__doc__, version='0.1.0')
kwargs = dict()
for key in args.keys():
if key != '--help':
param_name = key
assert isinstance(param_name, str)
param_name = param_name.replace('<', '')
param_name = param_name.replace('>', '')
param_name = param_name.replace('--', '')
kwargs[param_name] = args[key]
preprocess(**kwargs)
demo/recommendation/data/ml_data.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -ex
cd "$(dirname "$0")"
# download the dataset
wget http://files.grouplens.org/datasets/movielens/ml-1m.zip
# unzip the dataset
unzip ml-1m.zip
# remove the unused zip file
rm ml-1m.zip
demo/recommendation/data/split.py 已删除 100644 → 0
浏览文件 @ e36e24d4
#!/bin/env python2
# 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.
"""
Separate movielens 1m dataset to train/test file.
Usage:
./separate.py <input_file> [--test_ratio=<test_ratio>] [--delimiter=<delimiter>]
./separate.py -h | --help
Options:
-h --help Show this screen.
--version Show version.
--test_ratio=<test_ratio> Test ratio for separate [default: 0.1].
--delimiter=<delimiter> File delimiter [default: ,].
"""
import docopt
import collections
import random
def process(test_ratio, input_file, delimiter, **kwargs):
test_ratio = float(test_ratio)
rating_dict = collections.defaultdict(list)
with open(input_file, 'r') as f:
for line in f:
user_id = int(line.split(delimiter)[0])
rating_dict[user_id].append(line.strip())
with open(input_file + ".train", 'w') as train_file:
with open(input_file + ".test", 'w') as test_file:
for k in rating_dict.keys():
lines = rating_dict[k]
assert isinstance(lines, list)
random.shuffle(lines)
test_len = int(len(lines) * test_ratio)
for line in lines[:test_len]:
print >> test_file, line
for line in lines[test_len:]:
print >> train_file, line
if __name__ == '__main__':
args = docopt.docopt(__doc__, version='0.1.0')
kwargs = dict()
for key in args.keys():
if key != '--help':
param_name = key
assert isinstance(param_name, str)
param_name = param_name.replace('<', '')
param_name = param_name.replace('>', '')
param_name = param_name.replace('--', '')
kwargs[param_name] = args[key]
process(**kwargs)
demo/recommendation/dataprovider.py 已删除 100755 → 0
浏览文件 @ e36e24d4
# 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.PyDataProvider2 import *
import common_utils # parse
def __list_to_map__(lst):
ret_val = dict()
for each in lst:
k, v = each
ret_val[k] = v
return ret_val
def hook(settings, meta, **kwargs):
"""
Init hook is invoked before process data. It will set obj.slots and store
data meta.
:param obj: global object. It will passed to process routine.
:type obj: object
:param meta: the meta file object, which passed from trainer_config. Meta
file record movie/user features.
:param kwargs: unused other arguments.
"""
del kwargs # unused kwargs
# Header define slots that used for paddle.
# first part is movie features.
# second part is user features.
# final part is rating score.
# header is a list of [USE_SEQ_OR_NOT?, SlotType]
movie_headers = list(common_utils.meta_to_header(meta, 'movie'))
settings.movie_names = [h[0] for h in movie_headers]
headers = movie_headers
user_headers = list(common_utils.meta_to_header(meta, 'user'))
settings.user_names = [h[0] for h in user_headers]
headers.extend(user_headers)
headers.append(("rating", dense_vector(1))) # Score
# slot types.
settings.input_types = __list_to_map__(headers)
settings.meta = meta
@provider(init_hook=hook, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, filename):
with open(filename, 'r') as f:
for line in f:
# Get a rating from file.
user_id, movie_id, score = map(int, line.split('::')[:-1])
# Scale score to [-5, +5]
score = float(score) * 2 - 5.0
# Get movie/user features by movie_id, user_id
movie_meta = settings.meta['movie'][movie_id]
user_meta = settings.meta['user'][user_id]
outputs = [('movie_id', movie_id - 1)]
# Then add movie features
for i, each_meta in enumerate(movie_meta):
outputs.append((settings.movie_names[i + 1], each_meta))
# Then add user id.
outputs.append(('user_id', user_id - 1))
# Then add user features.
for i, each_meta in enumerate(user_meta):
outputs.append((settings.user_names[i + 1], each_meta))
# Finally, add score
outputs.append(('rating', [score]))
# Return data to paddle
yield __list_to_map__(outputs)
demo/recommendation/evaluate.py 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/usr/bin/python
# 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 sys
import re
import math
def get_best_pass(log_filename):
with open(log_filename, 'r') as f:
text = f.read()
pattern = re.compile('Test.*? cost=([0-9]+\.[0-9]+).*?pass-([0-9]+)',
re.S)
results = re.findall(pattern, text)
sorted_results = sorted(results, key=lambda result: float(result[0]))
return sorted_results[0]
log_filename = sys.argv[1]
log = get_best_pass(log_filename)
predict_error = math.sqrt(float(log[0])) / 2
print 'Best pass is %s, error is %s, which means predict get error as %f' % (
log[1], log[0], predict_error)
evaluate_pass = "output/pass-%s" % log[1]
print "evaluating from pass %s" % evaluate_pass
demo/recommendation/evaluate.sh 已删除 100755 → 0
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#!/bin/bash
# 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.
set -e
function get_best_pass() {
cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | sort | head -n 1
}
LOG=`get_best_pass log.txt`
LOG=(${LOG})
echo 'Best pass is '${LOG[1]}, ' error is '${LOG[0]}, 'which means predict get error as '`echo ${LOG[0]} | python -c 'import math; print math.sqrt(float(raw_input()))/2'`
evaluate_pass="output/pass-${LOG[1]}"
echo 'evaluating from pass '$evaluate_pass
demo/recommendation/prediction.py 已删除 100755 → 0
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#!/bin/env python2
# 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 py_paddle import swig_paddle, DataProviderConverter
from common_utils import *
from paddle.trainer.config_parser import parse_config
try:
import cPickle as pickle
except ImportError:
import pickle
import sys
if __name__ == '__main__':
model_path = sys.argv[1]
swig_paddle.initPaddle('--use_gpu=0')
conf = parse_config("trainer_config.py", "is_predict=1")
network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(network, swig_paddle.GradientMachine)
network.loadParameters(model_path)
with open('./data/meta.bin', 'rb') as f:
meta = pickle.load(f)
headers = [h[1] for h in meta_to_header(meta, 'movie')]
headers.extend([h[1] for h in meta_to_header(meta, 'user')])
cvt = DataProviderConverter(headers)
while True:
movie_id = int(raw_input("Input movie_id: "))
user_id = int(raw_input("Input user_id: "))
movie_meta = meta['movie'][movie_id] # Query Data From Meta.
user_meta = meta['user'][user_id]
data = [movie_id - 1]
data.extend(movie_meta)
data.append(user_id - 1)
data.extend(user_meta)
print "Prediction Score is %.2f" % (
(network.forwardTest(cvt.convert([data]))[0]['value'][0][0] + 5)
/ 2)
demo/recommendation/preprocess.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
UNAME_STR=`uname`
if [[ ${UNAME_STR} == 'Linux' ]]; then
SHUF_PROG='shuf'
else
SHUF_PROG='gshuf'
fi
cd "$(dirname "$0")"
delimiter='::'
dir=ml-1m
cd data
echo 'generate meta config file'
python config_generator.py config.json > meta_config.json
echo 'generate meta file'
python meta_generator.py $dir meta.bin --config=meta_config.json
echo 'split train/test file'
python split.py $dir/ratings.dat --delimiter=${delimiter} --test_ratio=0.1
echo 'shuffle train file'
${SHUF_PROG} $dir/ratings.dat.train > ratings.dat.train
cp $dir/ratings.dat.test .
echo "./data/ratings.dat.train" > train.list
echo "./data/ratings.dat.test" > test.list
demo/recommendation/run.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
paddle train \
--config=trainer_config.py \
--save_dir=./output \
--use_gpu=false \
--trainer_count=4\
--test_all_data_in_one_period=true \
--log_period=100 \
--dot_period=1 \
--num_passes=50 2>&1 | tee 'log.txt'
paddle usage -l log.txt -e $? -n "recommendation" >/dev/null 2>&1
demo/recommendation/trainer_config.py 已删除 100755 → 0
浏览文件 @ e36e24d4
# 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 *
try:
import cPickle as pickle
except ImportError:
import pickle
is_predict = get_config_arg('is_predict', bool, False)
META_FILE = 'data/meta.bin'
with open(META_FILE, 'rb') as f:
# load meta file
meta = pickle.load(f)
settings(
batch_size=1600, learning_rate=1e-3, learning_method=RMSPropOptimizer())
def construct_feature(name):
"""
Construct movie/user features.
This method read from meta data. Then convert feature to neural network due
to feature type. The map relation as follow.
* id: embedding => fc
* embedding:
is_sequence: embedding => context_projection => fc => pool
not sequence: embedding => fc
* one_hot_dense: fc => fc
Then gather all features vector, and use a fc layer to combined them as
return.
:param name: 'movie' or 'user'
:type name: basestring
:return: combined feature output
:rtype: LayerOutput
"""
__meta__ = meta[name]['__meta__']['raw_meta']
fusion = []
for each_meta in __meta__:
type_name = each_meta['type']
slot_name = each_meta.get('name', '%s_id' % name)
if type_name == 'id':
slot_dim = each_meta['max']
embedding = embedding_layer(
input=data_layer(
slot_name, size=slot_dim), size=256)
fusion.append(fc_layer(input=embedding, size=256))
elif type_name == 'embedding':
is_seq = each_meta['seq'] == 'sequence'
slot_dim = len(each_meta['dict'])
din = data_layer(slot_name, slot_dim)
embedding = embedding_layer(input=din, size=256)
if is_seq:
fusion.append(
text_conv_pool(
input=embedding, context_len=5, hidden_size=256))
else:
fusion.append(fc_layer(input=embedding, size=256))
elif type_name == 'one_hot_dense':
slot_dim = len(each_meta['dict'])
hidden = fc_layer(input=data_layer(slot_name, slot_dim), size=256)
fusion.append(fc_layer(input=hidden, size=256))
return fc_layer(name="%s_fusion" % name, input=fusion, size=256)
movie_feature = construct_feature("movie")
user_feature = construct_feature("user")
similarity = cos_sim(a=movie_feature, b=user_feature)
if not is_predict:
outputs(mse_cost(input=similarity, label=data_layer('rating', size=1)))
define_py_data_sources2(
'data/train.list',
'data/test.list',
module='dataprovider',
obj='process',
args={'meta': meta})
else:
outputs(similarity)
demo/semantic_role_labeling/.gitignore 已删除 100644 → 0
浏览文件 @ e36e24d4
*.pyc
train.log
data/feature
data/conll05st-release/
data/src.dict
data/test.wsj.props
data/test.wsj.seq_pair
data/test.wsj.words
data/tgt.dict
output
data/emb
data/targetDict.txt
data/verbDict.txt
data/wordDict.txt
demo/semantic_role_labeling/api_train_v2.py 已删除 100644 → 0
浏览文件 @ e36e24d4
import math
import numpy as np
import gzip
import logging
import paddle.v2.dataset.conll05 as conll05
import paddle.v2.evaluator as evaluator
import paddle.v2 as paddle
logger = logging.getLogger('paddle')
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
default_std = 1 / math.sqrt(hidden_dim) / 3.0
mix_hidden_lr = 1e-3
def d_type(size):
return paddle.data_type.integer_value_sequence(size)
def db_lstm():
#8 features
word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))
ctx_n2 = paddle.layer.data(name='ctx_n2_data', type=d_type(word_dict_len))
ctx_n1 = paddle.layer.data(name='ctx_n1_data', type=d_type(word_dict_len))
ctx_0 = paddle.layer.data(name='ctx_0_data', type=d_type(word_dict_len))
ctx_p1 = paddle.layer.data(name='ctx_p1_data', type=d_type(word_dict_len))
ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))
emb_para = paddle.attr.Param(name='emb', initial_std=0., is_static=True)
std_0 = paddle.attr.Param(initial_std=0.)
std_default = paddle.attr.Param(initial_std=default_std)
predicate_embedding = paddle.layer.embedding(
size=word_dim,
input=predicate,
param_attr=paddle.attr.Param(
name='vemb', initial_std=default_std))
mark_embedding = paddle.layer.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 = [
paddle.layer.embedding(
size=word_dim, input=x, param_attr=emb_para) for x in word_input
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
hidden_0 = paddle.layer.mixed(
size=hidden_dim,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=emb, param_attr=std_default) for emb in emb_layers
])
lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
hidden_para_attr = paddle.attr.Param(
initial_std=default_std, learning_rate=mix_hidden_lr)
lstm_0 = paddle.layer.lstmemory(
input=hidden_0,
act=paddle.activation.Relu(),
gate_act=paddle.activation.Sigmoid(),
state_act=paddle.activation.Sigmoid(),
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 = paddle.layer.mixed(
size=hidden_dim,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
paddle.layer.full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
])
lstm = paddle.layer.lstmemory(
input=mix_hidden,
act=paddle.activation.Relu(),
gate_act=paddle.activation.Sigmoid(),
state_act=paddle.activation.Sigmoid(),
reverse=((i % 2) == 1),
bias_attr=std_0,
param_attr=lstm_para_attr)
input_tmp = [mix_hidden, lstm]
feature_out = paddle.layer.mixed(
size=label_dict_len,
bias_attr=std_default,
input=[
paddle.layer.full_matrix_projection(
input=input_tmp[0], param_attr=hidden_para_attr),
paddle.layer.full_matrix_projection(
input=input_tmp[1], param_attr=lstm_para_attr)
], )
return feature_out
def load_parameter(file_name, h, w):
with open(file_name, 'rb') as f:
f.read(16) # skip header.
return np.fromfile(f, dtype=np.float32).reshape(h, w)
def train():
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
feature_out = db_lstm()
target = paddle.layer.data(name='target', type=d_type(label_dict_len))
crf_cost = paddle.layer.crf(size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(
name='crfw',
initial_std=default_std,
learning_rate=mix_hidden_lr))
crf_dec = paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(name='crfw'))
evaluator.sum(input=crf_dec)
# create parameters
parameters = paddle.parameters.create(crf_cost)
parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
# create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0,
learning_rate=2e-2,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(
average_window=0.5, max_average_window=10000), )
trainer = paddle.trainer.SGD(cost=crf_cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=crf_dec)
reader = paddle.batch(
paddle.reader.shuffle(
conll05.test(), buf_size=8192), batch_size=10)
feeding = {
'word_data': 0,
'ctx_n2_data': 1,
'ctx_n1_data': 2,
'ctx_0_data': 3,
'ctx_p1_data': 4,
'ctx_p2_data': 5,
'verb_data': 6,
'mark_data': 7,
'target': 8
}
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
logger.info("Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics))
if event.batch_id and event.batch_id % 1000 == 0:
result = trainer.test(reader=reader, feeding=feeding)
logger.info("\nTest with Pass %d, Batch %d, %s" %
(event.pass_id, event.batch_id, result.metrics))
if isinstance(event, paddle.event.EndPass):
# save parameters
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=reader, feeding=feeding)
logger.info("\nTest with Pass %d, %s" %
(event.pass_id, result.metrics))
trainer.train(
reader=reader,
event_handler=event_handler,
num_passes=10,
feeding=feeding)
def infer_a_batch(inferer, test_data, word_dict, pred_dict, label_dict):
probs = inferer.infer(input=test_data, field='id')
assert len(probs) == sum(len(x[0]) for x in test_data)
for idx, test_sample in enumerate(test_data):
start_id = 0
pred_str = "%s\t" % (pred_dict[test_sample[6][0]])
for w, tag in zip(test_sample[0],
probs[start_id:start_id + len(test_sample[0])]):
pred_str += "%s[%s] " % (word_dict[w], label_dict[tag])
print(pred_str.strip())
start_id += len(test_sample[0])
def infer():
label_dict_reverse = dict((value, key)
for key, value in label_dict.iteritems())
word_dict_reverse = dict((value, key)
for key, value in word_dict.iteritems())
pred_dict_reverse = dict((value, key)
for key, value in verb_dict.iteritems())
test_creator = paddle.dataset.conll05.test()
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
feature_out = db_lstm()
predict = paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
param_attr=paddle.attr.Param(name='crfw'))
test_pass = 0
with gzip.open('params_pass_%d.tar.gz' % (test_pass)) as f:
parameters = paddle.parameters.Parameters.from_tar(f)
inferer = paddle.inference.Inference(
output_layer=predict, parameters=parameters)
# prepare test data
test_data = []
test_batch_size = 50
for idx, item in enumerate(test_creator()):
test_data.append(item[0:8])
if idx and (not idx % test_batch_size):
infer_a_batch(
inferer,
test_data,
word_dict_reverse,
pred_dict_reverse,
label_dict_reverse, )
test_data = []
infer_a_batch(
inferer,
test_data,
word_dict_reverse,
pred_dict_reverse,
label_dict_reverse, )
test_data = []
def main(is_inferring=False):
if is_inferring:
infer()
else:
train()
if __name__ == '__main__':
main(is_inferring=False)
demo/semantic_role_labeling/data/extract_dict_feature.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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 sys
import os
from optparse import OptionParser
def extract_dict_features(pair_file, feature_file):
with open(pair_file) as fin, open(feature_file, 'w') as feature_out:
for line in fin:
sentence, predicate, labels = line.strip().split('\t')
sentence_list = sentence.split()
labels_list = labels.split()
verb_index = labels_list.index('B-V')
mark = [0] * len(labels_list)
if verb_index > 0:
mark[verb_index - 1] = 1
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]
else:
ctx_n2 = 'bos'
mark[verb_index] = 1
ctx_0 = sentence_list[verb_index]
if verb_index < len(labels_list) - 1:
mark[verb_index + 1] = 1
ctx_p1 = sentence_list[verb_index + 1]
else:
ctx_p1 = 'eos'
if verb_index < len(labels_list) - 2:
mark[verb_index + 2] = 1
ctx_p2 = sentence_list[verb_index + 2]
else:
ctx_p2 = 'eos'
feature_str = sentence + '\t' \
+ predicate + '\t' \
+ ctx_n2 + '\t' \
+ ctx_n1 + '\t' \
+ ctx_0 + '\t' \
+ ctx_p1 + '\t' \
+ ctx_p2 + '\t' \
+ ' '.join([str(i) for i in mark]) + '\t' \
+ labels
feature_out.write(feature_str + '\n')
if __name__ == '__main__':
usage = '-p pair_file -f feature_file'
parser = OptionParser(usage)
parser.add_option('-p', dest='pair_file', help='the pair file')
parser.add_option('-f', dest='feature_file', help='the feature file')
(options, args) = parser.parse_args()
extract_dict_features(options.pair_file, options.feature_file)
demo/semantic_role_labeling/data/extract_pairs.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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 sys
import os
from optparse import OptionParser
def read_labels(props_file):
'''
a sentence maybe has more than one verb, each verb has its label sequence
label[], is a 3-dimension list.
the first dim is to store all sentence's label seqs, len is the sentence number
the second dim is to store all label sequences for one sentences
the third dim is to store each label for one word
'''
labels = []
with open(props_file) as fin:
label_seqs_for_one_sentences = []
one_seg_in_file = []
for line in fin:
line = line.strip()
if line == '':
for i in xrange(len(one_seg_in_file[0])):
a_kind_lable = [x[i] for x in one_seg_in_file]
label_seqs_for_one_sentences.append(a_kind_lable)
labels.append(label_seqs_for_one_sentences)
one_seg_in_file = []
label_seqs_for_one_sentences = []
else:
part = line.split()
one_seg_in_file.append(part)
return labels
def read_sentences(words_file):
sentences = []
with open(words_file) as fin:
s = ''
for line in fin:
line = line.strip()
if line == '':
sentences.append(s)
s = ''
else:
s += line + ' '
return sentences
def transform_labels(sentences, labels):
sen_lab_pair = []
for i in xrange(len(sentences)):
if len(labels[i]) == 1:
continue
else:
verb_list = []
for x in labels[i][0]:
if x != '-':
verb_list.append(x)
for j in xrange(1, len(labels[i])):
label_list = labels[i][j]
current_tag = 'O'
is_in_bracket = False
label_seq = []
verb_word = ''
for ll in label_list:
if ll == '*' and is_in_bracket == False:
label_seq.append('O')
elif ll == '*' and is_in_bracket == True:
label_seq.append('I-' + current_tag)
elif ll == '*)':
label_seq.append('I-' + current_tag)
is_in_bracket = False
elif ll.find('(') != -1 and ll.find(')') != -1:
current_tag = ll[1:ll.find('*')]
label_seq.append('B-' + current_tag)
is_in_bracket = False
elif ll.find('(') != -1 and ll.find(')') == -1:
current_tag = ll[1:ll.find('*')]
label_seq.append('B-' + current_tag)
is_in_bracket = True
else:
print 'error:', ll
sen_lab_pair.append((sentences[i], verb_list[j - 1], label_seq))
return sen_lab_pair
def write_file(sen_lab_pair, output_file):
with open(output_file, 'w') as fout:
for x in sen_lab_pair:
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')
if __name__ == '__main__':
usage = '-w words_file -p props_file -o output_file'
parser = OptionParser(usage)
parser.add_option('-w', dest='words_file', help='the words file')
parser.add_option('-p', dest='props_file', help='the props file')
parser.add_option('-o', dest='output_file', help='the output_file')
(options, args) = parser.parse_args()
sentences = read_sentences(options.words_file)
labels = read_labels(options.props_file)
sen_lab_pair = transform_labels(sentences, labels)
write_file(sen_lab_pair, options.output_file)
demo/semantic_role_labeling/data/get_data.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz
wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt
wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt
wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt
wget http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/emb
tar -xzvf conll05st-tests.tar.gz
rm conll05st-tests.tar.gz
cp ./conll05st-release/test.wsj/words/test.wsj.words.gz .
cp ./conll05st-release/test.wsj/props/test.wsj.props.gz .
gunzip test.wsj.words.gz
gunzip test.wsj.props.gz
python extract_pairs.py -w test.wsj.words -p test.wsj.props -o test.wsj.seq_pair
python extract_dict_feature.py -p test.wsj.seq_pair -f feature
demo/semantic_role_labeling/dataprovider.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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.PyDataProvider2 import *
UNK_IDX = 0
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)),
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(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=True,
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]
predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
marks = mark.split()
mark_slot = [int(w) for w in marks]
label_list = label.split()
label_slot = [settings.label_dict.get(w) for w in label_list]
yield word_slot, ctx_n2_slot, ctx_n1_slot, \
ctx_0_slot, ctx_p1_slot, ctx_p2_slot, predicate_slot, mark_slot, label_slot
demo/semantic_role_labeling/db_lstm.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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 math
import os
import sys
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'
train_list_file = './data/train.list'
test_list_file = './data/test.list'
is_test = get_config_arg('is_test', bool, False)
is_predict = get_config_arg('is_predict', bool, False)
if not is_predict:
#load dictionaries
word_dict = dict()
label_dict = dict()
predicate_dict = dict()
with open(word_dict_file, 'r') as f_word, \
open(label_dict_file, 'r') as f_label, \
open(predicate_file, 'r') as f_pre:
for i, line in enumerate(f_word):
w = line.strip()
word_dict[w] = i
for i, line in enumerate(f_label):
w = line.strip()
label_dict[w] = i
for i, line in enumerate(f_pre):
w = line.strip()
predicate_dict[w] = i
if is_test:
train_list_file = None
#define data provider
define_py_data_sources2(
train_list=train_list_file,
test_list=test_list_file,
module='dataprovider',
obj='process',
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)
pred_len = len(predicate_dict)
else:
word_dict_len = get_config_arg('dict_len', int)
label_dict_len = get_config_arg('label_len', int)
pred_len = get_config_arg('pred_len', int)
############################## Hyper-parameters ##################################
mark_dict_len = 2
word_dim = 32
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), )
####################################### network ##############################
#8 features and 1 target
word = data_layer(name='word_data', size=word_dict_len)
predicate = data_layer(name='verb_data', size=pred_len)
ctx_n2 = data_layer(name='ctx_n2_data', size=word_dict_len)
ctx_n1 = data_layer(name='ctx_n1_data', size=word_dict_len)
ctx_0 = data_layer(name='ctx_0_data', size=word_dict_len)
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
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
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
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
])
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)
#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)
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)
], )
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'))
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'))
outputs(crf_dec_l)
demo/semantic_role_labeling/predict.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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 os
import numpy as np
from optparse import OptionParser
from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import integer_value_sequence
from paddle.trainer.config_parser import parse_config
"""
Usage: run following command to show help message.
python predict.py -h
"""
UNK_IDX = 0
class Prediction():
def __init__(self, train_conf, dict_file, model_dir, label_file,
predicate_dict_file):
"""
train_conf: trainer configure.
dict_file: word dictionary file name.
model_dir: directory of model.
"""
self.dict = {}
self.labels = {}
self.predicate_dict = {}
self.labels_reverse = {}
self.load_dict_label(dict_file, label_file, predicate_dict_file)
len_dict = len(self.dict)
len_label = len(self.labels)
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')
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)
]
self.converter = DataProviderConverter(slots)
def load_dict_label(self, dict_file, label_file, predicate_dict_file):
"""
Load dictionary from self.dict_file.
"""
for line_count, line in enumerate(open(dict_file, 'r')):
self.dict[line.strip()] = line_count
for line_count, line in enumerate(open(label_file, 'r')):
self.labels[line.strip()] = line_count
self.labels_reverse[line_count] = line.strip()
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.
"""
with open(data_file, '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 = [self.dict.get(w, UNK_IDX) for w in words]
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
ctx_p1_slot = [self.dict.get(ctx_p1, UNK_IDX)] * sen_len
ctx_p2_slot = [self.dict.get(ctx_p2, UNK_IDX)] * sen_len
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
def predict(self, data_file, output_file):
"""
data_file: file name of input data.
"""
input = self.converter(self.get_data(data_file))
output = self.network.forwardTest(input)
lab = output[0]["id"].tolist()
with open(data_file, 'r') as fin, open(output_file, 'w') as fout:
index = 0
for line in fin:
sen = line.split('\t')[0]
len_sen = len(sen.split())
line_labels = lab[index:index + len_sen]
index += len_sen
fout.write(sen + '\t' + ' '.join(
[self.labels_reverse[i] for i in line_labels]) + '\n')
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")
parser = OptionParser(usage="usage: %s [options]" % usage)
parser.add_option(
"-c",
"--tconf",
action="store",
dest="train_conf",
help="network config")
parser.add_option(
"-d",
"--dict",
action="store",
dest="dict_file",
help="dictionary file")
parser.add_option(
"-l",
"--label",
action="store",
dest="label_file",
default=None,
help="label file")
parser.add_option(
"-p",
"--predict_dict_file",
action="store",
dest="predict_dict_file",
default=None,
help="predict_dict_file")
parser.add_option(
"-i",
"--data",
action="store",
dest="data_file",
help="data file to predict")
parser.add_option(
"-w",
"--model",
action="store",
dest="model_path",
default=None,
help="model path")
parser.add_option(
"-o",
"--output_file",
action="store",
dest="output_file",
default=None,
help="output file")
return parser.parse_args()
def main():
options, args = option_parser()
train_conf = options.train_conf
data_file = options.data_file
dict_file = options.dict_file
model_path = options.model_path
label_file = options.label_file
predict_dict_file = options.predict_dict_file
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)
if __name__ == '__main__':
main()
demo/semantic_role_labeling/predict.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
function get_best_pass() {
cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \
sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' | \
sort -n | head -n 1
}
log=train.log
LOG=`get_best_pass $log`
LOG=(${LOG})
best_model_path="output/pass-${LOG[1]}"
config_file=db_lstm.py
dict_file=./data/wordDict.txt
label_file=./data/targetDict.txt
predicate_dict_file=./data/verbDict.txt
input_file=./data/feature
output_file=predict.res
python predict.py \
-c $config_file \
-w $best_model_path \
-l $label_file \
-p $predicate_dict_file \
-d $dict_file \
-i $input_file \
-o $output_file
demo/semantic_role_labeling/test.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
function get_best_pass() {
cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \
sed -r 'N;s/Test.* cost=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' |\
sort -n | head -n 1
}
log=train.log
LOG=`get_best_pass $log`
LOG=(${LOG})
evaluate_pass="output/pass-${LOG[1]}"
echo 'evaluating from pass '$evaluate_pass
model_list=./model.list
touch $model_list | echo $evaluate_pass > $model_list
paddle train \
--config=./db_lstm.py \
--model_list=$model_list \
--job=test \
--use_gpu=false \
--config_args=is_test=1 \
--test_all_data_in_one_period=1 \
2>&1 | tee 'test.log'
paddle usage -l test.log -e $? -n "semantic_role_labeling_test" >/dev/null 2>&1
demo/semantic_role_labeling/train.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
paddle train \
--config=./db_lstm.py \
--use_gpu=0 \
--log_period=5000 \
--trainer_count=1 \
--show_parameter_stats_period=5000 \
--save_dir=./output \
--num_passes=10000 \
--average_test_period=10000000 \
--init_model_path=./data \
--load_missing_parameter_strategy=rand \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
paddle usage -l train.log -e $? -n "semantic_role_labeling_train" >/dev/null 2>&1
demo/sentiment/.gitignore 已删除 100644 → 0
浏览文件 @ e36e24d4
data/aclImdb
data/imdb
data/pre-imdb
data/mosesdecoder-master
logs/
model_output
dataprovider_copy_1.py
model.list
test.log
train.log
*.pyc
demo/sentiment/data/get_imdb.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
set -x
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd $DIR
#download the dataset
echo "Downloading aclImdb..."
#http://ai.stanford.edu/%7Eamaas/data/sentiment/
wget http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
echo "Downloading mosesdecoder..."
#https://github.com/moses-smt/mosesdecoder
wget https://github.com/moses-smt/mosesdecoder/archive/master.zip
#extract package
echo "Unzipping..."
tar -zxvf aclImdb_v1.tar.gz
unzip master.zip
#move train and test set to imdb_data directory
#in order to process when traing
mkdir -p imdb/train
mkdir -p imdb/test
cp -r aclImdb/train/pos/ imdb/train/pos
cp -r aclImdb/train/neg/ imdb/train/neg
cp -r aclImdb/test/pos/ imdb/test/pos
cp -r aclImdb/test/neg/ imdb/test/neg
#remove compressed package
rm aclImdb_v1.tar.gz
rm master.zip
echo "Done."
demo/sentiment/dataprovider.py 已删除 100755 → 0
浏览文件 @ e36e24d4
# 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.PyDataProvider2 import *
def hook(settings, dictionary, **kwargs):
settings.word_dict = dictionary
settings.input_types = [
integer_value_sequence(len(settings.word_dict)), integer_value(2)
]
settings.logger.info('dict len : %d' % (len(settings.word_dict)))
@provider(init_hook=hook)
def process(settings, file_name):
with open(file_name, 'r') as fdata:
for line_count, line in enumerate(fdata):
label, comment = line.strip().split('\t\t')
label = int(label)
words = comment.split()
word_slot = [
settings.word_dict[w] for w in words if w in settings.word_dict
]
if not word_slot:
continue
yield word_slot, label
demo/sentiment/predict.py 已删除 100755 → 0
浏览文件 @ e36e24d4
# 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 os, sys
import numpy as np
from optparse import OptionParser
from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import integer_value_sequence
from paddle.trainer.config_parser import parse_config
"""
Usage: run following command to show help message.
python predict.py -h
"""
class SentimentPrediction():
def __init__(self, train_conf, dict_file, model_dir=None, label_file=None):
"""
train_conf: trainer configure.
dict_file: word dictionary file name.
model_dir: directory of model.
"""
self.train_conf = train_conf
self.dict_file = dict_file
self.word_dict = {}
self.dict_dim = self.load_dict()
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.label = None
if label_file is not None:
self.load_label(label_file)
conf = parse_config(train_conf, "is_predict=1")
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
self.network.loadParameters(self.model_dir)
input_types = [integer_value_sequence(self.dict_dim)]
self.converter = DataProviderConverter(input_types)
def load_dict(self):
"""
Load dictionary from self.dict_file.
"""
for line_count, line in enumerate(open(self.dict_file, 'r')):
self.word_dict[line.strip().split('\t')[0]] = line_count
return len(self.word_dict)
def load_label(self, label_file):
"""
Load label.
"""
self.label = {}
for v in open(label_file, 'r'):
self.label[int(v.split('\t')[1])] = v.split('\t')[0]
def get_index(self, data):
"""
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]
return word_slot
def batch_predict(self, data_batch):
input = self.converter(data_batch)
output = self.network.forwardTest(input)
prob = output[0]["value"]
labs = np.argsort(-prob)
for idx, lab in enumerate(labs):
if self.label is None:
print("predicting label is %d" % (lab[0]))
else:
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 "
parser = OptionParser(usage="usage: %s [options]" % usage)
parser.add_option(
"-n",
"--tconf",
action="store",
dest="train_conf",
help="network config")
parser.add_option(
"-d",
"--dict",
action="store",
dest="dict_file",
help="dictionary file")
parser.add_option(
"-b",
"--label",
action="store",
dest="label",
default=None,
help="dictionary file")
parser.add_option(
"-c",
"--batch_size",
type="int",
action="store",
dest="batch_size",
default=1,
help="the batch size for prediction")
parser.add_option(
"-w",
"--model",
action="store",
dest="model_path",
default=None,
help="model path")
return parser.parse_args()
def main():
options, args = option_parser()
train_conf = options.train_conf
batch_size = options.batch_size
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = SentimentPrediction(train_conf, dict_file, model_path, label)
batch = []
for line in sys.stdin:
words = predict.get_index(line)
if words:
batch.append([words])
else:
print('All the words in [%s] are not in the dictionary.' % line)
if len(batch) == batch_size:
predict.batch_predict(batch)
batch = []
if len(batch) > 0:
predict.batch_predict(batch)
if __name__ == '__main__':
main()
demo/sentiment/predict.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
#Note the default model is pass-00002, you shold make sure the model path
#exists or change the mode path.
model=model_output/pass-00002/
config=trainer_config.py
label=data/pre-imdb/labels.list
cat ./data/aclImdb/test/pos/10007_10.txt | python predict.py \
--tconf=$config\
--model=$model \
--label=$label \
--dict=./data/pre-imdb/dict.txt \
--batch_size=1
demo/sentiment/preprocess.py 已删除 100755 → 0
浏览文件 @ e36e24d4
# 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 os
import sys
import random
import operator
import numpy as np
from subprocess import Popen, PIPE
from os.path import join as join_path
from optparse import OptionParser
from paddle.utils.preprocess_util import *
"""
Usage: run following command to show help message.
python preprocess.py -h
"""
def save_dict(dict, filename, is_reverse=True):
"""
Save dictionary into file.
dict: input dictionary.
filename: output file name, string.
is_reverse: True, descending order by value.
False, ascending order by value.
"""
f = open(filename, 'w')
for k, v in sorted(dict.items(), key=operator.itemgetter(1),\
reverse=is_reverse):
f.write('%s\t%s\n' % (k, v))
f.close()
def tokenize(sentences):
"""
Use tokenizer.perl to tokenize input sentences.
tokenizer.perl is tool of Moses.
sentences : a list of input sentences.
return: a list of processed text.
"""
dir = './data/mosesdecoder-master/scripts/tokenizer/tokenizer.perl'
tokenizer_cmd = [dir, '-l', 'en', '-q', '-']
assert isinstance(sentences, list)
text = "\n".join(sentences)
tokenizer = Popen(tokenizer_cmd, stdin=PIPE, stdout=PIPE)
tok_text, _ = tokenizer.communicate(text)
toks = tok_text.split('\n')[:-1]
return toks
def read_lines(path):
"""
path: String, file path.
return a list of sequence.
"""
seqs = []
with open(path, 'r') as f:
for line in f.readlines():
line = line.strip()
if len(line):
seqs.append(line)
return seqs
class SentimentDataSetCreate():
"""
A class to process data for sentiment analysis task.
"""
def __init__(self,
data_path,
output_path,
use_okenizer=True,
multi_lines=False):
"""
data_path: string, traing and testing dataset path
output_path: string, output path, store processed dataset
multi_lines: whether a file has multi lines.
In order to shuffle fully, it needs to read all files into
memory, then shuffle them if one file has multi lines.
"""
self.output_path = output_path
self.data_path = data_path
self.train_dir = 'train'
self.test_dir = 'test'
self.train_list = "train.list"
self.test_list = "test.list"
self.label_list = "labels.list"
self.classes_num = 0
self.batch_size = 50000
self.batch_dir = 'batches'
self.dict_file = "dict.txt"
self.dict_with_test = False
self.dict_size = 0
self.word_count = {}
self.tokenizer = use_okenizer
self.overwrite = False
self.multi_lines = multi_lines
self.train_dir = join_path(data_path, self.train_dir)
self.test_dir = join_path(data_path, self.test_dir)
self.train_list = join_path(output_path, self.train_list)
self.test_list = join_path(output_path, self.test_list)
self.label_list = join_path(output_path, self.label_list)
self.dict_file = join_path(output_path, self.dict_file)
def data_list(self, path):
"""
create dataset from path
path: data path
return: data list
"""
label_set = get_label_set_from_dir(path)
data = []
for lab_name in label_set.keys():
file_paths = list_files(join_path(path, lab_name))
for p in file_paths:
data.append({"label" : label_set[lab_name],\
"seq_path": p})
return data, label_set
def create_dict(self, data):
"""
create dict for input data.
data: list, [sequence, sequnce, ...]
"""
for seq in data:
for w in seq.strip().lower().split():
if w not in self.word_count:
self.word_count[w] = 1
else:
self.word_count[w] += 1
def create_dataset(self):
"""
create file batches and dictionary of train data set.
If the self.overwrite is false and train.list already exists in
self.output_path, this function will not create and save file
batches from the data set path.
return: dictionary size, class number.
"""
out_path = self.output_path
if out_path and not os.path.exists(out_path):
os.makedirs(out_path)
# If self.overwrite is false or self.train_list has existed,
# it will not process dataset.
if not (self.overwrite or not os.path.exists(self.train_list)):
print "%s already exists." % self.train_list
return
# Preprocess train data.
train_data, train_lab_set = self.data_list(self.train_dir)
print "processing train set..."
file_lists = self.save_data(train_data, "train", self.batch_size, True,
True)
save_list(file_lists, self.train_list)
# If have test data path, preprocess test data.
if os.path.exists(self.test_dir):
test_data, test_lab_set = self.data_list(self.test_dir)
assert (train_lab_set == test_lab_set)
print "processing test set..."
file_lists = self.save_data(test_data, "test", self.batch_size,
False, self.dict_with_test)
save_list(file_lists, self.test_list)
# save labels set.
save_dict(train_lab_set, self.label_list, False)
self.classes_num = len(train_lab_set.keys())
# save dictionary.
save_dict(self.word_count, self.dict_file, True)
self.dict_size = len(self.word_count)
def save_data(self,
data,
prefix="",
batch_size=50000,
is_shuffle=False,
build_dict=False):
"""
Create batches for a Dataset object.
data: the Dataset object to process.
prefix: the prefix of each batch.
batch_size: number of data in each batch.
build_dict: whether to build dictionary for data
return: list of batch names
"""
if is_shuffle and self.multi_lines:
return self.save_data_multi_lines(data, prefix, batch_size,
build_dict)
if is_shuffle:
random.shuffle(data)
num_batches = int(math.ceil(len(data) / float(batch_size)))
batch_names = []
for i in range(num_batches):
batch_name = join_path(self.output_path,
"%s_part_%03d" % (prefix, i))
begin = i * batch_size
end = min((i + 1) * batch_size, len(data))
# read a batch of data
label_list, data_list = self.get_data_list(begin, end, data)
if build_dict:
self.create_dict(data_list)
self.save_file(label_list, data_list, batch_name)
batch_names.append(batch_name)
return batch_names
def get_data_list(self, begin, end, data):
"""
begin: int, begining index of data.
end: int, ending index of data.
data: a list of {"seq_path": seqquence path, "label": label index}
return a list of label and a list of sequence.
"""
label_list = []
data_list = []
for j in range(begin, end):
seqs = read_lines(data[j]["seq_path"])
lab = int(data[j]["label"])
#File may have multiple lines.
for seq in seqs:
data_list.append(seq)
label_list.append(lab)
if self.tokenizer:
data_list = tokenize(data_list)
return label_list, data_list
def save_data_multi_lines(self,
data,
prefix="",
batch_size=50000,
build_dict=False):
"""
In order to shuffle fully, there is no need to load all data if
each file only contains one sample, it only needs to shuffle list
of file name. But one file contains multi lines, each line is one
sample. It needs to read all data into memory to shuffle fully.
This interface is mainly for data containning multi lines in each
file, which consumes more memory if there is a great mount of data.
data: the Dataset object to process.
prefix: the prefix of each batch.
batch_size: number of data in each batch.
build_dict: whether to build dictionary for data
return: list of batch names
"""
assert self.multi_lines
label_list = []
data_list = []
# read all data
label_list, data_list = self.get_data_list(0, len(data), data)
if build_dict:
self.create_dict(data_list)
length = len(label_list)
perm_list = np.array([i for i in xrange(length)])
random.shuffle(perm_list)
num_batches = int(math.ceil(length / float(batch_size)))
batch_names = []
for i in range(num_batches):
batch_name = join_path(self.output_path,
"%s_part_%03d" % (prefix, i))
begin = i * batch_size
end = min((i + 1) * batch_size, length)
sub_label = [label_list[perm_list[i]] for i in range(begin, end)]
sub_data = [data_list[perm_list[i]] for i in range(begin, end)]
self.save_file(sub_label, sub_data, batch_name)
batch_names.append(batch_name)
return batch_names
def save_file(self, label_list, data_list, filename):
"""
Save data into file.
label_list: a list of int value.
data_list: a list of sequnece.
filename: output file name.
"""
f = open(filename, 'w')
print "saving file: %s" % filename
for lab, seq in zip(label_list, data_list):
f.write('%s\t\t%s\n' % (lab, seq))
f.close()
def option_parser():
parser = OptionParser(usage="usage: python preprcoess.py "\
"-i data_dir [options]")
parser.add_option(
"-i",
"--data",
action="store",
dest="input",
help="Input data directory.")
parser.add_option(
"-o",
"--output",
action="store",
dest="output",
default=None,
help="Output directory.")
parser.add_option(
"-t",
"--tokenizer",
action="store",
dest="use_tokenizer",
default=True,
help="Whether to use tokenizer.")
parser.add_option("-m", "--multi_lines", action="store",
dest="multi_lines", default=False,
help="If input text files have multi lines and they "\
"need to be shuffled, you should set -m True,")
return parser.parse_args()
def main():
options, args = option_parser()
data_dir = options.input
output_dir = options.output
use_tokenizer = options.use_tokenizer
multi_lines = options.multi_lines
if output_dir is None:
outname = os.path.basename(options.input)
output_dir = join_path(os.path.dirname(data_dir), 'pre-' + outname)
data_creator = SentimentDataSetCreate(data_dir, output_dir, use_tokenizer,
multi_lines)
data_creator.create_dataset()
if __name__ == '__main__':
main()
demo/sentiment/preprocess.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
echo "Start to preprcess..."
data_dir="./data/imdb"
python preprocess.py -i $data_dir
echo "Done."
demo/sentiment/sentiment_net.py 已删除 100644 → 0
浏览文件 @ e36e24d4
# 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 os.path import join as join_path
from paddle.trainer_config_helpers import *
def sentiment_data(data_dir=None,
is_test=False,
is_predict=False,
train_list="train.list",
test_list="test.list",
dict_file="dict.txt"):
"""
Predefined data provider for sentiment analysis.
is_test: whether this config is used for test.
is_predict: whether this config is used for prediction.
train_list: text file name, containing a list of training set.
test_list: text file name, containing a list of testing set.
dict_file: text file name, containing dictionary.
"""
dict_dim = len(open(join_path(data_dir, "dict.txt")).readlines())
class_dim = len(open(join_path(data_dir, 'labels.list')).readlines())
if is_predict:
return dict_dim, class_dim
if data_dir is not None:
train_list = join_path(data_dir, train_list)
test_list = join_path(data_dir, test_list)
dict_file = join_path(data_dir, dict_file)
train_list = train_list if not is_test else None
word_dict = dict()
with open(dict_file, 'r') as f:
for i, line in enumerate(open(dict_file, 'r')):
word_dict[line.split('\t')[0]] = i
define_py_data_sources2(
train_list,
test_list,
module="dataprovider",
obj="process",
args={'dictionary': word_dict})
return dict_dim, class_dim
def bidirectional_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
lstm_dim=128,
is_predict=False):
data = data_layer("word", input_dim)
emb = embedding_layer(input=data, size=emb_dim)
bi_lstm = bidirectional_lstm(input=emb, size=lstm_dim)
dropout = dropout_layer(input=bi_lstm, dropout_rate=0.5)
output = fc_layer(input=dropout, size=class_dim, act=SoftmaxActivation())
if not is_predict:
lbl = data_layer("label", 1)
outputs(classification_cost(input=output, label=lbl))
else:
outputs(output)
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3,
is_predict=False):
"""
A Wrapper for sentiment classification task.
This network uses bi-directional recurrent network,
consisting three LSTM layers. This configure is referred to
the paper as following url, but use fewer layrs.
http://www.aclweb.org/anthology/P15-1109
input_dim: here is word dictionary dimension.
class_dim: number of categories.
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
is_predict: is predicting or not.
Some layers is not needed in network when predicting.
"""
hid_lr = 1e-3
assert stacked_num % 2 == 1
layer_attr = ExtraLayerAttribute(drop_rate=0.5)
fc_para_attr = ParameterAttribute(learning_rate=hid_lr)
lstm_para_attr = ParameterAttribute(initial_std=0., learning_rate=1.)
para_attr = [fc_para_attr, lstm_para_attr]
bias_attr = ParameterAttribute(initial_std=0., l2_rate=0.)
relu = ReluActivation()
linear = LinearActivation()
data = data_layer("word", input_dim)
emb = embedding_layer(input=data, size=emb_dim)
fc1 = fc_layer(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
lstm1 = lstmemory(
input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = fc_layer(
input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = lstmemory(
input=fc,
reverse=(i % 2) == 0,
act=relu,
bias_attr=bias_attr,
layer_attr=layer_attr)
inputs = [fc, lstm]
fc_last = pooling_layer(input=inputs[0], pooling_type=MaxPooling())
lstm_last = pooling_layer(input=inputs[1], pooling_type=MaxPooling())
output = fc_layer(
input=[fc_last, lstm_last],
size=class_dim,
act=SoftmaxActivation(),
bias_attr=bias_attr,
param_attr=para_attr)
if is_predict:
outputs(output)
else:
outputs(classification_cost(input=output, label=data_layer('label', 1)))
demo/sentiment/test.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# 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.
set -e
function get_best_pass() {
cat $1 | grep -Pzo 'Test .*\n.*pass-.*' | \
sed -r 'N;s/Test.* classification_error_evaluator=([0-9]+\.[0-9]+).*\n.*pass-([0-9]+)/\1 \2/g' |\
sort -n | head -n 1
}
log=train.log
LOG=`get_best_pass $log`
LOG=(${LOG})
evaluate_pass="model_output/pass-${LOG[1]}"
echo 'evaluating from pass '$evaluate_pass
model_list=./model.list
touch $model_list | echo $evaluate_pass > $model_list
net_conf=trainer_config.py
paddle train --config=$net_conf \
--model_list=$model_list \
--job=test \
--use_gpu=false \
--trainer_count=4 \
--config_args=is_test=1 \
2>&1 | tee 'test.log'
paddle usage -l test.log -e $? -n "sentiment_test" >/dev/null 2>&1
demo/sentiment/train.sh 已删除 100755 → 0
浏览文件 @ e36e24d4
#!/bin/bash
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
set -e
config=trainer_config.py
output=./model_output
paddle train --config=$config \
--save_dir=$output \
--job=train \
--use_gpu=false \
--trainer_count=4 \
--num_passes=10 \
--log_period=10 \
--dot_period=20 \
--show_parameter_stats_period=100 \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
paddle usage -l train.log -e $? -n "sentiment_train" >/dev/null 2>&1
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# 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 sys
import paddle.v2 as paddle
def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
conv_3 = paddle.networks.sequence_conv_pool(
input=emb, context_len=3, hidden_size=hid_dim)
conv_4 = paddle.networks.sequence_conv_pool(
input=emb, context_len=4, hidden_size=hid_dim)
output = paddle.layer.fc(input=[conv_3, conv_4],
size=class_dim,
act=paddle.activation.Softmax())
lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
"""
A Wrapper for sentiment classification task.
This network uses bi-directional recurrent network,
consisting three LSTM layers. This configure is referred to
the paper as following url, but use fewer layrs.
http://www.aclweb.org/anthology/P15-1109
input_dim: here is word dictionary dimension.
class_dim: number of categories.
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
"""
assert stacked_num % 2 == 1
layer_attr = paddle.attr.Extra(drop_rate=0.5)
fc_para_attr = paddle.attr.Param(learning_rate=1e-3)
lstm_para_attr = paddle.attr.Param(initial_std=0., learning_rate=1.)
para_attr = [fc_para_attr, lstm_para_attr]
bias_attr = paddle.attr.Param(initial_std=0., l2_rate=0.)
relu = paddle.activation.Relu()
linear = paddle.activation.Linear()
data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
fc1 = paddle.layer.fc(input=emb,
size=hid_dim,
act=linear,
bias_attr=bias_attr)
lstm1 = paddle.layer.lstmemory(
input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = paddle.layer.fc(input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = paddle.layer.lstmemory(
input=fc,
reverse=(i % 2) == 0,
act=relu,
bias_attr=bias_attr,
layer_attr=layer_attr)
inputs = [fc, lstm]
fc_last = paddle.layer.pooling(
input=inputs[0], pooling_type=paddle.pooling.Max())
lstm_last = paddle.layer.pooling(
input=inputs[1], pooling_type=paddle.pooling.Max())
output = paddle.layer.fc(input=[fc_last, lstm_last],
size=class_dim,
act=paddle.activation.Softmax(),
bias_attr=bias_attr,
param_attr=para_attr)
lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost
if __name__ == '__main__':
# init
paddle.init(use_gpu=False)
#data
print 'load dictionary...'
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
train_reader = paddle.batch(
paddle.reader.shuffle(
lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=100)
test_reader = paddle.batch(
lambda: paddle.dataset.imdb.test(word_dict), batch_size=100)
feeding = {'word': 0, 'label': 1}
# network config
# Please choose the way to build the network
# by uncommenting the corresponding line.
cost = convolution_net(dict_dim, class_dim=class_dim)
# cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
adam_optimizer = paddle.optimizer.Adam(
learning_rate=2e-3,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(average_window=0.5))
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=test_reader, feeding=feeding)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=adam_optimizer)
trainer.train(
reader=train_reader,
event_handler=event_handler,
feeding=feeding,
num_passes=2)
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# 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 sentiment_net import *
from paddle.trainer_config_helpers import *
# whether this config is used for test
is_test = get_config_arg('is_test', bool, False)
# whether this config is used for prediction
is_predict = get_config_arg('is_predict', bool, False)
data_dir = "./data/pre-imdb"
dict_dim, class_dim = sentiment_data(data_dir, is_test, is_predict)
################## Algorithm Config #####################
settings(
batch_size=128,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
model_average=ModelAverage(0.5),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25)
#################### Network Config ######################
stacked_lstm_net(
dict_dim, class_dim=class_dim, stacked_num=3, is_predict=is_predict)
# bidirectional_lstm_net(dict_dim, class_dim=class_dim, is_predict=is_predict)
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data/wmt14
data/pre-wmt14
data/wmt14_model
data/paraphrase
data/pre-paraphrase
data/paraphrase_model
translation/gen.log
translation/gen_result
translation/train.log
paraphrase/train.log
dataprovider_copy_1.py
translation/thirdparty.tgz
translation/thirdparty/train.conf
translation/thirdparty/dataprovider.py
translation/thirdparty/seqToseq_net.py
translation/thirdparty/*.dict
*.pyc
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#!/bin/bash
# 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.
set -e
set -x
# download the in-house paraphrase dataset
wget http://paddlepaddle.bj.bcebos.com/model_zoo/embedding/paraphrase.tar.gz
# untar the dataset
tar -zxvf paraphrase.tar.gz
rm paraphrase.tar.gz
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#!/bin/bash
# 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.
set -e
set -x
dim=32
pretrained_dir='../../model_zoo/embedding/'
preModel=$pretrained_dir'model_'$dim'.emb'
preDict=$pretrained_dir'baidu.dict'
usrDict_dir='pre-paraphrase/'
srcDict=$usrDict_dir'src.dict'
trgDict=$usrDict_dir'trg.dict'
usrModel_dir='paraphrase_model/'
mkdir $usrModel_dir
srcModel=$usrModel_dir'_source_language_embedding'
trgModel=$usrModel_dir'_target_language_embedding'
echo 'extract desired parameters based on user dictionary'
script=$pretrained_dir'extract_para.py'
python $script --preModel $preModel --preDict $preDict \
--usrModel $srcModel --usrDict $srcDict -d $dim
python $script --preModel $preModel --preDict $preDict \
--usrModel $trgModel --usrDict $trgDict -d $dim
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#!/bin/bash
# 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.
set -e
set -x
mkdir wmt14
cd wmt14
# download the dataset
wget http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/bitexts.tgz
wget http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz
# untar the dataset
tar -zxvf bitexts.tgz
tar -zxvf dev+test.tgz
gunzip bitexts.selected/*
mv bitexts.selected train
rm bitexts.tgz
rm dev+test.tgz
# separate the dev and test dataset
mkdir test gen
mv dev/ntst1213.* test
mv dev/ntst14.* gen
rm -rf dev
set +x
# rename the suffix, .fr->.src, .en->.trg
for dir in train test gen
do
filelist=`ls $dir`
cd $dir
for file in $filelist
do
if [ ${file##*.} = "fr" ]; then
mv $file ${file/%fr/src}
elif [ ${file##*.} = 'en' ]; then
mv $file ${file/%en/trg}
fi
done
cd ..
done
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#!/bin/bash
# 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.
set -e
set -x
# download the pretrained model
wget http://paddlepaddle.bj.bcebos.com/model_zoo/wmt14_model.tar.gz
# untar the model
tar -zxvf wmt14_model.tar.gz
rm wmt14_model.tar.gz
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#edit-mode: -*- python -*-
# 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 sys
sys.path.append("..")
from seqToseq_net import *
is_generating = False
### Data Definiation
train_conf = seq_to_seq_data(data_dir = "./data/pre-paraphrase",
is_generating = is_generating)
### Algorithm Configuration
settings(
learning_method = AdamOptimizer(),
batch_size = 50,
learning_rate = 5e-4)
### Network Architecture
gru_encoder_decoder(train_conf, is_generating, word_vector_dim = 32)
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#!/bin/bash
# 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.
set -e
cd ..
paddle train \
--job=test \
--config='translation/gen.conf' \
--save_dir='data/wmt14_model' \
--use_gpu=false \
--num_passes=13 \
--test_pass=12 \
--trainer_count=1 \
2>&1 | tee 'translation/gen.log'
paddle usage -l 'translation/gen.log' -e $? -n "seqToseq_translation_gen" >/dev/null 2>&1
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