liaogang
8 years ago
commit
ec8e2108a4
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# Locales
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export LC_ALL=en_US.UTF-8
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export LANG=en_US.UTF-8
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export LANGUAGE=en_US.UTF-8
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# Aliases
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alias rm='rm -i'
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alias cp='cp -i'
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alias mv='mv -i'
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|
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alias ls='ls -hFG'
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alias l='ls -lF'
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alias ll='ls -alF'
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alias lt='ls -ltrF'
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alias ll='ls -alF'
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alias lls='ls -alSrF'
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alias llt='ls -altrF'
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# Colorize directory listing
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alias ls="ls -ph --color=auto"
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# Colorize grep
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if echo hello|grep --color=auto l >/dev/null 2>&1; then
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export GREP_OPTIONS="--color=auto" GREP_COLOR="1;31"
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fi
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# Shell
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export CLICOLOR="1"
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YELLOW="\[\033[1;33m\]"
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NO_COLOUR="\[\033[0m\]"
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GREEN="\[\033[1;32m\]"
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WHITE="\[\033[1;37m\]"
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source ~/.scripts/git-prompt.sh
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export PS1="\[\033[1;33m\]λ $WHITE\h $GREEN\w$YELLOW\$(__git_ps1 \" \[\033[35m\]{\[\033[36m\]%s\[\033[35m\]}\")$NO_COLOUR "
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# Git
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source ~/.scripts/git-completion.sh
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[user]
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name =
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email =
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[alias]
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st = status --branch --short
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ci = commit
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br = branch
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co = checkout
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df = diff
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l = log --pretty=format:\"%h %ad | %s%d [%an]\" --graph --date=short
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ll = log --stat
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[merge]
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tool = vimdiff
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|
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[core]
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excludesfile = ~/.gitignore
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editor = vim
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[color]
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branch = auto
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diff = auto
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status = auto
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[color "branch"]
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current = yellow reverse
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local = yellow
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remote = green
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[color "diff"]
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meta = yellow bold
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frag = magenta bold
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old = red bold
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new = green bold
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[color "status"]
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added = yellow
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changed = green
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untracked = cyan
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[push]
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default = matching
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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from paddle.trainer_config_helpers.default_decorators import wrap_name_default
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import paddle.trainer_config_helpers as conf_helps
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class Layer(object):
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def __init__(self, name=None, parent_layers=None):
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assert isinstance(parent_layers, dict)
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self.name = name
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self.__parent_layers__ = parent_layers
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def to_proto(self, context):
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"""
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function to set proto attribute
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"""
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kwargs = dict()
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for layer_name in self.__parent_layers__:
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if not isinstance(self.__parent_layers__[layer_name],
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collections.Sequence):
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v1_layer = self.__parent_layers__[layer_name].to_proto(
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context=context)
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else:
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v1_layer = map(lambda x: x.to_proto(context=context),
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self.__parent_layers__[layer_name])
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kwargs[layer_name] = v1_layer
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if self.name is None:
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return self.to_proto_impl(**kwargs)
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elif self.name not in context:
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context[self.name] = self.to_proto_impl(**kwargs)
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return context[self.name]
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def to_proto_impl(self, **kwargs):
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raise NotImplementedError()
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def __convert_to_v2__(method_name, parent_names, is_default_name=True):
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if is_default_name:
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wrapper = wrap_name_default(name_prefix=method_name)
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else:
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wrapper = None
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class V2LayerImpl(Layer):
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def __init__(self, **kwargs):
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parent_layers = dict()
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other_kwargs = dict()
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for pname in parent_names:
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if kwargs.has_key(pname):
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parent_layers[pname] = kwargs[pname]
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for key in kwargs.keys():
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if key not in parent_names:
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other_kwargs[key] = kwargs[key]
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name = kwargs.get('name', None)
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super(V2LayerImpl, self).__init__(name, parent_layers)
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self.__other_kwargs__ = other_kwargs
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if wrapper is not None:
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__init__ = wrapper(__init__)
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def to_proto_impl(self, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__other_kwargs__:
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args[each] = self.__other_kwargs__[each]
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return getattr(conf_helps, method_name)(**args)
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return V2LayerImpl
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@ -0,0 +1,120 @@
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# /usr/bin/env python
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# -*- coding:utf-8 -*-
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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
|
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# limitations under the License.
|
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"""
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IMDB dataset: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
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"""
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import paddle.v2.dataset.common
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import tarfile
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import Queue
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import re
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import string
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import threading
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__all__ = ['build_dict', 'train', 'test']
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URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz'
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MD5 = '7c2ac02c03563afcf9b574c7e56c153a'
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# Read files that match pattern. Tokenize and yield each file.
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def tokenize(pattern):
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with tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb',
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MD5)) as tarf:
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# Note that we should use tarfile.next(), which does
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# sequential access of member files, other than
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# tarfile.extractfile, which does random access and might
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# destroy hard disks.
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tf = tarf.next()
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while tf != None:
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if bool(pattern.match(tf.name)):
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# newline and punctuations removal and ad-hoc tokenization.
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yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
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None, string.punctuation).lower().split()
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tf = tarf.next()
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def build_dict(pattern, cutoff):
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word_freq = {}
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for doc in tokenize(pattern):
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for word in doc:
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paddle.v2.dataset.common.dict_add(word_freq, word)
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# Not sure if we should prune less-frequent words here.
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word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
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dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
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words, _ = list(zip(*dictionary))
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word_idx = dict(zip(words, xrange(len(words))))
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word_idx['<unk>'] = len(words)
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return word_idx
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def reader_creator(pos_pattern, neg_pattern, word_idx, buffer_size):
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UNK = word_idx['<unk>']
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qs = [Queue.Queue(maxsize=buffer_size), Queue.Queue(maxsize=buffer_size)]
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def load(pattern, queue):
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for doc in tokenize(pattern):
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queue.put(doc)
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queue.put(None)
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def reader():
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# Creates two threads that loads positive and negative samples
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# into qs.
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t0 = threading.Thread(
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target=load, args=(
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pos_pattern,
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qs[0], ))
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t0.daemon = True
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t0.start()
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t1 = threading.Thread(
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target=load, args=(
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neg_pattern,
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qs[1], ))
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t1.daemon = True
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t1.start()
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# Read alternatively from qs[0] and qs[1].
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i = 0
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doc = qs[i].get()
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while doc != None:
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yield [word_idx.get(w, UNK) for w in doc], i % 2
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i += 1
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doc = qs[i % 2].get()
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# If any queue is empty, reads from the other queue.
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i += 1
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doc = qs[i % 2].get()
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while doc != None:
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yield [word_idx.get(w, UNK) for w in doc], i % 2
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doc = qs[i % 2].get()
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return reader()
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def train(word_idx):
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return reader_creator(
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re.compile("aclImdb/train/pos/.*\.txt$"),
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re.compile("aclImdb/train/neg/.*\.txt$"), word_idx, 1000)
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||||
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def test(word_idx):
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return reader_creator(
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re.compile("aclImdb/test/pos/.*\.txt$"),
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re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000)
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@ -0,0 +1,79 @@
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"""
|
||||
imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/
|
||||
"""
|
||||
import paddle.v2.dataset.common
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import tarfile
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||||
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||||
__all__ = ['train', 'test']
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||||
|
||||
URL = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz'
|
||||
MD5 = '30177ea32e27c525793142b6bf2c8e2d'
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||||
|
||||
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||||
def word_count(f, word_freq=None):
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||||
add = paddle.v2.dataset.common.dict_add
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||||
if word_freq == None:
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||||
word_freq = {}
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||||
|
||||
for l in f:
|
||||
for w in l.strip().split():
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add(word_freq, w)
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add(word_freq, '<s>')
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||||
add(word_freq, '<e>')
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||||
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||||
return word_freq
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||||
|
||||
|
||||
def build_dict(train_filename, test_filename):
|
||||
with tarfile.open(
|
||||
paddle.v2.dataset.common.download(
|
||||
paddle.v2.dataset.imikolov.URL, 'imikolov',
|
||||
paddle.v2.dataset.imikolov.MD5)) as tf:
|
||||
trainf = tf.extractfile(train_filename)
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testf = tf.extractfile(test_filename)
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||||
word_freq = word_count(testf, word_count(trainf))
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||||
|
||||
TYPO_FREQ = 50
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word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
|
||||
|
||||
dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
|
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words, _ = list(zip(*dictionary))
|
||||
word_idx = dict(zip(words, xrange(len(words))))
|
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word_idx['<unk>'] = len(words)
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||||
|
||||
return word_idx
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||||
|
||||
|
||||
word_idx = {}
|
||||
|
||||
|
||||
def reader_creator(filename, n):
|
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global word_idx
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||||
if len(word_idx) == 0:
|
||||
word_idx = build_dict('./simple-examples/data/ptb.train.txt',
|
||||
'./simple-examples/data/ptb.valid.txt')
|
||||
|
||||
def reader():
|
||||
with tarfile.open(
|
||||
paddle.v2.dataset.common.download(
|
||||
paddle.v2.dataset.imikolov.URL, 'imikolov',
|
||||
paddle.v2.dataset.imikolov.MD5)) as tf:
|
||||
f = tf.extractfile(filename)
|
||||
|
||||
UNK = word_idx['<unk>']
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||||
for l in f:
|
||||
l = ['<s>'] + l.strip().split() + ['<e>']
|
||||
if len(l) >= n:
|
||||
l = [word_idx.get(w, UNK) for w in l]
|
||||
for i in range(n, len(l) + 1):
|
||||
yield tuple(l[i - n:i])
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||||
|
||||
return reader
|
||||
|
||||
|
||||
def train(n):
|
||||
return reader_creator('./simple-examples/data/ptb.train.txt', n)
|
||||
|
||||
|
||||
def test(n):
|
||||
return reader_creator('./simple-examples/data/ptb.valid.txt', n)
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||||
@ -0,0 +1,43 @@
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||||
import paddle.v2.dataset.imdb
|
||||
import unittest
|
||||
import re
|
||||
|
||||
TRAIN_POS_PATTERN = re.compile("aclImdb/train/pos/.*\.txt$")
|
||||
TRAIN_NEG_PATTERN = re.compile("aclImdb/train/neg/.*\.txt$")
|
||||
TRAIN_PATTERN = re.compile("aclImdb/train/.*\.txt$")
|
||||
|
||||
TEST_POS_PATTERN = re.compile("aclImdb/test/pos/.*\.txt$")
|
||||
TEST_NEG_PATTERN = re.compile("aclImdb/test/neg/.*\.txt$")
|
||||
TEST_PATTERN = re.compile("aclImdb/test/.*\.txt$")
|
||||
|
||||
|
||||
class TestIMDB(unittest.TestCase):
|
||||
word_idx = None
|
||||
|
||||
def test_build_dict(self):
|
||||
if self.word_idx == None:
|
||||
self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
|
||||
150)
|
||||
|
||||
self.assertEqual(len(self.word_idx), 7036)
|
||||
|
||||
def check_dataset(self, dataset, expected_size):
|
||||
if self.word_idx == None:
|
||||
self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
|
||||
150)
|
||||
|
||||
sum = 0
|
||||
for l in dataset(self.word_idx):
|
||||
self.assertEqual(l[1], sum % 2)
|
||||
sum += 1
|
||||
self.assertEqual(sum, expected_size)
|
||||
|
||||
def test_train(self):
|
||||
self.check_dataset(paddle.v2.dataset.imdb.train, 25000)
|
||||
|
||||
def test_test(self):
|
||||
self.check_dataset(paddle.v2.dataset.imdb.test, 25000)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,20 @@
|
||||
import paddle.v2.dataset.imikolov
|
||||
import unittest
|
||||
|
||||
|
||||
class TestMikolov(unittest.TestCase):
|
||||
def check_reader(self, reader, n):
|
||||
for l in reader():
|
||||
self.assertEqual(len(l), n)
|
||||
|
||||
def test_train(self):
|
||||
n = 5
|
||||
self.check_reader(paddle.v2.dataset.imikolov.train(n), n)
|
||||
|
||||
def test_test(self):
|
||||
n = 5
|
||||
self.check_reader(paddle.v2.dataset.imikolov.test(n), n)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
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