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‎.gitignore‎

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.DS_Store
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*.DS_Store
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*/__pycache__/
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*__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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# custom
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.idea/
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*/.idea/
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*.idea/
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‎README.md‎

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# Amusing Python Codes
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![Authour](https://img.shields.io/badge/Author-Zhang%20Hao%20(Isaac%20Changhau)-blue.svg)
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![Author](https://img.shields.io/badge/Author-Zhang%20Hao%20(Isaac%20Changhau)-blue.svg)
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Interesting python codes to deal with some simple and practical tasks.
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- [**Stanford CoreNLP Demo**](/stanford_corenlp_demo)
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- [**Stock Prices Predict**](/stock_prices_predict)
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- [**TransX · Embedding Entities and Relationships of Multi-relational Data**](/transx)
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- [**WeChat Exploration using itchat Package**](/wechat_exploration)
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- [**WeChat Exploration using itchat Package**](/wechat_exploration)
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- [**Text Classification via Traditional Machine Learning Methods (sklearn-based)**](/text_classification_in_ml)

‎basic_word2vec_example/word2vec_basic.py‎

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import numpy as np
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import tensorflow as tf
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import os
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from data_utils import maybe_download, read_data, build_dataset, tsne_and_plot, generate_batch
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from .data_utils import maybe_download, read_data, build_dataset, tsne_and_plot, generate_batch
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # suppress tensorflow warnings
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‎batch_normalization/batch_norm_cnn_example_1.py‎

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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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from nn_functions import multiclass_log_loss
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from cnn_model import batch_norm_cnn
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from .nn_functions import multiclass_log_loss
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from .cnn_model import batch_norm_cnn
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # suppress tensorflow warnings
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‎batch_normalization/batch_norm_cnn_example_2.py‎

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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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from nn_functions import multiclass_log_loss
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from cnn_model import batch_norm_cnn
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from .nn_functions import multiclass_log_loss
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from .cnn_model import batch_norm_cnn
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # suppress tensorflow warnings
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‎batch_normalization/cnn_model.py‎

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import tensorflow as tf
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from nn_layers import Conv2dLayer, MaxPooling2dLayer, DenseLayer, SoftmaxLayer
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from nn_functions import batch_normalization
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from .nn_layers import Conv2dLayer, MaxPooling2dLayer, DenseLayer, SoftmaxLayer
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from .nn_functions import batch_normalization
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def batch_norm_cnn(x, y_, keep_prob, phase_train):

‎dmn/main.py‎

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#!/usr/bin/env python3
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import os
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import tensorflow as tf
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from read_data import read_babi, get_max_sizes
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from utils.data_utils import load_glove, WordTable
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from .read_data import read_babi, get_max_sizes
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from .utils.data_utils import load_glove, WordTable
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flags = tf.flags
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‎dmn/models/new/dmn_plus.py‎

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import tensorflow as tf
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from tensorflow.python.ops import rnn_cell
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from models.base_model import BaseModel
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from models.new.episode_module import EpisodeModule
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from utils.nn import weight, bias, dropout, batch_norm
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from ..base_model import BaseModel
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from .episode_module import EpisodeModule
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from ...utils.nn import weight, bias, dropout, batch_norm
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class DMN(BaseModel):

‎dmn/models/new/episode_module.py‎

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import tensorflow as tf
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from utils.nn import weight, bias
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from utils.attn_gru import AttnGRU
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from ...utils.nn import weight, bias
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from ...utils.attn_gru import AttnGRU
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class EpisodeModule:

‎dmn/models/old/dmn.py‎

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from tensorflow.python.ops import rnn_cell
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from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq
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from models.base_model import BaseModel
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from models.old.episode_module import EpisodeModule
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from utils.nn import weight, batch_norm, dropout
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from ..base_model import BaseModel
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from .episode_module import EpisodeModule
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from ...utils.nn import weight, batch_norm, dropout
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class DMN(BaseModel):

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