確保目錄結(jié)構(gòu)存在。每次創(chuàng)建文件，確保父目錄已經(jīng)存在。確保指定路徑全部或部分目錄已經(jīng)存在。創(chuàng)建沿指定路徑上不存在目錄。

下載函數(shù)，如果文件名未指定，從URL解析。下載文件，返回本地文件系統(tǒng)文件名。如果文件存在，不下載。如果文件未指定，從URL解析，返回filepath 。實(shí)際下載前，檢查下載位置是否有目標(biāo)名稱文件。是，跳過(guò)下載。下載文件，返回路徑。重復(fù)下載，把文件從文件系統(tǒng)刪除。

import os
import shutil
import errno
from lxml import etree
from urllib.request import urlopen


def ensure_directory(directory):
    directory = os.path.expanduser(directory)
    try:
        os.makedirs(directory)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise e

def download(url, directory, filename=None):
    if not filename:
        _, filename = os.path.split(url)
    directory = os.path.expanduser(directory)
    ensure_directory(directory)
    filepath = os.path.join(directory, filename)
    if os.path.isfile(filepath):
        return filepath
    print('Download', filepath)
    with urlopen(url) as response, open(filepath, 'wb') as file_:
        shutil.copyfileobj(response, file_)
    return filepath

磁盤緩存修飾器，較大規(guī)模數(shù)據(jù)集處理中間結(jié)果保存磁盤公共位置，緩存加載函數(shù)修飾器。Python pickle功能實(shí)現(xiàn)函數(shù)返回值序列化、反序列化。只適合能納入主存數(shù)據(jù)集。@disk_cache修飾器，函數(shù)實(shí)參傳給被修飾函數(shù)。函數(shù)參數(shù)確定參數(shù)組合是否有緩存。散列映射為文件名數(shù)字。如果是'method'，跳過(guò)第一參數(shù)，緩存filepath，'directory/basename-hash.pickle'。方法method=False參數(shù)通知修飾器是否忽略第一個(gè)參數(shù)。

import functools
import os
import pickle

def disk_cache(basename, directory, method=False):
    directory = os.path.expanduser(directory)
    ensure_directory(directory)

    def wrapper(func):
        @functools.wraps(func)
        def wrapped(*args, **kwargs):
            key = (tuple(args), tuple(kwargs.items()))
            if method and key:
                key = key[1:]
            filename = '{}-{}.pickle'.format(basename, hash(key))
            filepath = os.path.join(directory, filename)
            if os.path.isfile(filepath):
                with open(filepath, 'rb') as handle:
                    return pickle.load(handle)
            result = func(*args, **kwargs)
            with open(filepath, 'wb') as handle:
                pickle.dump(result, handle)
            return result
        return wrapped

    return wrapper
@disk_cache('dataset', '/home/user/dataset/')
def get_dataset(one_hot=True):
    dataset = Dataset('http://example.com/dataset.bz2')
    dataset = Tokenize(dataset)
    if one_hot:
        dataset = OneHotEncoding(dataset)
    return dataset

屬性字典。繼承自內(nèi)置dict類，可用屬性語(yǔ)法訪問(wèn)悠已有元素。傳入標(biāo)準(zhǔn)字典(鍵值對(duì))。內(nèi)置函數(shù)locals，返回作用域所有局部變量名值映射。

class AttrDict(dict):

    def __getattr__(self, key):
        if key not in self:
            raise AttributeError
        return self[key]

    def __setattr__(self, key, value):
        if key not in self:
            raise AttributeError
        self[key] = value

惰性屬性修飾器。外部使用。訪問(wèn)model.optimze，數(shù)據(jù)流圖創(chuàng)建新計(jì)算路徑。調(diào)用model.prediction，創(chuàng)建新權(quán)值和偏置。定義只計(jì)算一次屬性。結(jié)果保存到帶有某些前綴的函數(shù)調(diào)用。惰性屬性，TensorFlow模型結(jié)構(gòu)化、分類。

import functools

def lazy_property(function):
    attribute = '_lazy_' + function.__name__

    @property
    @functools.wraps(function)
    def wrapper(self):
        if not hasattr(self, attribute):
            setattr(self, attribute, function(self))
        return getattr(self, attribute)
    return wrapper

class Model:

    def __init__(self, data, target):
        self.data = data
        self.target = target
        self.prediction
        self.optimize
        self.error

    @lazy_property
    def prediction(self):
        data_size = int(self.data.get_shape()[1])
        target_size = int(self.target.get_shape()[1])
        weight = tf.Variable(tf.truncated_normal([data_size, target_size]))
        bias = tf.Variable(tf.constant(0.1, shape=[target_size]))
        incoming = tf.matmul(self.data, weight) + bias
        return tf.nn.softmax(incoming)

    @lazy_property
    def optimize(self):
        cross_entropy = -tf.reduce_sum(self.target, tf.log(self.prediction))
        optimizer = tf.train.RMSPropOptimizer(0.03)
        return optimizer.minimize(cross_entropy)

    @lazy_property
    def error(self):
        mistakes = tf.not_equal(
            tf.argmax(self.target, 1), tf.argmax(self.prediction, 1))
        return tf.reduce_mean(tf.cast(mistakes, tf.float32))

覆蓋數(shù)據(jù)流圖修飾器。未明確指定使用期他數(shù)據(jù)流圖，TensorFlow使用默認(rèn)。Jupyter Notebook，解釋器狀態(tài)在不同一單元執(zhí)行期間保持。初始默認(rèn)數(shù)據(jù)流圖始終存在。執(zhí)行再次定義數(shù)據(jù)流圖運(yùn)算單元，添加到已存在數(shù)據(jù)流圖。根據(jù)菜單選項(xiàng)重新啟動(dòng)kernel，再次運(yùn)行所有單元。
創(chuàng)建定制數(shù)據(jù)流圖，設(shè)置默認(rèn)。所有運(yùn)算添加到該數(shù)據(jù)流圖，再次運(yùn)行單元，創(chuàng)建新數(shù)據(jù)流圖。舊數(shù)據(jù)流圖自動(dòng)清理。
修飾器中創(chuàng)建數(shù)據(jù)流圖，修飾主函數(shù)。主函數(shù)定義完整數(shù)據(jù)流圖，定義占位符，調(diào)用函數(shù)創(chuàng)建模型。

import functools
import tensorflow as tf

def overwrite_graph(function):
    @functools.wraps(function)
    def wrapper(*args, **kwargs):
        with tf.Graph().as_default():
            return function(*args, **kwargs)
    return wrapper
@overwrite_graph
def main():
    data = tf.placeholder(...)
    target = tf.placeholder(...)
    model = Model()

main()

API文檔，編寫代碼時(shí)參考：
https://www.tensorflow.org/versions/master/api_docs/index.html
Github庫(kù)，跟蹤TensorFlow最新功能特性，閱讀拉拽請(qǐng)求(pull request)、問(wèn)題(issues)、發(fā)行記錄(release note)：
https://github.com/tensorflow/tensorflow
分布式 TensorFlow：
https://www.tensorflow.org/versions/master/how_tos/distributed/index.html
構(gòu)建新TensorFlow功能：
https://www.tensorflow.org/master/how_tos/adding_an_op/index.html
郵件列表：
https://groups.google.com/a/tensorflow.org/d/forum/discuss
StackOverflow：
http://stackoverflow.com/questions/tagged/tensorflow
代碼：
https://github.com/backstopmedia/tensorflowbook

參考資料：
《面向機(jī)器智能的TensorFlow實(shí)踐》

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