Class Model

class Model:

    def __init__(self, data, target):
        data_size = int(data.get_shape()[1])
        target_size = int(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(data, weight) + bias
        self._prediction = tf.nn.softmax(incoming)
        cross_entropy = -tf.reduce_sum(target, tf.log(self._prediction))
        self._optimize = tf.train.RMSPropOptimizer(0.03).minimize(cross_entropy)
        mistakes = tf.not_equal(
            tf.argmax(target, 1), tf.argmax(self._prediction, 1))
        self._error = tf.reduce_mean(tf.cast(mistakes, tf.float32))

    @property
    def prediction(self):
        return self._prediction

    @property
    def optimize(self):
        return self._optimize

    @property
    def error(self):
        return self._error

@property裝飾器可以將類函數(shù)與其屬性相關(guān)聯(lián)。但是以上的方法可讀性和復(fù)利用性太差。

Use property

class Model:

    def __init__(self, data, target):
        self.data = data
        self.target = target
        self._prediction = None
        self._optimize = None
        self._error = None

    @property
    def prediction(self):
        if not self._prediction:
            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
            self._prediction = tf.nn.softmax(incoming)
        return self._prediction

    @property
    def optimize(self):
        if not self._optimize:
            cross_entropy = -tf.reduce_sum(self.target, tf.log(self.prediction))
            optimizer = tf.train.RMSPropOptimizer(0.03)
            self._optimize = optimizer.minimize(cross_entropy)
        return self._optimize

    @property
    def error(self):
        if not self._error:
            mistakes = tf.not_equal(
                tf.argmax(self.target, 1), tf.argmax(self.prediction, 1))
            self._error = tf.reduce_mean(tf.cast(mistakes, tf.float32))
        return self._error

Emmm 存在lazy_loading 的問題 ， 這里還不太理解。
Code is still a bit bloated due to the lazy loading logic

Lazy Property Decorator

import functools

def  lazy_property(function):
    attribute = '_cache_'  +  function.__name__
    
    @property
    @functools.wrap(function)
    def  decorator(self, attribute):
        if not hasattr(self, attribute):
            setattr(self, attribute, function(self))
        return getattr(self , attribute)

    return decorator

裝飾器——以后翻譯

通過這個(gè)裝飾器可以簡(jiǎn)化模型

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))

Note that we mention the properties in the constructor. This way the full graph is ensured to be defined by the time we run tf.initialize_variables().

定義計(jì)算圖的范圍

同過函數(shù)定義

import functools

def define_scope(function):
    attribute = '_cache_' + function.__name__

    @property
    @functools.wraps(function)
    def decorator(self):
        if not hasattr(self, attribute):
            with tf.variable_scope(function.__name__):
                setattr(self, attribute, function(self))
        return getattr(self, attribute)

    return decorator

插入tf.variable_scope(function.name) 或者 tf.name_scope(function.name) 來定義Scope。

自定義Scope

def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without parentheses
    if not arguments are provided. All arguments must be optional.
    """
    @functools.wraps(function)
    def decorator(*args, **kwargs):
        if len(args)  == 1 and len(kwargs) == 0 and callable(args[0]):
            return function(args[0])
        else:
            return lambda wrapee: function(wrapee, *args, **kwargs)
    return decorator

@doublewrap
def define_scope(function, scope=None, *args, **kwargs):
    """
     A decorator for functions that define TensorFlow operations. The wrapped
    function will only be executed once. Subsequent calls to it will directly
    return the result so that operations are added to the graph only once.
    The operations added by the function live within a tf.variable_scope(). If
    this decorator is used with arguments, they will be forwarded to the
    variable scope. The scope name defaults to the name of the wrapped
    function
    """
    attribute = '_cache_' + function.__name__
    name = scope or function.__name__

    @property
    @functools.wraps(function)
    def decorator(self):
        if not hasattr(self, attribute):
            with tf.variable_scope(name, *args, **kwargs):
                setattr(self, attribute, function(self))
        return getattr(self, attribute)

    return decorator

雙層裝飾器以保證無參時(shí)也可照常調(diào)用。

本文參考翻譯自 Danijar Hafner