利用 TensorFlow 实现排序和搜索算法

原文地址 http://blog.csdn.net/gitchat/article/details/78588732

当我们提到 TensorFlow 的时候，我们仅仅只会关注它是一个很好的神经网络和深度学习的库。

但是，其实 TensorFlow 具有 tf.cond( https://www.tensorflow.org/api_docs/python/tf/cond ) 和 tf.while_loop( https://www.tensorflow.org/api_docs/python/tf/while_loop ) 函数，前者可以处理判断语句，后者可以处理循环语句，所以它也具有一般编程语言相同的表达式。

简单的说，我们可以用 C 语言或者 Python 语言实现的排序和搜索算法都可以在 TensorFlow 图中实现。

在本文中，我们就是要介绍 TensorFlow 的另一面，它的一般编程语言表达方式。我们利用 TensorFlow 图实现了一些简单算法，诸如 FizzBuzz 问题，线性搜索，冒泡排序 等等。

1. API 解释

1.1 类似判断语句的 API：tf.cond()

cond(    pred,    true_fn=None,    false_fn=None,    strict=False,    name=None,    fn1=None,    fn2=None)

tf.cond(...) 是一个等效于 if 语句的节点。根据其中的参数 pred 返回的布尔值来判断返回什么值，比如当参数 pred 为 true 值时，节点返回参数 true_fn 的值，当参数 pred 为 false 时，节点返回参数 false_fn 的值。但是，其中的参数 true_fn 和参数 false_fn 都是需要是 lambda 或者函数。比如：

z = tf.multiply(a, b)result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))

当 x < y 是 true 时，节点就会去执行 tf.add 操作。当 x < y 是 false 时，节点就会去执行 tf.square 操作。

接下来，我们来看一个完整的例子，如下：

x = tf.constant(2)y = tf.constant(5)def f1():    return tf.multiply(x, 17)def f2():    return tf.add(y, 23)r = tf.cond(tf.less(x, y), f1, f2)with tf.Session() as sess:    print(sess.run(r))

请注意：API 中的某些参数被忽略了，因为它们将在以后的版本中被删除。

• tf.cond — TensorFlow API r1.3(https://www.tensorflow.org/api_docs/python/tf/cond)

1.2 类似判断语句的 API： tf.while_loop()

while_loop(    cond, # Condition      body, # Process to be executed when cond is True    loop_vars, # Argument to body    shape_invariants=None,    parallel_iterations=10,    back_prop=True,    swap_memory=False,    name=None)

tf.while_loop(...) 是一个等效于 while 语句的节点。根据其中的参数 cond 的布尔值来判断是否将循环继续，比如当参数 pred 为 true 值时，节点去执行 body 中的语句，当参数 pred 为 false 时，那么退出这个函数。比如：

i = tf.constant(0)c = lambda i: tf.less(i, 10)b = lambda i: tf.add(i, 1)r = tf.while_loop(c, b, [i])

当 i < 10 时，cond 返回的值是 true，所以节点会去执行 body 中的语句。当 i == 10 时，cond 返回的值是 false，那么节点就会退出。这种执行方式和一般语言中的 while 非常像。

我们也可以将循环式表达成如下：

while(cond(loop_vars)){     loop_vars = body(loop_vars); }

接下来，我们来看一个完整的例子，如下：

import tensorflow as tfimport numpy as npdef body(x):    a = tf.random_uniform(shape=[2, 2], dtype=tf.int32, maxval=100)    b = tf.constant(np.array([[1, 2], [3, 4]]), dtype=tf.int32)    c = a + b    return tf.nn.relu(x + c)def condition(x):    return tf.reduce_sum(x) < 100x = tf.Variable(tf.constant(0, shape=[2, 2]))with tf.Session():    tf.initialize_all_variables().run()    result = tf.while_loop(condition, body, [x])    print(result.eval())

• tf.while_loop — TensorFlow API r1.3(https://www.tensorflow.org/api_docs/python/tf/while_loop)

2. 在 TensorFlow 中实现算法

2.1 Fizz Buzz 问题

请依次打印从1至100的整数，在该数能被3整除的时候，打印”Fizz”，能被5整除的时候打印”Buzz”，如果既能被3又能被5整除的时候，打印”FizzBuzz”。

import tensorflow as tfclass FizzBuzz():    def __init__(self, length=30):        self.length = length  # 程序需要执行的序列长度        self.array = tf.Variable([str(i) for i in range(1, length+1)], dtype=tf.string, trainable=False)  # 最后程序返回的结果        self.graph = tf.while_loop(self.cond, self.body, [1, self.array],)   # 对每一个值进行循环判断    def run(self):        with tf.Session() as sess:            tf.global_variables_initializer().run()            return sess.run(self.graph)        def cond(self, i, _):        return (tf.less(i, self.length+1)) # 判断是否是最后一个值    def body(self, i, _):        flow = tf.cond(            tf.equal(tf.mod(i, 15), 0),  # 如果值能被 15 整除，那么就把该位置赋值为 FizzBuzz                lambda: tf.assign(self.array[i - 1], 'FizzBuzz'),                lambda: tf.cond(tf.equal(tf.mod(i, 3), 0), # 如果值能被 3 整除，那么就把该位置赋值为 Fizz                        lambda: tf.assign(self.array[i - 1], 'Fizz'),                        lambda: tf.cond(tf.equal(tf.mod(i, 5), 0),   # 如果值能被 5 整除，那么就把该位置赋值为 Buzz                                lambda: tf.assign(self.array[i - 1], 'Buzz'),                                lambda: self.array  # 最后返回的结果                )            )        )        return (tf.add(i, 1), flow)if __name__ == '__main__':    fizzbuzz = FizzBuzz(length=50)    ix, array = fizzbuzz.run()    print(array)

输出结果：

['1' '2' 'Fizz' '4' 'Buzz' 'Fizz' '7' '8' 'Fizz' 'Buzz' '11' 'Fizz' '13' '14' 'FizzBuzz' '16' '17' 'Fizz' '19' 'Buzz' 'Fizz' '22' '23' 'Fizz' 'Buzz' '26' 'Fizz' '28' '29' 'FizzBuzz' '31' '32' 'Fizz' '34' 'Buzz' 'Fizz' '37' '38' 'Fizz' 'Buzz' '41' 'Fizz' '43' '44' 'FizzBuzz' '46' '47' 'Fizz' '49' 'Buzz']

2.2 线性搜索

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 {'close':44860,'date':42865,'markup':0.00357941834451902},        {'close':45270,'date':42866,'markup':0.00711902113459399},        {'close':44930,'date':42867,'markup':-0.000222518914107699},        {'close':45430,'date':42870,'markup':0.00888296691094826},        {'close':45170,'date':42871,'markup':-0.000663716814159292},        {'close':45390,'date':42872,'markup':0.00220799293442261},        {'close':44960,'date':42873,'markup':-0.00990971151728694},        {'close':45250,'date':42874,'markup':0.00265898515399956}    ]}

给定一个序列和一个目标值，从这个序列中找到这个目标值的位置。

import numpy as npimport tensorflow as tfclass LinearSearch():    def __init__(self, array, x):        self.x = tf.constant(x)        self.array = tf.constant(array)        self.length = len(array)        self.graph = tf.while_loop(self.cond, self.body, [0, self.x, False])    def run(self):        with tf.Session() as sess:            tf.global_variables_initializer().run()            return sess.run(self.graph)    def cond(self, i, _, is_found):        return tf.logical_and(tf.less(i, self.length), tf.logical_not(is_found))    def body(self, i, _, is_found):        return tf.cond(tf.equal(self.array[i], self.x),                    lambda: (i, self.array[i], True),                    lambda: (tf.add(i, 1), -1, False))if __name__ == '__main__':    array, x = [1, 22, 33, 1, 7, 3, 8], 3    search = LinearSearch(array, x)    ix, xx, is_found = search.run()    print('Array :', array)    print('Number to search :', x)    if is_found:        print('{} is at index {}.'.format(xx, ix))    else:        print('Not found.')

输出结果：

Array : [1, 22, 33, 1, 7, 3, 8]Number to search : 33 is at index 5.

2.3 冒泡排序

给定一个数组，利用冒泡排序进行排序，最后输出排好序的数组。冒泡排序算法可以查看这个文档(https://en.wikipedia.org/wiki/Bubble_sort)。

import numpy as npimport tensorflow as tfclass BubbleSort():    def __init__(self, array):        self.i = tf.constant(0)        self.j = tf.constant(len(array)-1)        self.array = tf.Variable(array, trainable=False)        self.length = len(array)        cond = lambda i, j, _: tf.less(i-1, self.length-1)        self.graph = tf.while_loop(cond, self.outer_loop, loop_vars=[self.i, self.j, self.array])    def run(self):        with tf.Session() as sess:            tf.global_variables_initializer().run()            return sess.run(self.graph)    def outer_loop(self, i, j, _):        cond = lambda i, j, _: tf.greater(j, i)        loop = tf.while_loop(cond, self.inner_loop, loop_vars=[i, self.length-1, self.array])        return tf.add(i, 1), loop[1], loop[2]    def inner_loop(self, i, j, _):        body = tf.cond(tf.greater(self.array[j-1], self.array[j]),                    lambda: tf.scatter_nd_update(self.array, [[j-1],[j]], [self.array[j],self.array[j-1]]),                    lambda: self.array)        return i, tf.subtract(j, 1), bodyif __name__ == '__main__':    x = np.array([1.,7.,3.,8.])    _, _, sorted_array = BubbleSort(x).run()    print(x)    print(sorted_array)

输出结果：

[ 1.  7.  3.  8.][ 1.  3.  7.  8.]

TensorFlow 还有更多的实现算法，你可以查看这个 Github(https://github.com/akimach/EsotericTensorFlow)。