python之tensorflow 中的二进制搜索和插值

我正在尝试在 tensorflow 中插入一维张量(我实际上想要 np.interp 的等价物)。由于找不到类似的 tensorflow 操作，我不得不自己执行插值。

第一步是在已排序的 x 值列表中搜索 y 值中的相应索引，即执行二分查找。我尝试为此使用 while 循环，但出现神秘的运行时错误。这是一些代码:

xaxis = tf.placeholder(tf.float32, shape=100, name='xaxis') 
query = tf.placeholder(tf.float32, name='query') 
 
with tf.name_scope("binsearch"): 
    up   = tf.Variable(0, dtype=tf.int32, name='up') 
    mid  = tf.Variable(0, dtype=tf.int32, name='mid') 
    down = tf.Variable(0, dtype=tf.int32, name='down') 
    done = tf.Variable(-1, dtype=tf.int32, name='done')            
 
    def cond(up, down, mid, done): 
        return tf.logical_and(done<0,up-down>1) 
 
    def body(up, down, mid, done): 
        val  = tf.gather(xaxis, mid) 
        done = tf.cond(val>query,  
                       tf.cond(tf.gather(xaxis, mid-1)<query, lambda:mid-1, lambda: -1),  
                       tf.cond(tf.gather(xaxis, mid+1)>query, lambda:mid, lambda: -1) ) 
        up = tf.cond(val>query, lambda: mid, lambda: up ) 
        down = tf.cond(val<query, lambda: mid, lambda: down ) 
 
        with tf.control_dependencies([done, up, down]): 
            return up, down, (up+down)//2, done 
 
    up, down, mid, done = tf.while_loop(cond, body, (xaxis.shape[0]-1, 0, (xaxis.shape[0]-1)//2, -1)) 

这导致

AttributeError: 'int' object has no attribute 'name' 

我在 Windows 7 上使用 Python 3.6，在支持 gpu 的情况下使用 tensorflow 1.1。知道出了什么问题吗？ 谢谢。

这是完整的堆栈跟踪:

AttributeError                            Traceback (most recent call last) 
<ipython-input-185-693d3873919c> in <module>() 
     19             return up, down, (up+down)//2, done 
     20  
---> 21     up, down, mid, done = tf.while_loop(cond, body, (xaxis.shape[0]-1, 0, (xaxis.shape[0]-1)//2, -1)) 
 
c:\program files\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name) 
   2621     context = WhileContext(parallel_iterations, back_prop, swap_memory, name) 
   2622     ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, context) 
-> 2623     result = context.BuildLoop(cond, body, loop_vars, shape_invariants) 
   2624     return result 
   2625  
 
c:\program files\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants) 
   2454       self.Enter() 
   2455       original_body_result, exit_vars = self._BuildLoop( 
-> 2456           pred, body, original_loop_vars, loop_vars, shape_invariants) 
   2457     finally: 
   2458       self.Exit() 
 
c:\program files\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants) 
   2404         structure=original_loop_vars, 
   2405         flat_sequence=vars_for_body_with_tensor_arrays) 
-> 2406     body_result = body(*packed_vars_for_body) 
   2407     if not nest.is_sequence(body_result): 
   2408       body_result = [body_result] 
 
<ipython-input-185-693d3873919c> in body(up, down, mid, done) 
     11         val  = tf.gather(xaxis, mid) 
     12         done = tf.cond(val>query,  
---> 13                        tf.cond(tf.gather(xaxis, mid-1)<query, lambda:mid-1, lambda: -1), 
     14                        tf.cond(tf.gather(xaxis, mid+1)>query, lambda:mid, lambda: -1) ) 
     15         up = tf.cond(val>query, lambda: mid, lambda: up ) 
 
c:\program files\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in cond(pred, fn1, fn2, name) 
   1746     context_f = CondContext(pred, pivot_2, branch=0) 
   1747     context_f.Enter() 
-> 1748     _, res_f = context_f.BuildCondBranch(fn2) 
   1749     context_f.ExitResult(res_f) 
   1750     context_f.Exit() 
 
c:\program files\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in BuildCondBranch(self, fn) 
   1666               real_v = sparse_tensor.SparseTensor(indices, values, dense_shape) 
   1667           else: 
-> 1668             real_v = self._ProcessOutputTensor(v) 
   1669         result.append(real_v) 
   1670     return original_r, result 
 
c:\program files\python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py in _ProcessOutputTensor(self, val) 
   1624     """Process an output tensor of a conditional branch.""" 
   1625     real_val = val 
-> 1626     if val.name not in self._values: 
   1627       # Handle the special case of lambda: x 
   1628       self._values.add(val.name) 
 
AttributeError: 'int' object has no attribute 'name' 

请您参考如下方法:

我不知道你的错误来源，但我可以告诉你 tf.while_loop很可能会很慢。您可以像这样在没有循环的情况下实现线性插值:

import numpy as np 
import tensorflow as tf 
 
xaxis = tf.placeholder(tf.float32, shape=100, name='xaxis') 
yaxis = tf.placeholder(tf.float32, shape=100, name='yaxis') 
query = tf.placeholder(tf.float32, name='query') 
 
# Add additional elements at the beginning and end for extrapolation 
xaxis_pad = tf.concat([[tf.minimum(query - 1, xaxis[0])], xaxis, [tf.maximum(query + 1, xaxis[-1])]], axis=0) 
yaxis_pad = tf.concat([yaxis[:1], yaxis, yaxis[-1:]], axis=0) 
 
# Find the index of the interval containing query 
cmp = tf.cast(query >= xaxis_pad, dtype=tf.int32) 
diff = cmp[1:] - cmp[:-1] 
idx = tf.argmin(diff) 
 
# Interpolate 
alpha = (query - xaxis_pad[idx]) / (xaxis_pad[idx + 1] - xaxis_pad[idx]) 
res = alpha * yaxis_pad[idx + 1] + (1 - alpha) * yaxis_pad[idx] 
 
# Test with f(x) = 2 * x 
q = 5.4 
x = np.arange(100) 
y = 2 * x 
with tf.Session() as sess: 
    q_interp = sess.run(res, feed_dict={xaxis: x, yaxis: y, query: q}) 
print(q_interp) 
>>> 10.8 

填充部分只是为了在传递超出范围的值时避免麻烦，否则它只是比较和查找值开始大于 query 的位置的问题。 .