我试图在张量流中插入一维张量（我实际上想要相当于np.interp）。由于我无法找到类似的张量流op，我不得不自己执行插值。
第一步是在y值中搜索相应索引的x值的排序列表，即执行二分搜索。我尝试使用while循环，但我得到了一个神秘的运行时错误。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时使用tensorflow 1.1。知道什么是错的吗？ 感谢。
这里是完整的堆栈跟踪：
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'
答案 0 :(得分：2)
我不知道您的错误来源，但我可以告诉您，tf.while_loop很可能非常慢。您可以实现没有循环的线性插值，如下所示：
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
填充部分只是为了避免在超出范围时传递值时出现问题，但除此之外只需要比较和查找值开始大于query的位置。
答案 1 :(得分：0)
发现问题 - tensorflow不喜欢python整数作为cond的参数 - 它需要首先包装在常量中。此代码有效：
with tf.name_scope("binsearch"):
m_one = tf.constant(-1, dtype=tf.int32, name='minus_one')
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):
def fn1():
return mid, down, (mid+down)//2, tf.cond(tf.gather(xaxis, mid-1)<query, lambda:mid-1, lambda: m_one)
def fn2():
return up, mid, (up+mid)//2, tf.cond(tf.gather(xaxis, mid+1)>query, lambda:mid, lambda: m_one)
return tf.cond(tf.gather(xaxis, mid)>query, fn1, fn2 )
up, down, mid, done = tf.while_loop(cond, body, (xaxis.shape[0]-1, 0, (xaxis.shape[0]-1)//2, -1))