作为变量传递的Tensorflow内置函数不起作用。我怎样才能使它工作？

Question

当我运行下面的代码，

import numpy as np 
import tensorflow as tf 

class Config: 
    activation = tf.nn.tanh 

class Sample: 

    def function(self, x): 
     return self.config.activation(x) 

    def __init__(self, config): 
     self.config = config 

if __name__ == "__main__": 
    with tf.Graph().as_default(): 
     config = Config() 
     sample = Sample(config) 
     with tf.Session() as sess: 
      a = tf.constant(2.0) 
      print sess.run(sample.function(a)) 

我收到此错误信息：

Traceback (most recent call last): 
    File "test.py", line 27, in <module> 
    print sess.run(sample.function(a)) 
    File "test.py", line 11, in function 
    return self.config.activation(x) 
    File "/Users/byungwookang/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 2019, in tanh 
    with ops.name_scope(name, "Tanh", [x]) as name: 
    File "/Users/byungwookang/anaconda/lib/python2.7/contextlib.py", line 17, in __enter__ 
    return self.gen.next() 
    File "/Users/byungwookang/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 4185, in name_scope 
    with g.as_default(), g.name_scope(n) as scope: 
    File "/Users/byungwookang/anaconda/lib/python2.7/contextlib.py", line 17, in __enter__ 
    return self.gen.next() 
    File "/Users/byungwookang/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2839, in name_scope 
    if name: 
    File "/Users/byungwookang/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 541, in __nonzero__ 
    raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. " 
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor. 

相反，该代码按预期工作。

import numpy as np 
import tensorflow as tf 

class Config: 
    activation = np.tanh 

class Sample: 

    def function(self, x): 
     return self.config.activation(x) 

    def __init__(self, config): 
     self.config = config 

if __name__ == "__main__": 
    config = Config() 
    sample = Sample(config) 
    print sample.function(2.0) 
    print np.tanh(2.0) 

它给

0.964027580076 
0.964027580076 

我很好奇，为什么一个不能越过tensorflow内置函数作为变量（如在第一代码完成以上），以及是否有办法以避免上述错误。特别是在第二个代码中，numpy函数作为变量很好地传递，我感到tensorflow不允许这样做。

Answer 1

你的东西不工作的原因是因为你的情况

print sample.function # <bound method Sample.function of <__main__.Sample instance at 0xXXX>> 
print tf.nn.tanh  # <function tanh at 0xXXX> 

是不一样的，而在第二个情况下，他们相匹配。所以当你运行sample.function(a)时，不是tanh，而是其他的东西被执行。

---

这是我很难理解所有这些类和功能做一个简单的工作的目的，所以我才发现修改任何写最简单的方法，它的工作：

import numpy as np 
import tensorflow as tf 

def config(): 
    return {'activation': tf.nn.tanh} 

class Sample: 
    def function(self, x): 
     return self.config['activation'](x) 

    def __init__(self, config): 
     self.config = config 

if __name__ == "__main__": 
    with tf.Graph().as_default(): # this is also not needed 
     sample = Sample(config()) 
     with tf.Session() as sess: 
      a = tf.constant(2.0) 
      print sess.run(sample.function(a))