Python Tensorflow-使用张量作为索引

我想使用向后累积和函数：

def _backwards_cumsum(x, length, batch_size):

upper_triangular_ones = np.float32(np.triu(np.ones((length, length))))
repeated_tri = np.float32(np.kron(np.eye(batch_size), upper_triangular_ones))
return tf.matmul(repeated_tri,
                  tf.reshape(x, [length, 1]))

但是，长度是一个占位符：

length = tf.placeholder("int32" ,name = 'xx')

所以每次它得到一个新值，然后开始计算_向后_总和

尝试运行该函数后，我出现了一个错误：

TypeError: 'Tensor' object cannot be interpreted as an index

完整回溯：

{
TypeError                                 Traceback (most recent call last)
<ipython-input-561-970ae9e96aa1> in <module>()
----> 1 rewards = _backwards_cumsum(tf.reshape(tf.reshape(decays,[-1,1]) * tf.sigmoid(disc_pred_gen_ph), [-1]), _maxx, batch_size)

<ipython-input-546-5c6928fac357> in _backwards_cumsum(x, length, batch_size)
      1 def _backwards_cumsum(x, length, batch_size):
      2 
----> 3     upper_triangular_ones = np.float32(np.triu(np.ones((length, length))))
      4     repeated_tri = np.float32(np.kron(np.eye(batch_size), upper_triangular_ones))
      5     return tf.matmul(repeated_tri,

/Users/onivron/anaconda/envs/tensorflow/lib/python2.7/site-packages/numpy/core/numeric.pyc in ones(shape, dtype, order)
    190 
    191     """
--> 192     a = empty(shape, dtype, order)
    193     multiarray.copyto(a, 1, casting='unsafe')
    194     return a

其中_maxx与上面的长度占位符相同

---

有解决办法吗

该错误与您无意中用于numpy数组的张量对象有关：length。在tensorflow中使用numpy功能的最佳方法是使用tf.py_func

---

没有完整的回溯很难判断。是Python解释器错误吗？TensorFlow运行时错误？主要的问题是，我想让不同的长度影响重复的三次方。那么问题是什么，这段代码支持不同的长度。

# Define a new function that only depends on numpy/any non tensorflow graph object

def get_repeated_tri(length, batch_size):
    upper_triangular_ones = np.float32(np.triu(np.ones((length, length))))
    repeated_tri = np.float32(np.kron(np.eye(batch_size), upper_triangular_ones))
    return repeated_tri
# Here length and batch size must be non tensor object
repeated_tri = tf.py_func(get_repeated_tri, [length, batch_size], tf.int32)
# there're some size mismacthes also in your code `tf.matmul`
def _backwards_cumsum(repeated_tri, x, length_, batch_size):
    return tf.matmul(repeated_tri, tf.reshape(x, [length_*batch_size, -1]))
length_ = tf.placeholder(tf.int32, name='length')
# also define length, batch_size as nump constants
# x as tensorflow tensor
some_tensor_out= _backwards_cumsum(repeated_tri, x, length_, batch_size)

some_tensor_out_ = sess.run(some_tensor_out, {length_:length})