I don't think you can unpack
a tensor with the argument num
unspecified and non-inferrable. As their documentation says:
Raises ValueError if num
is unspecified and cannot be inferred.
It has something to do with how TensorFlow's internal design for operations like unpack
. In this other tread, Yaroslav Bulatov explained
Operations like unpack
compile into "tensor-in/tensor-out" ops during graph construction time.
Hence TensorFlow needs to know the specific value of num
to pass compiling.
Although, I'd try to get around this by using TensorArray. (see the following code for illustration).
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
# assume vector_size=2 for simplicity
x = tf.placeholder(tf.int32, shape=[None, 2])
TensorArr = tf.TensorArray(tf.int32, 1, dynamic_size=True, infer_shape=False)
x_array = TensorArr.unpack(x)
TensorArray
is a class for wrapping dynamically sized arrays of Tensors. When initialize a TensorArray
object in this application, TensorArr = tf.TensorArray(tf.int32, 1, dynamic_size=True, infer_shape=False)
, set dynamic_size=True
and infer_shape=False
since the shape of placeholder x
is only partly defined.
To access each unpacked element:
# access the first element
x_elem0 = x_array.read(0)
# access the last element
last_idx = tf.placeholder(tf.int32)
x_last_elem = x_array.read(last_idx)
Then at evaluation time:
# generate random numpy array
dim0 = 4
x_np = np.random.randint(0, 25, size=[dim0, 2])
print x_np
# output of print x_np
[[17 15]
[17 19]
[ 3 0]
[ 4 13]]
feed_dict = {x : x_np, last_idx : dim0-1} #python 0 based indexing
x_elem0.eval(feed_dict=feed_dict)
array([17, 15], dtype=int32) #output of x_elem0.eval(feed_dict)
x_last_elem.eval(feed_dict=feed_dict)
array([ 4, 13], dtype=int32) #output of x_last_elem.eval(feed_dict)
sess.close()
Note that when trying to access each unpacked element, if the index
value is out of bound, you'd be able to pass the compiling but you'll get an error during runtime suggesting index out of bound. Additionally, the shape of the unpacked tensor would be TensorShape(None)
, since the shape of x
is only partially determined until being evaluated.