Getting Error in tf.cast while implementing CGAN

Question

Question

HI, I am implementing CGAN in TensorFlow. Please help me understand the cause of the error and how to resolve it.

Code

def train(dataset, epochs):   for epoch in range(epochs):
    start = time.time()
    for image_batch in dataset:
      img = tf.cast(image_batch, tf.float32)
      imgs = normalization(img)
      train_step(imgs,target)

    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))


train(ds, 200)

Normalization Function Code:

@tf.function
def normalization(tensor):
    tensor = tf.image.resize(
    tensor, (128,128))
    tensor = tf.subtract(tf.divide(tensor, 127.5), 1)
    return tensor

Error

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-24-bf631b0ce503> in <module>
     10 
     11 
---> 12 train(ds, 200)

<ipython-input-24-bf631b0ce503> in train(dataset, epochs)
      3     start = time.time()
      4     for image_batch in dataset:
----> 5       img = tf.cast(image_batch, tf.float32)
      6       imgs = normalization(img)
      7       train_step(imgs,target)

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
    199     """Call target, and fall back on dispatchers if there is a TypeError."""
    200     try:
--> 201       return target(*args, **kwargs)
    202     except (TypeError, ValueError):
    203       # Note: convert_to_eager_tensor currently raises a ValueError, not a

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py in cast(x, dtype, name)
    919       # allows some conversions that cast() can't do, e.g. casting numbers to
    920       # strings.
--> 921       x = ops.convert_to_tensor(x, name="x")
    922       if x.dtype.base_dtype != base_type:
    923         x = gen_math_ops.cast(x, base_type, name=name)

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
   1497 
   1498     if ret is None:
-> 1499       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
   1500 
   1501     if ret is NotImplemented:

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in _autopacking_conversion_function(v, dtype, name, as_ref)
   1500   elif dtype != inferred_dtype:
   1501     v = nest.map_structure(_cast_nested_seqs_to_dtype(dtype), v)
-> 1502   return _autopacking_helper(v, dtype, name or "packed")
   1503 
   1504 

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in _autopacking_helper(list_or_tuple, dtype, name)
   1406     # checking.
   1407     if all(isinstance(elem, core.Tensor) for elem in list_or_tuple):
-> 1408       return gen_array_ops.pack(list_or_tuple, name=name)
   1409   must_pack = False
   1410   converted_elems = []

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py in pack(values, axis, name)
   6456       return _result
   6457     except _core._NotOkStatusException as e:
-> 6458       _ops.raise_from_not_ok_status(e, name)
   6459     except _core._FallbackException:
   6460       pass

E:\Users\Asus\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in raise_from_not_ok_status(e, name)
   6841   message = e.message + (" name: " + name if name is not None else "")
   6842   # pylint: disable=protected-access
-> 6843   six.raise_from(core._status_to_exception(e.code, message), None)
   6844   # pylint: enable=protected-access
   6845 

E:\Users\Asus\anaconda3\lib\site-packages\six.py in raise_from(value, from_value)

**InvalidArgumentError: cannot compute Pack as input #1(zero-based) was expected to be a uint8 tensor but is a int64 tensor [Op:Pack] name: x**

Attempts I made in resolving this issue:

I tried changing

tensor = tf.subtract(tf.divide(tensor, 127.5), 1) 

to

tensor = tf.subtract(tf.divide(tensor, 127), 1)

Moreover,

img = tf.cast(image_batch, tf.float32)

to

img = tf.cast(image_batch, tf.int64)

But the error remained same. Any help shall be highly appreciated.