在将模型移动到急切执行时，我使用gradient_tape进行反向传播时遇到错误。虽然我可以告诉所有操作都在GPU上进行，但在后面的支持期间我得到以下错误：
File "tf_registration_continuous.py", line 128, in single_registration_step
elastic_grads = tape.gradient(loss_value, elastic_variable_list)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/eager/backprop.py", line 767, in gradient
output_gradients=output_gradients)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/eager/imperative_grad.py", line 63, in imperative_grad
tape._tape, vspace, target, sources, output_gradients) # pylint: disable=protected-access
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/eager/backprop.py", line 147, in grad_fn
op_inputs, op_outputs, orig_outputs)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/eager/backprop.py", line 115, in _magic_gradient_function
return grad_fn(mock_op, *out_grads)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/ops/array_grad.py", line 427, in _GatherV2Grad
params_shape = math_ops.to_int32(params_shape)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 875, in to_int32
return cast(x, dtypes.int32, name=name)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 787, in cast
x = gen_math_ops.cast(x, base_type, name=name)
File "/share/software/user/open/py-tensorflow/1.8.0_py27/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 1548, in cast
_six.raise_from(_core._status_to_exception(e.code, message), None)
File "/share/software/user/open/py-scipystack/1.0_py27/lib/python2.7/site-packages/six.py", line 718, in raise_from
raise value
tensorflow.python.framework.errors_impl.InvalidArgumentError: Tensors on conflicting devices: cannot compute Cast as input #0 was expected to be on /job:localhost/replica:0/task:0/device:GPU:0 but is actually on /job:localhost/replica:0/task:0/device:CPU:0 (operation running on /job:localhost/replica:0/task:0/device:GPU:0) Tensors can be copied explicitly using .gpu() or .cpu() methods, or transparently copied by using tf.enable_eager_execution(device_policy=tfe.DEVICE_PLACEMENT_SILENT). Copying tensors between devices may slow down your model [Op:Cast] name: ToInt32/