RNN 예측값 뽑는법 에러 납니다.

[정민욱]

예측값 = pmodel.predict(첫입력값)
print(예측값)

 

이 부분에서

ValueError Traceback (most recent call last)
<ipython-input-36-7a5118fdebd9> in <module>
----> 1 예측값 = pmodel.predict(첫입력값)
2 print(예측값)

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1627 for step in data_handler.steps():
1628 callbacks.on_predict_batch_begin(step)
-> 1629 tmp_batch_outputs = self.predict_function(iterator)
1630 if data_handler.should_sync:
1631 context.async_wait()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
860 # In this case we have not created variables on the first call. So we can
861 # run the first trace but we should fail if variables are created.
--> 862 results = self._stateful_fn(*args, **kwds)
863 if self._created_variables:
864 raise ValueError("Creating variables on a non-first call to a function"

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
2939 with self._lock:
2940 (graph_function,
-> 2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
2942 return graph_function._call_flat(
2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3355 self.input_signature is None and
3356 call_context_key in self._function_cache.missed):
-> 3357 return self._define_function_with_shape_relaxation(
3358 args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3359

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _define_function_with_shape_relaxation(self, args, kwargs, flat_args, filtered_flat_args, cache_key_context)
3277 expand_composites=True)
3278
-> 3279 graph_function = self._create_graph_function(
3280 args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
3281 self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3194 arg_names = base_arg_names + missing_arg_names
3195 graph_function = ConcreteFunction(
-> 3196 func_graph_module.func_graph_from_py_func(
3197 self._name,
3198 self._python_function,

~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: func_outputs contains only Tensors, CompositeTensors,

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636

~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise

ValueError: in user code:

C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:1478 predict_function *
return step_function(self, iterator)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:1468 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:1461 run_step **
outputs = model.predict_step(data)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:1434 predict_step
return self(x, training=False)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py:375 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py:424 call
return self._run_internal_graph(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\functional.py:560 _run_internal_graph
outputs = node.layer(*args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py:660 __call__
return super(RNN, self).__call__(inputs, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:1012 __call__
outputs = call_fn(inputs, *args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent_v2.py:1270 call
runtime) = lstm_with_backend_selection(**normal_lstm_kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent_v2.py:1655 lstm_with_backend_selection
last_output, outputs, new_h, new_c, runtime = defun_standard_lstm(**params)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\eager\function.py:2941 __call__
filtered_flat_args) = self._maybe_define_function(args, kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\eager\function.py:3361 _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\eager\function.py:3196 _create_graph_function
func_graph_module.func_graph_from_py_func(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py:990 func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent_v2.py:1392 standard_lstm
last_output, outputs, new_states = K.rnn(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4345 rnn
[inp[0] for inp in flatted_inputs])
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4345 <listcomp>
[inp[0] for inp in flatted_inputs])
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py:1036 _slice_helper
return strided_slice(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py:1209 strided_slice
op = gen_array_ops.strided_slice(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py:10473 strided_slice
_, _, _op, _outputs = _op_def_library._apply_op_helper(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:748 _apply_op_helper
op = g._create_op_internal(op_type_name, inputs, dtypes=None,
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py:590 _create_op_internal
return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py:3528 _create_op_internal
ret = Operation(
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py:2015 __init__
self._c_op = _create_c_op(self._graph, node_def, inputs,
C:\Users\admin\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py:1856 _create_c_op
raise ValueError(str(e))

ValueError: slice index 0 of dimension 0 out of bounds. for '{{node strided_slice_1}} = StridedSlice[Index=DT_INT32, T=DT_FLOAT, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=1](transpose, strided_slice_1/stack, strided_slice_1/stack_1, strided_slice_1/stack_2)' with input shapes: [0,?,31], [1], [1], [1] and with computed input tensors: input[1] = <0>, input[2] = <1>, input[3] = <1>.
 

이런 에러가 발생합니다.

에러가 왜 뜨는지 알 수 있을까요??

[codingapple]

pmodel.predict()에 집어넣는 데이터의 shape이 이상한 것 같습니다 

pmodel은 어떤 shape을 집어넣을 수 있는 모델인지

그런데 pmodel에 어떤 shape을 집어넣어버린 것인지 확인해야합니다 

[글쓴이]

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