The model is built successfully and all, but when attempting to call the fit method, I get the following error:
**ValueError: The two structures don't have the same sequence length. Input structure has length 10, while shallow structure has length 1.**
Hey @Subigya_Paudel,
Welcome to the community. Have you passed all the test-cases in your assignment? If not, then can you please post the traceback of the error that you receive in your notebook.
P.S. - Posting code publicly is strictly against the community guidelines. So, please make sure to post the traceback only, and not the code.
Cheers,
Elemento
Yes, all the test cases pass and even the assignment received 100 points, but the actual training is interrupted by this error. The requested feedback is as follows:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-47-1a1812141e7e> in <module>
----> 1 model.fit([Xoh, s0, c0], outputs, epochs=1, batch_size=100)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
821 # This is the first call of __call__, so we have to initialize.
822 initializers = []
--> 823 self._initialize(args, kwds, add_initializers_to=initializers)
824 finally:
825 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args, **kwds))
698
699 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2853 args, kwargs = None, None
2854 with self._lock:
-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2856 return graph_function
2857
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 function_spec=self.function_spec,
/opt/conda/lib/python3.7/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)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:759 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:388 update_state
self.build(y_pred, y_true)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:319 build
self._metrics, y_true, y_pred)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/nest.py:1139 map_structure_up_to
**kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/nest.py:1221 map_structure_with_tuple_paths_up_to
expand_composites=expand_composites)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/nest.py:854 assert_shallow_structure
input_length=len(input_tree), shallow_length=len(shallow_tree)))
ValueError: The two structures don't have the same sequence length. Input structure has length 10, while shallow structure has length 1.
Are you running in the Coursera Labs environment, or are you using some other platform?
It’s in Coursera labs.
Hey @Subigya_Paudel,
It seems like your code has an error that is not being caught by both, the test-cases and the auto-grader, and as a result, even though your assignment is receiving a full score, you are unable to train the model. This is because, I ran the notebook myself in Coursera labs, and it is running perfectly fine. Can you please DM your notebook as an attachment to me? You can find the instructions to download your notebook here.
Cheers,
Elemento
I got similar error, what helped me is that I was returning Model(inputs=[X, s0, c0], outputs=[outputs])instead of Model(inputs=[X, s0, c0], outputs=outputs) which solved it for me
Hey @Ankur_Srivastava,
Welcome, and we are glad that you could become a part of our community 
Thanks a lot for your contribution.
Cheers,
Elemento