Hi, I am new to DeepLearning.AI. I’m encountering a different type of error for Q6
‘EvalTask’ object has no attribute ‘weights’.
My lab ID is: utzmghixtiyl.
Your help will be highly appreciated. Thanks a lot.
Hi, I was able to rectify the attribute error. But now I’m getting an AssertionError: Invalid shape (16, 1); expected (16,). I checked the other public threads which talked about similar issues but still unable to rectify it.
Hey @Shaun_Basil_Almeida,
Seems to me that at some place, you have either missed out on reshaping or perhaps missed out on keepdims = True. Either way, can you please post your error stack, so that we can try and figure out the exact issue.
Cheers,
Elemento
Here are the error stacks:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
in
2 # Take a look on how the eval_task is inside square brackets and
3 # take that into account for you train_model implementation
----> 4 training_loop = train_model(model, train_task, [eval_task], 100, output_dir_expand)
in train_model(classifier, train_task, eval_task, n_steps, output_dir)
21 eval_tasks=[eval_task], # The evaluation task
22 output_dir=output_dir, # The output directory
---> 23 random_seed=31 # Do not modify this random seed in order to ensure reproducibility and for grading purposes.
24 )
25
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in __init__(self, model, tasks, eval_model, eval_tasks, output_dir, checkpoint_at, checkpoint_low_metric, checkpoint_high_metric, permanent_checkpoint_at, eval_at, which_task, n_devices, random_seed, loss_chunk_size, use_memory_efficient_trainer, adasum, callbacks)
300 metric_names = [
301 name # pylint: disable=g-complex-comprehension
--> 302 for eval_task in self._eval_tasks
303 for name in eval_task.metric_names
304 ]
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in (.0)
301 name # pylint: disable=g-complex-comprehension
302 for eval_task in self._eval_tasks
--> 303 for name in eval_task.metric_names
304 ]
305 self._rjust_len = max(map(len, loss_names + metric_names))
AttributeError: 'list' object has no attribute 'metric_names'
---------------------------------------------------------------------------
LayerError Traceback (most recent call last)
<ipython-input-102-7c2f23c889cd> in <module>
6 pass
7
----> 8 w1_unittest.test_train_model(train_model(classifier(), train_task, [eval_task], 10, './model_test/'))
<ipython-input-100-918bf0dfcb18> in train_model(classifier, train_task, eval_task, n_steps, output_dir)
20 eval_tasks=eval_task, # The evaluation task
21 output_dir=output_dir, # The output directory
---> 22 random_seed=31 # Do not modify this random seed in order to ensure reproducibility and for grading purposes.
23 )
24
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in __init__(self, model, tasks, eval_model, eval_tasks, output_dir, checkpoint_at, checkpoint_low_metric, checkpoint_high_metric, permanent_checkpoint_at, eval_at, which_task, n_devices, random_seed, loss_chunk_size, use_memory_efficient_trainer, adasum, callbacks)
305 self._rjust_len = max(map(len, loss_names + metric_names))
306 self._evaluator_per_task = tuple(
--> 307 self._init_evaluator(eval_task) for eval_task in self._eval_tasks)
308
309 if self._output_dir is None:
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in <genexpr>(.0)
305 self._rjust_len = max(map(len, loss_names + metric_names))
306 self._evaluator_per_task = tuple(
--> 307 self._init_evaluator(eval_task) for eval_task in self._eval_tasks)
308
309 if self._output_dir is None:
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in _init_evaluator(self, eval_task)
364 """Initializes the per-task evaluator."""
365 model_with_metrics = _model_with_metrics(
--> 366 self._eval_model, eval_task)
367 if self._use_memory_efficient_trainer:
368 return _Evaluator(
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in _model_with_metrics(model, eval_task)
1047 """
1048 return _model_with_ends(
-> 1049 model, eval_task.metrics, shapes.signature(eval_task.sample_batch)
1050 )
1051
/opt/conda/lib/python3.7/site-packages/trax/supervised/training.py in _model_with_ends(model, end_layers, batch_signature)
1029 metrics_layer = tl.Branch(*end_layers)
1030 metrics_input_signature = model.output_signature(batch_signature)
-> 1031 _, _ = metrics_layer.init(metrics_input_signature)
1032
1033 model_with_metrics = tl.Serial(model, metrics_layer)
/opt/conda/lib/python3.7/site-packages/trax/layers/base.py in init(self, input_signature, rng, use_cache)
309 name, trace = self._name, _short_traceback(skip=3)
310 raise LayerError(name, 'init', self._caller,
--> 311 input_signature, trace) from None
312
313 def init_from_file(self, file_name, weights_only=False, input_signature=None):
LayerError: Exception passing through layer Branch (in init):
layer created in file [...]/trax/supervised/training.py, line 1029
layer input shapes: (ShapeDtype{shape:(16, 1, 2), dtype:float32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16,), dtype:int32})
File [...]/trax/layers/combinators.py, line 106, in init_weights_and_state
outputs, _ = sublayer._forward_abstract(inputs)
LayerError: Exception passing through layer Parallel (in _forward_abstract):
layer created in file [...]/trax/supervised/training.py, line 1029
layer input shapes: (ShapeDtype{shape:(16, 1, 2), dtype:float32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16, 1, 2), dtype:float32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16,), dtype:int32})
File [...]/jax/interpreters/partial_eval.py, line 411, in abstract_eval_fun
lu.wrap_init(fun, params), avals, debug_info)
File [...]/jax/interpreters/partial_eval.py, line 1252, in trace_to_jaxpr_dynamic
jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, main, in_avals)
File [...]/jax/interpreters/partial_eval.py, line 1262, in trace_to_subjaxpr_dynamic
ans = fun.call_wrapped(*in_tracers)
File [...]/site-packages/jax/linear_util.py, line 166, in call_wrapped
ans = self.f(*args, **dict(self.params, **kwargs))
File [...]/site-packages/jax/linear_util.py, line 166, in call_wrapped
ans = self.f(*args, **dict(self.params, **kwargs))
LayerError: Exception passing through layer Parallel (in pure_fn):
layer created in file [...]/trax/supervised/training.py, line 1029
layer input shapes: (ShapeDtype{shape:(16, 1, 2), dtype:float32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16, 1, 2), dtype:float32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16,), dtype:int32})
File [...]/trax/layers/combinators.py, line 211, in forward
sub_outputs, sub_state = layer.pure_fn(x, w, s, r, use_cache=True)
LayerError: Exception passing through layer WeightedCategoryAccuracy (in pure_fn):
layer created in file [...]/<ipython-input-97-e22a181c30d5>, line 21
layer input shapes: (ShapeDtype{shape:(16, 1, 2), dtype:float32}, ShapeDtype{shape:(16,), dtype:int32}, ShapeDtype{shape:(16,), dtype:int32})
File [...]/trax/layers/base.py, line 743, in forward
raw_output = self._forward_fn(inputs)
File [...]/trax/layers/base.py, line 784, in _forward
return f(*xs)
File [...]/trax/layers/metrics.py, line 195, in f
shapes.assert_same_shape(predictions, targets)
File [...]/site-packages/trax/shapes.py, line 140, in assert_same_shape
assert_shape_equals(array1, array2.shape)
File [...]/site-packages/trax/shapes.py, line 134, in assert_shape_equals
'Invalid shape {}; expected {}.'.format(array.shape, shape)
AssertionError: Invalid shape (16, 1); expected (16,).
Hey @Shaun_Basil_Almeida,
Once again, the issue lies in your implementation of train_model only. You have specified [eval_task] as the argument for the parameter eval_model, whereas you are supposed to specify it as the argument for eval_tasks. Please take a look at the documentation here once again, to understand the meaning of different parameters. Let us know if this resolves your issue.
Cheers,
Elemento