Issue
When using Keras with Tensorflow 2.3.1 to train a network, I received the following error message:
TypeError: The two structures don't have the same sequence type. Input structure has type <class 'list'>, while shallow structure has type <class 'dict'>.
I can guess that an input structure relates to the data type being feed to the keras.layers.Input() object during the fit() method. But I’m having trouble guessing what a "shallow structure" is.
The above error occurs when calling the model.fit() method. There I’m using a TF dataset to feed a tuple of the form: (input_data, {"target1": target1, "target1": target2}). Thus, the "input structure" should be a dict, but it could be considered the list also.
My question is: what is a "shallow structure" in this context?
Edit #1:
Here is the full traceback:
File "<ipython-input-2-0c8f2fdd652d>", line 1, in <module>
runfile('/Users/username/my_repo/my_trainer_R2.py', wdir='/Users/username/my_repo')
File "/Users/username/Library/Application Support/JetBrains/Toolbox/apps/PyCharm-P/ch-0/203.5981.165/PyCharm 2020.3 EAP.app/Contents/plugins/python/helpers/pydev/_pydev_bundle/pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "/Users/username/Library/Application Support/JetBrains/Toolbox/apps/PyCharm-P/ch-0/203.5981.165/PyCharm 2020.3 EAP.app/Contents/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/Users/username/my_repo/my_trainer_R2.py", line 131, in <module>
graph_builder.train_model(dataset_train_tuple, dataset_valid_tuple, n_epochs, n_batch_size, run_dir=run_dir, run_name=run_name, valid_dataset=pipeline_valid.get_dataset(), plotting_records=plotting_records[0])
File "/Users/username/my_repo/graph_builder_R2.py", line 401, in train_model
callbacks = [early_stopping_cb, tensorboard_cb, checkpoint_cb, general_callbacks]
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 1098, in fit
tmp_logs = train_function(iterator)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 823, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 697, in _initialize
*args, **kwds))
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2855, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3213, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 3075, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 986, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 600, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 973, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/Users/username/.local/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)
/Users/username/.local/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)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:759 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:388 update_state
self.build(y_pred, y_true)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/compile_utils.py:319 build
self._metrics, y_true, y_pred)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/util/nest.py:1139 map_structure_up_to
**kwargs)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/util/nest.py:1221 map_structure_with_tuple_paths_up_to
expand_composites=expand_composites)
/Users/username/.local/lib/python3.7/site-packages/tensorflow/python/util/nest.py:825 assert_shallow_structure
shallow_type=type(shallow_tree)))
TypeError: The two structures don't have the same sequence type. Input structure has type <class 'list'>, while shallow structure has type <class 'dict'>.
Solution
If anyone still having this issue – it can be resolved by obtaining the shallow first and then modifying it to your needs.
Example:
import tensorflow_datasets as tfds
ds = tfds.load('mnist', split='train', shuffle_files=True)
from tensorflow.python.data.ops import dataset_ops
dataset_ops.get_legacy_output_shapes(ds)
and then you can just modify the output from:
{'image': TensorShape([28, 28, 1]), 'label': TensorShape([])}
to:
{'image': [28, 28, 1], 'label': []}
And you can use it in padded_batch to apply padding. In this case from 1 channel to 3. If one size of input has variable length, just use None instead a fixed number and it will get padded to largest size in the batch (ie. [28, 28, None] if you would have variable number of channels in images in this dataset):
list(ds.padded_batch(1,padded_shapes={'image': [28, 28, 3], 'label': []}).take(1))
result:
[{'image': <tf.Tensor: shape=(1, 28, 28, 3), dtype=uint8, numpy=
array([[[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
...,
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
...,
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]]], dtype=uint8)>,
'label': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([4])>}]
Hopefully this helps.
Answered By – JakubVedral
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