DeepChem GraphConvodel (GNN) training TypeError - machine-learning

Question

I am a beginner to GNNs and I was trying out a code for predicting drug toxicity using DeepChem's Tox21 dataset. It is a dataset with a training set of 12 thousand compounds and test set of 650 compounds. I need in help in debugging and rectifying this error:"TypeError: 'NoneType' object is not subscriptable", which I get at the end.
Here is the code snippet:
model = GraphConvModel(len(tox21_tasks),
batch_size=32,
mode='classification')
print("Fitting the model")
model.fit(train_dataset, nb_epoch=10)
And here is my error:
TypeError Traceback (most recent call last)
<ipython-input-5-8088249b7fd6> in <module>
4 mode='classification')
5 print("Fitting the model")
----> 6 model.fit(train_dataset, nb_epoch=10)
~\anaconda3\lib\site-packages\deepchem\models\keras_model.py in fit(self, dataset, nb_epoch, max_checkpoints_to_keep, checkpoint_interval, deterministic, restore, variables, loss, callbacks, all_losses)
322 dataset, epochs=nb_epoch,
323 deterministic=deterministic), max_checkpoints_to_keep,
--> 324 checkpoint_interval, restore, variables, loss, callbacks, all_losses)
325
326 def fit_generator(self,
~\anaconda3\lib\site-packages\deepchem\models\keras_model.py in fit_generator(self, generator, max_checkpoints_to_keep, checkpoint_interval, restore, variables, loss, callbacks, all_losses)
407 inputs = inputs[0]
408
--> 409 batch_loss = apply_gradient_for_batch(inputs, labels, weights, loss)
410 current_step = self._global_step.numpy()
411
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~\anaconda3\lib\site-packages\tensorflow_core\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)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~\anaconda3\lib\site-packages\tensorflow_core\python\framework\func_graph.py in wrapper(*args, **kwargs)
903 except Exception as e: # pylint:disable=broad-except
904 if hasattr(e, "ag_error_metadata"):
--> 905 raise e.ag_error_metadata.to_exception(e)
906 else:
907 raise
TypeError: in converted code:
relative to C:\Users\Madiha\anaconda3\lib\site-packages:
deepchem\models\keras_model.py:474 apply_gradient_for_batch *
grads = tape.gradient(batch_loss, vars)
tensorflow_core\python\eager\backprop.py:1014 gradient
unconnected_gradients=unconnected_gradients)
tensorflow_core\python\eager\imperative_grad.py:76 imperative_grad
compat.as_str(unconnected_gradients.value))
tensorflow_core\python\eager\backprop.py:138 _gradient_function
return grad_fn(mock_op, *out_grads)
tensorflow_core\python\ops\math_grad.py:455 _UnsortedSegmentMaxGrad
return _UnsortedSegmentMinOrMaxGrad(op, grad)
tensorflow_core\python\ops\math_grad.py:432 _UnsortedSegmentMinOrMaxGrad
_GatherDropNegatives(op.outputs[0], op.inputs[1])
TypeError: 'NoneType' object is not subscriptable

Answer 1

As an advise, check some examples on the DeepChem website. Here is a code which will work:
tasks, datasets, transformers = dc.molnet.load_tox21(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = datasets
model = dc.models.GraphConvModel(len(tasks),
batch_size=32,
mode='classification')
print("Fitting the model")
model.fit(train_dataset)
Hope is work for you!