问题描述
我是 GNN 的初学者，我正在尝试使用 DeepChem 的 Tox21 数据集预测药物毒性的代码。它是一个包含 12,000 个化合物的训练集和 650 个化合物的测试集的数据集。我需要帮助调试和纠正这个错误：“TypeError: 'nonetype' object is not subscriptable”，我在最后得到了。
这是代码片段：
model = GraphConvModel(len(tox21_tasks),batch_size=32,mode='classification')
print("Fitting the model")
model.fit(train_dataset,nb_epoch=10)
这是我的错误：
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),--> 324 checkpoint_interval,all_losses)
325
326 def fit_generator(self,~\anaconda3\lib\site-packages\deepchem\models\keras_model.py in fit_generator(self,generator,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,**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,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,**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,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,kwargs
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self,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,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
解决方法
作为建议，请查看 DeepChem 网站上的一些示例。这是一个可以工作的代码：
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)
希望对你有用！