使用Keras Tuner调整模型时如何跳过有问题的超参数组合？

如何解决使用Keras Tuner调整模型时如何跳过有问题的超参数组合？

使用Keras Tuner时，似乎没有一种方法可以跳过有问题的超参数组合。例如，Conv1D层中的过滤器数量可能与随后的MaxPooling1D层中的池大小的所有值不兼容，因此会导致模型构建中的错误。但是，这可能在运行调谐器之前未知。调谐器一旦运行，将导致错误，从而终止整个调谐过程。有没有办法跳过任何会导致错误的超参数组合？

示例代码：

def model_builder(hp):
    model = Sequential()
    model.add(
        Embedding(
            input_dim=hp.Int(
                'vocab_size',min_value=4000,max_value=10000,step=1000,default=4000
            ),output_dim=hp.Choice(
                'embedding_dim',values=[32,64,128,256],default=32
            ),input_length=hp.Int(
                'max_length',min_value=50,max_value=200,step=10,default=50
            )
        )
    )
    model.add(
        Conv1D(
            filters=hp.Choice(
                'num_filters_1',64],kernel_size=hp.Choice(
                'kernel_size_1',values=[3,5,7,9],default=7
            ),activation='relu'
        )
    )
    model.add(
        MaxPooling1D(
            pool_size=hp.Choice(
                'pool_size',5],default=5
            )
        )
    )
    model.add(
        Conv1D(
            filters=hp.Choice(
                'num_filters_2',kernel_size=hp.Choice(
                'kernel_size_2',activation='relu'
        )
    )
    model.add(
        GlobalMaxPooling1D()
    )
    model.add(
        Dropout(
            rate=hp.Float(
                'dropout_1',min_value=0.0,max_value=0.5,default=0.5,step=0.05
            )
        )
    )
    model.add(
        Dense(
            units=hp.Int(
                'units',min_value=10,max_value=100,default=10
            ),kernel_regularizer=tf.keras.regularizers.l2(
                hp.Float(
                    'regularizer_1',min_value=1e-4,max_value=1e-1,sampling='LOG',default=1e-2
                )
            ),activation='relu'
        )
    )
    model.add(
        Dropout(
            hp.Float(
                'dropout_2',step=0.05
            )
        )
    )
    model.add(
        Dense(
            1,kernel_regularizer=tf.keras.regularizers.l2(
                hp.Float(
                    'regularizer_2',activation='sigmoid'
        )
    )

    
    model.compile(
        loss='binary_crossentropy',optimizer=hp.Choice(
            'optimizer',values=['rmsprop','adam','sgd']
        ),metrics=['accuracy']
    )
    
    return model


tuner = kt.Hyperband(
    model_builder,objective='val_accuracy',max_epochs=20,#factor=3,directory='my_dir',project_name='cec',seed=seed
)   

class ClearTrainingOutput(tf.keras.callbacks.Callback):
  def on_train_end(*args,**kwargs):
    IPython.display.clear_output(wait=True)
    
tuner.search(
    X_train,y_train,epochs=20,validation_data=(X_test,y_test),callbacks=[ClearTrainingOutput()]
)

错误消息：

Epoch 1/3
WARNING:tensorflow:Model was constructed with shape (None,150) for input Tensor("embedding_input:0",shape=(None,150),dtype=float32),but it was called on an input with incompatible shape (32,50).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-62-16a1eae457d8> in <module>
      3     IPython.display.clear_output(wait=True)
      4 
----> 5 tuner.search(
      6     X_train,7     y_train,~/anaconda3/envs/cec/lib/python3.8/site-packages/kerastuner/engine/base_tuner.py in search(self,*fit_args,**fit_kwargs)
    128 
    129             self.on_trial_begin(trial)
--> 130             self.run_trial(trial,**fit_kwargs)
    131             self.on_trial_end(trial)
    132         self.on_search_end()

~/anaconda3/envs/cec/lib/python3.8/site-packages/kerastuner/tuners/hyperband.py in run_trial(self,trial,**fit_kwargs)
    385             fit_kwargs['epochs'] = hp.values['tuner/epochs']
    386             fit_kwargs['initial_epoch'] = hp.values['tuner/initial_epoch']
--> 387         super(Hyperband,self).run_trial(trial,**fit_kwargs)
    388 
    389     def _build_model(self,hp):

~/anaconda3/envs/cec/lib/python3.8/site-packages/kerastuner/engine/multi_execution_tuner.py in run_trial(self,**fit_kwargs)
     94 
     95             model = self.hypermodel.build(trial.hyperparameters)
---> 96             history = model.fit(*fit_args,**copied_fit_kwargs)
     97             for metric,epoch_values in history.history.items():
     98                 if self.oracle.objective.direction == 'min':

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self,*args,**kwargs)
     64   def _method_wrapper(self,**kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self,**kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

~/anaconda3/envs/cec/lib/python3.8/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)
    846                 batch_size=batch_size):
    847               callbacks.on_train_batch_begin(step)
--> 848               tmp_logs = train_function(iterator)
    849               # Catch OutOfRangeError for Datasets of unkNown size.
    850               # This blocks until the batch has finished executing.

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self,**kwds)
    578         xla_context.Exit()
    579     else:
--> 580       result = self._call(*args,**kwds)
    581 
    582     if tracing_count == self._get_tracing_count():

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self,**kwds)
    625       # This is the first call of __call__,so we have to initialize.
    626       initializers = []
--> 627       self._initialize(args,kwds,add_initializers_to=initializers)
    628     finally:
    629       # At this point we kNow that the initialization is complete (or less

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self,args,add_initializers_to)
    503     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
    504     self._concrete_stateful_fn = (
--> 505         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
    506             *args,**kwds))
    507 

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
   2444       args,kwargs = None,None
   2445     with self._lock:
-> 2446       graph_function,_,_ = self._maybe_define_function(args,kwargs)
   2447     return graph_function
   2448 

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self,kwargs)
   2775 
   2776       self._function_cache.missed.add(call_context_key)
-> 2777       graph_function = self._create_graph_function(args,kwargs)
   2778       self._function_cache.primary[cache_key] = graph_function
   2779       return graph_function,kwargs

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
   2655     arg_names = base_arg_names + missing_arg_names
   2656     graph_function = ConcreteFunction(
-> 2657         func_graph_module.func_graph_from_py_func(
   2658             self._name,2659             self._python_function,~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/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)
    979         _,original_func = tf_decorator.unwrap(python_func)
    980 
--> 981       func_outputs = python_func(*func_args,**func_kwargs)
    982 
    983       # invariant: `func_outputs` contains only Tensors,CompositeTensors,~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args,**kwds)
    439         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    440         # the function a weak reference to itself to avoid a reference cycle.
--> 441         return weak_wrapped_fn().__wrapped__(*args,**kwds)
    442     weak_wrapped_fn = weakref.ref(wrapped_fn)
    443 

~/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args,**kwargs)
    966           except Exception as e:  # pylint:disable=broad-except
    967             if hasattr(e,"ag_error_Metadata"):
--> 968               raise e.ag_error_Metadata.to_exception(e)
    969             else:
    970               raise

ValueError: in user code:

    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn,kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args,**kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:531 train_step  **
        y_pred = self(x,training=True)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:927 __call__
        outputs = call_fn(cast_inputs,**kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:277 call
        return super(Sequential,self).call(inputs,training=training,mask=mask)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py:717 call
        return self._run_internal_graph(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py:888 _run_internal_graph
        output_tensors = layer(computed_tensors,**kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:927 __call__
        outputs = call_fn(cast_inputs,**kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/keras/layers/convolutional.py:207 call
        outputs = self._convolution_op(inputs,self.kernel)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:1106 __call__
        return self.conv_op(inp,filter)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:638 __call__
        return self.call(inp,filter)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:231 __call__
        return self.conv_op(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:220 _conv1d
        return conv1d(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py:574 new_func
        return func(*args,**kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/util/deprecation.py:574 new_func
        return func(*args,**kwargs)
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/ops/nn_ops.py:1655 conv1d
        result = gen_nn_ops.conv2d(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/ops/gen_nn_ops.py:965 conv2d
        _,_op,_outputs = _op_def_library._apply_op_helper(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:742 _apply_op_helper
        op = g._create_op_internal(op_type_name,inputs,dtypes=None,/home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:593 _create_op_internal
        return super(FuncGraph,self)._create_op_internal(  # pylint: disable=protected-access
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3319 _create_op_internal
        ret = Operation(
    /home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1816 __init__
        self._c_op = _create_c_op(self._graph,node_def,/home/george/anaconda3/envs/cec/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1657 _create_c_op
        raise ValueError(str(e))

    ValueError: Negative dimension size caused by subtracting 7 from 6 for '{{node sequential/conv1d_1/conv1d}} = Conv2D[T=DT_FLOAT,data_format="NHWC",dilations=[1,1,1],explicit_paddings=[],padding="VALID",strides=[1,use_cudnn_on_gpu=true](sequential/conv1d_1/conv1d/ExpandDims,sequential/conv1d_1/conv1d/ExpandDims_1)' with input shapes: [32,6,32],[1,32,32].