我想使用Keras Tuner来选择我的模型的最优超参数集。我提出的代码：
ValueError": Input 0 of layer dense is incompatible with the layer:
expected axis -1 of input shape to have value 867000 but received input with shape (None, 100)
我也尝试使用model.add(layers.Flatten(input_shape=X.shape[1:]))，但它引发了相同的错误。
标签编码
encoder = LabelEncoder()
df["subtype"] = encoder.fit_transform(df[["subtype"]])
检索特征和目标
X = df.iloc[:,7:1007] # 1000 features
y = df[["subtype"]]
列车测试值拆分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2
定义型号
# Define Sequential model
def get_model(hp):
model = keras.Sequential()
model.add(layers.Flatten(input_shape=X.shape))
# Tune the number of units in the first Dense layer
hp_units = hp.Int('units', min_value=30, max_value=len(X.T), step=32)
model.add(keras.layers.Dense(units=hp_units, activation='relu'))
model.add(keras.layers.Dense(10))
# Tune the learning rate for the optimizer
# Choose an optimal value from 0.01, 0.001, or 0.0001
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
model.compile(optimizer=Adam(learning_rate=hp_learning_rate),
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
return model
提前停止
es_callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
特征选择
selector = SelectKBest(f_classif, k=100) # Retrieve 100 best features
selected_features_subtype = selector.fit_transform(X_train, y_train.values.ravel())
实例化调谐器
tuner = kt.Hyperband(get_model,
objective='val_accuracy',
max_epochs=10,
factor=3)
超参数搜索：
tuner.search(selected_features_subtype, y_train, validation_split=0.5, batch_size=batch_size, callbacks=[es_callback])
追溯：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_17/1635591259.py in <module>
----> 1 tuner.search(selected_features_subtype, y_train, validation_split=0.5, batch_size=batch_size, callbacks=[es_callback])
2
3 # Get the optimal hyperparameters
4 best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]
5
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
177
178 self.on_trial_begin(trial)
--> 179 results = self.run_trial(trial, *fit_args, **fit_kwargs)
180 # `results` is None indicates user updated oracle in `run_trial()`.
181 if results is None:
/opt/conda/lib/python3.7/site-packages/keras_tuner/tuners/hyperband.py in run_trial(self, trial, *fit_args, **fit_kwargs)
382 fit_kwargs["epochs"] = hp.values["tuner/epochs"]
383 fit_kwargs["initial_epoch"] = hp.values["tuner/initial_epoch"]
--> 384 return super(Hyperband, self).run_trial(trial, *fit_args, **fit_kwargs)
385
386 def _build_model(self, hp):
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
292 callbacks.append(model_checkpoint)
293 copied_kwargs["callbacks"] = callbacks
--> 294 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
295
296 histories.append(obj_value)
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py in _build_and_fit_model(self, trial, *args, **kwargs)
220 hp = trial.hyperparameters
221 model = self._try_build(hp)
--> 222 results = self.hypermodel.fit(hp, model, *args, **kwargs)
223 return tuner_utils.convert_to_metrics_dict(
224 results, self.oracle.objective, "HyperModel.fit()"
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/hypermodel.py in fit(self, hp, model, *args, **kwargs)
135 If return a float, it should be the `objective` value.
136 """
--> 137 return model.fit(*args, **kwargs)
138
139
/opt/conda/lib/python3.7/site-packages/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)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
758 self._concrete_stateful_fn = (
759 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 760 *args, **kwds))
761
762 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3064 args, kwargs = None, None
3065 with self._lock:
-> 3066 graph_function, _ = self._maybe_define_function(args, kwargs)
3067 return graph_function
3068
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3461
3462 self._function_cache.missed.add(call_context_key)
-> 3463 graph_function = self._create_graph_function(args, kwargs)
3464 self._function_cache.primary[cache_key] = graph_function
3465
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3306 arg_names=arg_names,
3307 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3308 capture_by_value=self._capture_by_value),
3309 self._function_attributes,
3310 function_spec=self.function_spec,
/opt/conda/lib/python3.7/site-packages/tensorflow/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, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:853 train_function *
return step_function(self, iterator)
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:842 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:835 run_step **
outputs = model.train_step(data)
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py:787 train_step
y_pred = self(x, training=True)
/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/opt/conda/lib/python3.7/site-packages/keras/engine/sequential.py:369 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
/opt/conda/lib/python3.7/site-packages/keras/engine/functional.py:415 call
inputs, training=training, mask=mask)
/opt/conda/lib/python3.7/site-packages/keras/engine/functional.py:550 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py:1020 __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
/opt/conda/lib/python3.7/site-packages/keras/engine/input_spec.py:254 assert_input_compatibility
' but received input with shape ' + display_shape(x.shape))
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 867000 but received input with shape (None, 100)
样本数据(如dict(：
{'cg00000289': {'TCGA-5P-A9JW-01A': 0.619047033443068,
'TCGA-5P-A9JY-01A': 0.662345356057447,
'TCGA-5P-A9JZ-01A': 0.699464419990523,
'TCGA-5P-A9K0-01A': 0.581701228463189,
'TCGA-5P-A9K2-01A': 0.673198201701496,
'TCGA-5P-A9K3-01A': 0.626858745753386,
'TCGA-5P-A9K4-01A': 0.68070511240185,
'TCGA-5P-A9K6-01A': 0.736978843263676,
'TCGA-5P-A9K8-01A': 0.520786654021559,
'TCGA-5P-A9K9-01A': 0.630167782543268,
'TCGA-5P-A9KA-01A': 0.626926559177059,
'TCGA-5P-A9KC-01A': 0.630977089669812,
'TCGA-5P-A9KE-01A': 0.67563913188817,
'TCGA-5P-A9KF-01A': 0.645760654461395,
'TCGA-5P-A9KH-01A': 0.743286554209644,
'TCGA-6D-AA2E-01A': 0.664370540359405,
'TCGA-A3-3357-01A': 0.604326417072586,
'TCGA-A3-3358-01A': 0.458291671200214,
'TCGA-A3-3367-01A': 0.643363591881443,
'TCGA-A3-3370-01A': 0.75808536817831},
'cg00000292': {'TCGA-5P-A9JW-01A': 0.833751448211989,
'TCGA-5P-A9JY-01A': 0.761479643484317,
'TCGA-5P-A9JZ-01A': 0.555219387831784,
'TCGA-5P-A9K0-01A': 0.911434986725676,
'TCGA-5P-A9K2-01A': 0.592839496707225,
'TCGA-5P-A9K3-01A': 0.728724740061591,
'TCGA-5P-A9K4-01A': 0.871647081931081,
'TCGA-5P-A9K6-01A': 0.687137365325043,
'TCGA-5P-A9K8-01A': 0.377068349756215,
'TCGA-5P-A9K9-01A': 0.885089826740071,
'TCGA-5P-A9KA-01A': 0.678749255227915,
'TCGA-5P-A9KC-01A': 0.82812139328519,
'TCGA-5P-A9KE-01A': 0.864590733749797,
'TCGA-5P-A9KF-01A': 0.858070865799283,
'TCGA-5P-A9KH-01A': 0.914435928566657,
'TCGA-6D-AA2E-01A': 0.502154665104261,
'TCGA-A3-3357-01A': 0.662047197870235,
'TCGA-A3-3358-01A': 0.837059251611538,
'TCGA-A3-3367-01A': 0.519939858249351,
'TCGA-A3-3370-01A': 0.515743234863198},
'cg00000321': {'TCGA-5P-A9JW-01A': 0.489674207394165,
'TCGA-5P-A9JY-01A': 0.558997284357574,
'TCGA-5P-A9JZ-01A': 0.169654991100549,
'TCGA-5P-A9K0-01A': 0.524017780921585,
'TCGA-5P-A9K2-01A': 0.613973121455874,
'TCGA-5P-A9K3-01A': 0.695670625292722,
'TCGA-5P-A9K4-01A': 0.54705053331032,
'TCGA-5P-A9K6-01A': 0.430048300391044,
'TCGA-5P-A9K8-01A': 0.198107812192402,
'TCGA-5P-A9K9-01A': 0.688004412660976,
'TCGA-5P-A9KA-01A': 0.463728628068311,
'TCGA-5P-A9KC-01A': 0.195610858899826,
'TCGA-5P-A9KE-01A': 0.550900635535007,
'TCGA-5P-A9KF-01A': 0.183061803083559,
'TCGA-5P-A9KH-01A': 0.0687041391027568,
'TCGA-6D-AA2E-01A': 0.570614767869701,
'TCGA-A3-3357-01A': 0.680216582417148,
'TCGA-A3-3358-01A': 0.288408070866453,
'TCGA-A3-3367-01A': 0.68957300320214,
'TCGA-A3-3370-01A': 0.432450706527388},
'cg00000363': {'TCGA-5P-A9JW-01A': 0.276273359465442,
'TCGA-5P-A9JY-01A': 0.25703867804956,
'TCGA-5P-A9JZ-01A': 0.0981746197111535,
'TCGA-5P-A9K0-01A': 0.569434380074143,
'TCGA-5P-A9K2-01A': 0.229164840583894,
'TCGA-5P-A9K3-01A': 0.819181728250669,
'TCGA-5P-A9K4-01A': 0.385400157144987,
'TCGA-5P-A9K6-01A': 0.121114850921845,
'TCGA-5P-A9K8-01A': 0.0913002964111161,
'TCGA-5P-A9K9-01A': 0.157709640291589,
'TCGA-5P-A9KA-01A': 0.182786461024344,
'TCGA-5P-A9KC-01A': 0.413992919815649,
'TCGA-5P-A9KE-01A': 0.461011690059099,
'TCGA-5P-A9KF-01A': 0.206300754004666,
'TCGA-5P-A9KH-01A': 0.105913974360403,
'TCGA-6D-AA2E-01A': 0.414195666341229,
'TCGA-A3-3357-01A': 0.236700230821645,
'TCGA-A3-3358-01A': 0.26841943012007,
'TCGA-A3-3367-01A': 0.324092383012077,
'TCGA-A3-3370-01A': 0.20675761437022},
'cg00000622': {'TCGA-5P-A9JW-01A': 0.0127307580597565,
'TCGA-5P-A9JY-01A': 0.0120665526567922,
'TCGA-5P-A9JZ-01A': 0.0129414663486043,
'TCGA-5P-A9K0-01A': 0.0151053469374601,
'TCGA-5P-A9K2-01A': 0.0147191458229104,
'TCGA-5P-A9K3-01A': 0.0128586680833482,
'TCGA-5P-A9K4-01A': 0.0131839246367822,
'TCGA-5P-A9K6-01A': 0.0145337257462313,
'TCGA-5P-A9K8-01A': 0.0131548295351121,
'TCGA-5P-A9K9-01A': 0.0164371867221858,
'TCGA-5P-A9KA-01A': 0.0153209540816947,
'TCGA-5P-A9KC-01A': 0.0146341900882511,
'TCGA-5P-A9KE-01A': 0.0138002584809048,
'TCGA-5P-A9KF-01A': 0.012958575912875,
'TCGA-5P-A9KH-01A': 0.0142346121115625,
'TCGA-6D-AA2E-01A': 0.0139666701044385,
'TCGA-A3-3357-01A': 0.0082183731354049,
'TCGA-A3-3358-01A': 0.0143527756424356,
'TCGA-A3-3367-01A': 0.0100636145864037,
'TCGA-A3-3370-01A': 0.0108528842825329},
'type': {'TCGA-5P-A9JW-01A': 'tumor',
'TCGA-5P-A9JY-01A': 'tumor',
'TCGA-5P-A9JZ-01A': 'tumor',
'TCGA-5P-A9K0-01A': 'tumor',
'TCGA-5P-A9K2-01A': 'tumor',
'TCGA-5P-A9K3-01A': 'tumor',
'TCGA-5P-A9K4-01A': 'tumor',
'TCGA-5P-A9K6-01A': 'tumor',
'TCGA-5P-A9K8-01A': 'tumor',
'TCGA-5P-A9K9-01A': 'tumor',
'TCGA-5P-A9KA-01A': 'tumor',
'TCGA-5P-A9KC-01A': 'tumor',
'TCGA-5P-A9KE-01A': 'tumor',
'TCGA-5P-A9KF-01A': 'tumor',
'TCGA-5P-A9KH-01A': 'tumor',
'TCGA-6D-AA2E-01A': 'tumor',
'TCGA-A3-3357-01A': 'tumor',
'TCGA-A3-3358-01A': 'tumor',
'TCGA-A3-3367-01A': 'tumor',
'TCGA-A3-3370-01A': 'tumor'},
'subtype': {'TCGA-5P-A9JW-01A': 'KIRP',
'TCGA-5P-A9JY-01A': 'KIRP',
'TCGA-5P-A9JZ-01A': 'KIRP',
'TCGA-5P-A9K0-01A': 'KIRP',
'TCGA-5P-A9K2-01A': 'KIRP',
'TCGA-5P-A9K3-01A': 'KIRP',
'TCGA-5P-A9K4-01A': 'KIRP',
'TCGA-5P-A9K6-01A': 'KIRP',
'TCGA-5P-A9K8-01A': 'KIRP',
'TCGA-5P-A9K9-01A': 'KIRP',
'TCGA-5P-A9KA-01A': 'KIRP',
'TCGA-5P-A9KC-01A': 'KIRP',
'TCGA-5P-A9KE-01A': 'KIRP',
'TCGA-5P-A9KF-01A': 'KIRP',
'TCGA-5P-A9KH-01A': 'KIRP',
'TCGA-6D-AA2E-01A': 'KIRC',
'TCGA-A3-3357-01A': 'KIRC',
'TCGA-A3-3358-01A': 'KIRC',
'TCGA-A3-3367-01A': 'KIRC',
'TCGA-A3-3370-01A': 'KIRC'}}
确保最后一个(输出层(中的units=hp_units是一个整数，也将activation='ru'更改为softmax。希望这能工作