Created
September 14, 2024 02:14
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| def create_model(meta): | |
| model1 = keras.models.Sequential() | |
| # Definamos la capa de entrada | |
| # CapaEntrada = keras.layers.Input(shape = Xtrain.shape[1]) | |
| # model1.add(CapaEntrada) | |
| # Inicio de las Capas Ocultas | |
| model1.add(keras.layers.Dense(units = 15, input_dim = Xtrain.shape[1] , | |
| activation = meta["activation"], | |
| name = "1eraCapaOculta_5Neuronas") ) | |
| model1.add(keras.layers.Dense(units = 12, | |
| activation = meta["activation"], | |
| name = "2daCapaOculta_7Neuronas")) | |
| model1.add(keras.layers.Dense(units = 20, | |
| activation = meta["activation"], | |
| name = "3eraCapaOculta_12Nueronas")) | |
| # Fin de las Capas Ocultas | |
| # Capa de salida | |
| model1.add(keras.layers.Dense(units = 1, | |
| activation = "sigmoid", | |
| name ="Capa_de_Salida")) | |
| model1.compile(optimizer=meta["optimizer"], | |
| loss='binary_crossentropy', | |
| metrics=['accuracy']) | |
| return model1 |
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| Traceback (most recent call last): | |
| Cell In[96], line 25 | |
| grid_search_result = grid_search.fit(Xtrain, Ytrain) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/base.py:1473 in wrapper | |
| return fit_method(estimator, *args, **kwargs) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1018 in fit | |
| self._run_search(evaluate_candidates) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1572 in _run_search | |
| evaluate_candidates(ParameterGrid(self.param_grid)) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/model_selection/_search.py:964 in evaluate_candidates | |
| out = parallel( | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/utils/parallel.py:74 in __call__ | |
| return super().__call__(iterable_with_config) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/joblib/parallel.py:1918 in __call__ | |
| return output if self.return_generator else list(output) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/joblib/parallel.py:1847 in _get_sequential_output | |
| res = func(*args, **kwargs) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/utils/parallel.py:136 in __call__ | |
| return self.function(*args, **kwargs) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:876 in _fit_and_score | |
| estimator = estimator.set_params(**clone(parameters, safe=False)) | |
| File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/scikeras/wrappers.py:1175 in set_params | |
| raise ValueError( | |
| ValueError: Invalid parameter activation for estimator KerasClassifier. | |
| This issue can likely be resolved by setting this parameter in the KerasClassifier constructor: | |
| `KerasClassifier(activation=relu)` | |
| Check the list of available parameters with `estimator.get_params().keys()` |
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| clf = KerasClassifier(model=create_model, | |
| model__activation = "relu", | |
| model__optimizer = "adam", | |
| epochs=10, batch_size=8) | |
| # Diccionario de hiperparametros | |
| param_grid = { | |
| 'activation': ['relu', 'sigmoid', 'tanh'], | |
| 'optimizer': ['adam', 'sgd', 'rmsprop'], | |
| } | |
| # Perform GridSearchCV | |
| grid_search = GridSearchCV(estimator=clf, | |
| param_grid=param_grid, | |
| cv=3) | |
| grid_search_result = grid_search.fit(Xtrain, Ytrain) |
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