All the 2880 fits failed.
It is very likely that your model is misconfigured.
You can try to debug the error by setting error_score=’raise’.

Below are more details about the failures:

2880 fits failed with the following error:
Traceback (most recent call last):
File “/home/devendra/anaconda3/lib/python3.10/site-packages/sklearn/model_selection/_validation.py”, line 888, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File “/home/devendra/anaconda3/lib/python3.10/site-packages/scikeras/wrappers.py”, line 1501, in fit
super().fit(X=X, y=y, sample_weight=sample_weight, **kwargs)
File “/home/devendra/anaconda3/lib/python3.10/site-packages/scikeras/wrappers.py”, line 770, in fit
self._fit(
File “/home/devendra/anaconda3/lib/python3.10/site-packages/scikeras/wrappers.py”, line 928, in _fit
self._ensure_compiled_model()
File “/home/devendra/anaconda3/lib/python3.10/site-packages/scikeras/wrappers.py”, line 439, in ensure_compiled_model
if not self.model.compiled:
AttributeError: ‘Sequential’ object has no attribute ‘compiled’

def create_model(neurons_1,neurons_2,optimizer,learning_rate,dropout):
    
    cnn = models.Sequential([
        layers.Conv1D(filters=neurons_1, kernel_size=8, activation='relu', input_shape=(82,1)),
        layers.MaxPooling1D(4),
        layers.BatchNormalization(),
        
        layers.Conv1D(filters=neurons_2, kernel_size=8, activation='relu'),
        layers.MaxPooling1D(4),
        layers.BatchNormalization(),
        
        layers.Flatten(),
        layers.Dense(100, activation='relu'),
        layers.Dropout(dropout),
        layers.Dense(50, activation='relu'),
        layers.Dropout(dropout),
        
        layers.Dense(2, activation='softmax')
    ])
            
    
    cnn.compile(optimizer=optimizer,
                  loss= tf.keras.losses.SparseCategoricalCrossentropy(),
                  metrics=['accuracy'])
    
    
    return cnn


model = KerasClassifier(model=create_model)

params={
    'epochs':[20,50,100],
    'model__neurons_1':[16,32,64,128],
    'model__neurons_2':[16,32,64,128],
    'model__optimizer':['adam','SGD'],
    'model__dropout':[0.2,0.5],
    'model__learning_rate':[0.001, 0.01, 0.1]
    
    }

# labels
y_val = train_df.TB_status.values
# data
X_val = train_df[feat_names]

grid= GridSearchCV(estimator=model,param_grid=params,cv=5,verbose=1)

transformer = RobustScaler().fit(X_val)
X_r_scaled = transformer.transform(X_val)
grid_search = grid.fit(X_r_scaled, y_val)