Cannot clone object <keras.wrappers.scikit_learn.KerasRegressor object at 0x7fdc9c3ba550>

[ghost]

Trying to hypertune ANN but getting an error while using fit..(grid1.fit(X_train, y_train)) Below is the code

def create_model(dropout_rate,weight_constraint,optimizer,init,layers,activation): model = Sequential() model.add(Dense(nodes, input_dim=171, kernel_initializer=init, activation='relu', kernel_constraint=maxnorm(weight_constraint))) model.add(Dropout(dropout_rate)) model.add(Dense(1, kernel_initializer=init, activation='relu'))

model.compile(loss='mse', optimizer=optimizers, metrics=['mean_absolute_error'])
return model

model = KerasRegressor(build_fn=create_model, verbose=0)

hyperparameters

layers = [[50],[50, 20], [50, 30, 15], [70,45,15,5]] optimizers = ['rmsprop', 'adam'] dropout_rate = [0.1, 0.2, 0.3, 0.4] init = ['glorot_uniform', 'normal', 'uniform'] epochs = [150, 500] batches = [5, 10, 20] weight_constraint = [1, 2, 3] param_dist = dict(optimizer=optimizers, layers=layers, dropout_rate=dropout_rate, epochs=epochs, batch_size=batches, weight_constraint=weight_constraint, init=init )

grid1 = RandomizedSearchCV(estimator=model,param_distributions=param_dist,n_jobs=-1, cv=6)

grid1.fit(X_train, y_train)

`

[sebastian-steindl]

Had the same Problem. Try to Change layers = [[50],[50, 20], [50, 30, 15], [70,45,15,5]] To layers = [(50), (50,20), (50,30,15), (70,45,15,5)]

That worked for me.