How to pass a parameter to Scikit-Learn Keras model function

You can add an input_dim keyword argument to the KerasClassifier constructor:

model = KerasClassifier(build_fn=create_model, input_dim=5, nb_epoch=150, batch_size=10, verbose=0)

Last answer does not work anymore.

An alternative is to return a function from create_model, as KerasClassifier build_fn expects a function:

def create_model(input_dim=None):    def model():        # create model        nn = Sequential()        nn.add(Dense(12, input_dim=input_dim, init='uniform', activation='relu'))        nn.add(Dense(6, init='uniform', activation='relu'))        nn.add(Dense(1, init='uniform', activation='sigmoid'))        # Compile model        nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])        return nn    return model

Or even better, according to documentation

sk_params takes both model parameters and fitting parameters. Legal model parameters are the arguments of build_fn. Note that like all other estimators in scikit-learn, build_fn should provide default values for its arguments, so that you could create the estimator without passing any values to sk_params

So you can define your function like this:

def create_model(number_of_features=10): # 10 is the *default value*    # create model    nn = Sequential()    nn.add(Dense(12, input_dim=number_of_features, init='uniform', activation='relu'))    nn.add(Dense(6, init='uniform', activation='relu'))    nn.add(Dense(1, init='uniform', activation='sigmoid'))    # Compile model    nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])    return nn

And create a wrapper:

KerasClassifier(build_fn=create_model, number_of_features=20, epochs=25, batch_size=1000, ...)

To pass a parameter to build_fn model, can be done passing arguments to __init__() and in turn it will be passed to model_build_fn directly. For example, calling KerasClassifier(myparam=10) will result in a model_build_fn(my_param=10)

here's an example:

class MyMultiOutputKerasRegressor(KerasRegressor):        # initializing    def __init__(self, **kwargs):        KerasRegressor.__init__(self, **kwargs)            # simpler fit method    def fit(self, X, y, **kwargs):        KerasRegressor.fit(self, X, [y]*3, **kwargs)

(...)

def get_quantile_reg_rpf_nn(layers_shape=[50,100,200,100,50], inDim= 4, outDim=1, act='relu'):          # do model stuff...

(...)initialize the Keras regressor:

base_model = MyMultiOutputKerasRegressor(build_fn=get_quantile_reg_rpf_nn,                                         layers_shape=[50,100,200,100,50], inDim= 4,                                          outDim=1, act='relu', epochs=numEpochs,                                          batch_size=batch_size, verbose=0)