I have the following code, using Keras Scikit-Learn Wrapper, which work fine:
from keras.models import Sequential
from keras.layers import Dense
from sklearn import datasets
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy as np
def create_model():
# create model
model = Sequential()
model.add(Dense(12, input_dim=4, init='uniform', activation='relu'))
model.add(Dense(6, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def main():
"""
Description of main
"""
iris = datasets.load_iris()
X, y = iris.data, iris.target
NOF_ROW, NOF_COL = X.shape
# evaluate using 10-fold cross validation
seed = 7
np.random.seed(seed)
model = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10, verbose=0)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(model, X, y, cv=kfold)
print(results.mean())
# 0.666666666667
if __name__ == '__main__':
main()
The pima-indians-diabetes.data
can be downloaded here.
Now what I want to do is to pass a value NOF_COL
into a parameter of create_model()
function the following way
model = KerasClassifier(build_fn=create_model(input_dim=NOF_COL), nb_epoch=150, batch_size=10, verbose=0)
With the create_model()
function that looks like this:
def create_model(input_dim=None):
# create model
model = Sequential()
model.add(Dense(12, input_dim=input_dim, init='uniform', activation='relu'))
model.add(Dense(6, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
But it fails giving this error:
TypeError: __call__() takes at least 2 arguments (1 given)
What's the right way to do it?
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)
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