def create_model(neurons=1):
# create model
model = Sequential()
model.add(Dense(neurons, input_dim=8, init='uniform', activation='softplus', W_constraint=maxnorm(4)))
model.add(Dropout(0.1))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# fix random seed for reproducibility
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