Keras MLP

input layer: 60 nodes
hidden layer 1: 60 nodes, activiation=relu, dropout=20%
hidden layer 2: 30 nodes, activation = relu, dropout=20%
output layer: 1 nodes, activation= sigmoid

model = Sequential()
model.add(Dense(60, input_dim=60, init='normal', activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(30, init='normal', activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(1, init='normal', activation='sigmoid'))

# Compile model
sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])


model = Sequential()
model.add(Dense(400, input_dim = xtrain.shape[1], init = 'he_normal'))
model.add(PReLU())
model.add(Dropout(0.4))
model.add(Dense(200, init = 'he_normal'))
model.add(PReLU())
model.add(Dropout(0.2))
model.add(Dense(1, init = 'he_normal'))
model.compile(loss = 'mae', optimizer = 'adadelta')
return(model)