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from keras.layers import Dense

def create_model():
    model = Sequential()
    model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
    model.add(Dense(384, activation='relu'))
    model.add(Dense(352, activation='relu'))
    model.add(Dense(448, activation='relu'))
    model.add(Dense(160, activation='relu'))
    model.add(Dense(160, activation='relu'))
    model.add(Dense(32, activation='relu'))
    model.add(Dense(1))
    model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
    return model
model = create_model()
model.summary()

X_train.shape