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- from keras.layers import Input, Dense
- from keras.models import Model, Sequential
- sensor1 = Input(shape=(4,))
- sensor2 = Input(shape=(4,))
- sensor3 = Input(shape=(4,))
- sensor4 = Input(shape=(4,))
- sensor5 = Input(shape=(4,))
- sensor6 = Input(shape=(4,))
- sensor_model = Sequential([
- Dense(64, activation='relu'),
- Dense(64, activation='relu'),
- ])
- sensor1_encoding = sensor_model(sensor1)
- sensor2_encoding = sensor_model(sensor2)
- sensor3_encoding = sensor_model(sensor3)
- sensor4_encoding = sensor_model(sensor4)
- sensor5_encoding = sensor_model(sensor5)
- sensor6_encoding = sensor_model(sensor6)
- sensor_encoding = average([
- sensor1_encoding,
- sensor2_encoding,
- sensor3_encoding,
- sensor4_encoding,
- sensor5_encoding,
- sensor6_encoding,
- ])
- h = sensor_encoding
- h = Dense(128, activation='relu')(h)
- h = Dense(128, activation='relu')(h)
- h = Dense(3, activation='linear')
- model = Model(inputs=[sensor1, sensor2, sensor3, sensor4, sensor5, sensor6], outputs=[h])
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