tensorflowwide & Deep模型

wide & Deep模型

稀疏特徵：離散數值（可叉乘）
密集特徵：向量表達

示例

函數式API

#deep
input = keras.layers.Input(shape = x_train.shape[1:])
hidden1 = keras.layers.Dense(30,activation = 'relu')(input)
hidden2 = keras.layers.Dense(30,activation = 'relu')(hidden1) 
#wide
#直接有輸入值進行傳遞

#拼接
concat = keras.layers.concatenate([input,hidden2])
#輸出
output = keras.layers.Dense(1)(concat)
#固化模型
concat = keras.models.Model(inputs = [input],
                                                              outputs = [output])

子類API

class WideDeepModel(keras.models.Model):
    def __init__(self):
        super(WideDeepModel, self).__init__()
        """定義模型的層次"""
        self.hidden1_layer = keras.layers.Dense(30,activation="relu")
        self.hidden2_layer = keras.layers.Dense(30,activation="relu")
        self.output_layer = keras.layers.Dense(1)
        
    def call(self, input):
        """完成模型的正向計算"""
        hidden1 = self.hidden1_layer(input)
        hidden2 = self.hidden2_layer(hidden1)
        concat = keras.layers.concatenate([input,hidden2])
        output = self.output_layer(concat)
        return output

#model = WideDeepModel() #這種方法或者下一種方法都可以
model = keras.models.Sequential([
    WideDeepModel(),
])
model.build(input_shape=(None, 8)) #(樣本的數目，輸入的fetch的數目)
#這樣只是定義層仍需要compile和fit

多輸入

# 多輸入
input_wide = keras.layers.Input(shape = [5])
input_deep = keras.layers.Input(shape = [6])
hidden1 = keras.layers.Dense(30,activation="relu")(input_deep)
hidden2 = keras.layers.Dense(30,activation="relu")(hidden1)
concat = keras.layers.concatenate([input_wide, hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.models.Model(inputs = [input_wide, input_deep],
                            outputs = [output])

model.summary()
model.compile(loss = "mean_squared_error",optimizer = "sgd")
callbacks = [keras.callbacks.EarlyStopping(
    patience=5,min_delta=1e-2)]

擴展多輸出

# 多輸出
input_wide = keras.layers.Input(shape = [5])
input_deep = keras.layers.Input(shape = [6])
hidden1 = keras.layers.Dense(30,activation="relu")(input_deep)
hidden2 = keras.layers.Dense(30,activation="relu")(hidden1)
concat = keras.layers.concatenate([input_wide, hidden2])
output = keras.layers.Dense(1)(concat)
output2 = keras.layers.Dense(1)(hidden2)
model = keras.models.Model(inputs = [input_wide, input_deep],
                            outputs = [output,output2])

model.summary()
model.compile(loss = "mean_squared_error",optimizer = "sgd")
callbacks = [keras.callbacks.EarlyStopping(
    patience=5,min_delta=1e-2)]