问题描述
我有一个问题，当给定一个预测变量向量时预测两个输出.假设一个预测向量看起来像 x1, y1, att1, att2, ..., attn，它说 x1, y1 是坐标，而 att's > 是附加到 x1, y1 坐标出现的其他属性.基于这个预测器集，我想预测 x2, y2.这是一个时间序列问题，我正在尝试使用多重回归来解决它.我的问题是如何设置 keras，它可以在最后一层给我 2 个输出.
I have a problem which deals with predicting two outputs when given a vector of predictors.Assume that a predictor vector looks like x1, y1, att1, att2, ..., attn, which says x1, y1 are coordinates and att's are the other attributes attached to the occurrence of x1, y1 coordinates. Based on this predictor set I want to predict x2, y2. This is a time series problem, which I am trying to solve using multiple regresssion.My question is how do I setup keras, which can give me 2 outputs in the final layer.
推荐答案
from keras.models import Model
from keras.layers import *
#inp is a "tensor", that can be passed when calling other layers to produce an output
inp = Input((10,)) #supposing you have ten numeric values as input
#here, SomeLayer() is defining a layer,
#and calling it with (inp) produces the output tensor x
x = SomeLayer(blablabla)(inp)
x = SomeOtherLayer(blablabla)(x) #here, I just replace x, because this intermediate output is not interesting to keep
#here, I want to keep the two different outputs for defining the model
#notice that both left and right are called with the same input x, creating a fork
out1 = LeftSideLastLayer(balbalba)(x)
out2 = RightSideLastLayer(banblabala)(x)
#here, you define which path you will follow in the graph you've drawn with layers
#notice the two outputs passed in a list, telling the model I want it to have two outputs.
model = Model(inp, [out1,out2])
model.compile(optimizer = ...., loss = ....) #loss can be one for both sides or a list with different loss functions for out1 and out2
model.fit(inputData,[outputYLeft, outputYRight], epochs=..., batch_size=...)
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