问题描述
神经网络通常用于分类.因此，输出层中神经元的激活指示了您要分类的内容的类别.
Neural Networks are mostly used to classify. So, the activation of a neuron in the output layer indicates the class of whatever you are classifying.
是否有可能(并且正确)设计NN以获取3D坐标?这是三个输出神经元，每个神经元的值都在例如[-1000.0，1000.0]的范围内.
Is it possible (and correct) to design a NN to get 3D coordinates? This is, three output neurons with values in ranges, for example [-1000.0, 1000.0], each one.
推荐答案
是.您可以使用神经网络执行线性回归，以及更复杂的回归类型，其中输出层具有多个可以解释为3-D坐标的节点(或高维元组).
Yes. You can use a neural network to perform linear regression, and more complicated types of regression, where the output layer has multiple nodes that can be interpreted as a 3-D coordinate (or a much higher-dimensional tuple).
要在TensorFlow中实现此目的，您将创建一个包含三个输出神经元的最终层，每个神经元分别对应于目标坐标的不同维度，然后最小化每个示例的当前输出和已知值之间的均方根误差.
To achieve this in TensorFlow, you would create a final layer with three output neurons, each corresponding to a different dimension of your target coordinates, then minimize the root mean squared error between the current output and the known value for each example.
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