Abstract
To achieve the recognition of multi-attribute of object, I
redesign the mnist dataset, change the color, size, location of the
number. Meanwhile, I change the label accordingly.
The deep neural network I use is the most common convolution
neural network. Through test,we can conclude that we can use one neural
network to recognize multi-attribute so long as the attribute difference
of objects can be represented by functions. The concrete
network(generation network) can generate the output which the input
rarely contained from the attributes the network learned. Its
generalization ability is good because the network is a continuous
function. Through one more test, We can conclude that one neural network
can do image recognition, speech recognition,and nature language
processing and other things so long as the output node and the input
node and more parameters add into the network. The network is universal
so long as the network can process different inputs. I guess
that the phenomenon of synaesthesia is the result of multi-input and
multi-output. I guess that connection in mind can realize
through the universal network and sending the output into input.
