Hello.
Welcome back.
Now let's take a look at the multiple input multiple output neural network the multiple inputs multiple
outputs neural network is a combination of the multiple inputs single output in Europe networks.
Now let's say given the temperature humidity and air quality.
Apart from just wanting to predict what a person is happy or sad we also want to predict whether the
sick or healthy as well as predict whether they are active or inactive.
This is what the neural network would look like.
The simplest way to solve this is to view it as a three separate multiple input single output neural
networks.
First we take the input of temperature humidity and air quality.
Multiply each input with its respective weight and add up the three product.
Then we get the prediction for happy or sad.
We repeat the same to predict sick or healthy and then we repeat it again to get active or inactive.
Note that the inputs for predicting the three outputs are the same.
However the weights are different.
Different sets of weight is used to predict the three different output.
We can arrange the weights as a two dimensional array like this the first row represents the weight
for the prediction for sad or happy.
The second rule represents the weight for the prediction for sick or healthy and the third row represent
the weight for the prediction of active or inactive.
In this arrangement the first column in each row represent wait for the temperature inputs.
The second column represents weight for the humidity input and the third represent weight for the air
quality input to compute the multiple input multiple outputs new network.
We have to perform a vector matrix multiplication like this like this function over here.
The vector is the one dimensional array that contains our inputs and The Matrix is the 2 dimensional
array that contains the weight the shadow code will compute the throughput predictions of our multiple
inputs multiple outputs.
You can post a video to take a look at this.
I'm sure it's very straightforward.
Now let's write some code to demonstrate this.