Welcome back.
Now let's print a summary of this model here.
Under the compilation print model summary model.
Plot summary.
Run.
If you have any error, go to runtime and click run all.
Maybe sometimes it will be disconnected.
Or maybe you restart your computer or join the class later on and you need to continue with that notebook.
So notebook will maybe disconnect.
Okay, so I prefer to use run all and then run this cell.
Here we have the model summary.
The model sequential which refers for a very simple linear regression model the linear type which is
dense.
The name you gave the layer dense.
The type of the layer which is fully connected.
And this is your only layer.
The entire model.
The output shape.
None and one.
None.
Batch size can vary any number of samples one.
Each prediction is a single number the price.
So one house one 110 houses ten one okay, because each prediction is a single number, which is the
price if we have 1010 houses, we need to get the only one thing to be predicted, which is the price.
Okay, param not total parameters.
Do.
This is the most important part.
What are those two parameters?
The slope and the intercept.
W and b.
So here if we go to our model here we have the price equals to 2.9 times size plus 2.7.
The slope is the size.
The intercept is b here okay.
This is a general formula for our model.
This is not exactly this because price equals to 2.9 times size plus 2.7.
This is from the previous example and previous model.
But in our application we have exactly similar to what we've learned y equal to ax plus b a the slope
and b the intercept okay.
So those are the two parameters that our machine learning model we're going to learn.
For that we have two parameters okay.
This is how we get a model summary.