Now that we've got our data in the form of tenses most specifically batches of images in the form of
tenses and labels in the form of tenses we can go back to our little workflow.
Let's clean up our notebook a little bit and we can visualize what's happening with our data batches.
Let's go check out our workflow boom we've done.
Step 1 Get Data ready.
Turning into tenses.
Now this has taken a fairly long time to get to but this is what I want you to remember is that if we
go here what are we focused on.
A lot of our time is spent lining up our inputs with our outputs to make sure that they're consistent
with a machine learning algorithm.
So we want to input something in our case out photos of dogs with their labels in the form of tenses
into a machine learning algorithm and then we want it to output some sort of learned patterns so that
we can make predictions on more images over here fit the model to the data.
My prediction and then evaluate them and improve and save our model later on.
So this we're up to now picking a model from tensor flow hub to suit our problem a.k.a. our machine
learning algorithm that is already implemented.
Let's create a little section for doing that.
Let's go.
This is called building a model.
There's a few things we need to define and that is what we've just seen here.
The input shape of all of our images in the form of tenses to our model.
We need the output shape a.k.a. the image labels in the form of tenses of our model and the U.R.L. of
the model we want to use.
And you might be wondering you are El.
That's interesting.
Well this is where we're going to find out what tensor flow hobbits.
So let's write this down what we need to go before we build a model.
Now I want to also stress that in machine learning and deep learning intensive low in particular there
is a thousand ways to skin a cat.
Pardon the pun.
We're using dogs as our classifying problem.
This is only one way of how you can build a deep learning model and we're going to utilize transfer
learning so rather than building a model from scratch we're going to take what an existing model has
already learned and apply it to our problem which saves us on energy in terms of training a model could
take a long time cost because training a machine learning model isn't free.
And then once we've found some baseline results we can try and improve them from there.
So before we build a model there are a few things
we need to define.
One is the input shape how images shape in the form of tenses
to our model and the output shape image labels in the form of tenses of our model.
The U.R.L. of the model we want to use and we'll turn this into markdown.
Wonderful.
These three things maybe not the U.R.L. you might build a model from scratch but we're using a U.R.L.
so that we can get started experimenting as quick as possible these three things will be standard practice
with almost whatever machine learning model you use.
Defining the inputs defining the outputs and defining the model.
And because we use intensive flow everything will be in the form of tenses setup input shape to the
model.
And now
I'm going to define this as what it is and then we'll figure out where these come from in a second but
I want you to have a think about what this might be.
So where have we seen none.
Image size image size three before we go here.
Remember what image size is 224
so if we come up.
Remember how we checked the shape of our data batch.
None 2 2 4 2 2 4 3.
Well that is the input shape of our image.
And we've got batch height weight color channels wonderful.
And now we want to set up the output shape of our model if we want our model to predict labels what
might our output shape be how many different labels do we have.
So let's go.
Len unique reads
120 so we could just put that as 120 but what we might do is just write this in code so that's the output
shape of our model.
So we wanted to input an image find some patterns in that image and then if we fed it an image in the
future what it hasn't seen before we'd like it to find patterns in that image and then output after
it you learned some patterns output an array of length unique labels which hopefully contains the correct
label of what dog rate is in that image.
And now here's where we're gonna go set up model U.R.L. from tensor flow hub
eagles.
What might this be.
I want you before we get to the next video.
I'm going to go through it in the next video is to go through tensor flow hub and just have a play around.
Just have an explore and see if you can think about the problem that we're working on.
We're doing multi class image classification.
Think about that and see what you can find.
Knowing that by just googling tend to flow hob and knowing our problem domain what information can you
find out from tensor fly hub because that's exactly what we're going to be doing in the next video.
So I have a try yourself and I'll see you soon.