‫Now that we've got our data in the form of tenses, more 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.
‫So we've done step one, get data ready, turning into tenses.
‫And 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, our 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, put the model to the data, make prediction, and then evaluate them and improve
‫and save our model later on.
‫So this is where up to now picking a model from TensorFlow 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 URL of
‫the model we want to use.
‫And you might be wondering, Earl, that's interesting.
‫Well, this is where we're going to find out what TensorFlow Hub is.
‫So let's write this down what we need.
‫We go before we build a model.
‫Now I want to also stress that in machine learning and deep learning and TensorFlow in particular,
‫there is 1000 ways to skin a cat, pun 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 costs 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.
‫Our image is shape in the form of tenses.
‫To our model and the output shape.
‫Image labels in the form of tenses of our model.
‫The URL of the model.
‫We want to use.
‫And we'll turn this into markdown.
‫Wonderful.
‫These three things.
‫Maybe not the URL.
‫You might build a model from scratch, but we're using a URL 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're using TensorFlow, everything will be in the form of tenses.
‫Set up 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.
‫Two, two, four, two, two, four, three.
‫Well, that is the input shape of our image.
‫And we've got batch height and width 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 breeds.
‫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 want it to input an image, find some patterns in that image, and then if we fed it an image
‫in the future, one it hasn't seen before, we'd like it to find patterns in that image and then output
‫after it's learned some patterns, output, an array of length, unique labels which hopefully contains
‫the correct label of what dog breed is in that image.
‫And now here's where we're going to go.
‫Set up model URL from TensorFlow hub.
‫Hmm.
‫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 TensorFlow 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 multiclass image classification.
‫Think about that and see what you can find.
‫Knowing that by just Googling TensorFlow hub and knowing our problem domain, what information can you
‫find out from TensorFlow hub?
‫Because that's exactly what we're going to be doing in the next video.
‫So have a try yourself and I'll see you soon.