I've run it it's given me the information from when I'm created the model and now it just starts going through we get this information tensor loss equals global step all kinds of stuff going on here and if you're following this it's just going through the steps in training it it gives you information it could dig deep into here but for this particular setup we're not gonna go too deep on what's going on just know that we've trained our model this model now has the information we need in it to start running predictions so as we sip our next take our next step of coffee or maybe it's tea or if you're one of those dreams late-night workers maybe it's a sip of wine .
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We go into the next step and we actually want to run some predictions on here but we don't want to run the training we want to run the test on there we want to take our test data and see what it did and suppose we're gonna do next is we're gonna run the test through and actually get some answers so if you were actually deploying it you would pull the answers out of the data it's bringing back let's take a look at that in our Jupiter notebook so here .
We go let's paste it in there I'm gonna go ahead and run it and you'll see that as it goes it's actually putting the answers out if you remember correctly well walk through this here in just a second but it goes through and it runs each line and it gives you a prediction for each line one at a time and prints them out so let's take a look and see what that actually looks like let's start with this first part we've another function this is just like the other function except for x equals x test and there's no why why is there no y because we don't know what the answer is on this yet we don't want to tell it the answer we wanted to guess what the answer is so we can evaluate that and see how good it did on that 33% of the data so this is our X test batch size is 10 again .
So if we were watching this roll down here we would see that it actually processes at 10 lines at a time it's only going to go through once it goes through all the X test data one time we don't need to have it predict multiple times and shuffle equals false very important we set the shuffle to false because if we were tracking this and actually giving people answers we'd want to make sure it connects to the right person so they get the right results of course we're just doing this to evaluate it so let's take a look down here what I put out and as.
I scroll down to my jupiter notebook we have some information as far as a tensor flow running and then the actual output and if we look at the output we know by this first bracket in python it's an array we know by this squiggly bracket that this is a dictionary so that this is a label the dictionary has logistic probabilities class IDs .classes and so this whole first part let me redo that this whole first part is one output and we know that because there is the bracket there and there is this bracket here for the dictionary and it's a dictionary of terms so if I pulled this out and I looked at object 0 in here. I would go down to let me just find it here it is classes remember how we defined classes we defined classes as our answer and so it's guessing.
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