Convolutional Neural Network Predictions with TensorFlow's Keras API

Key Takeaways

Hey I may be from deep lizard in this episode we'll demonstrate how to use a convolutional neural network for inference to predict on image data using tensor flows integrated Kerris API [Music] last time we built and trained our first CN n against cat and dog image data and we saw that the training results were great with the model achieving 100% accuracy on the training set however it lagged behind by quite a good bit at only 70 percent accuracy on our validation set so that tells us that the model wasn't generalizing as well as we hoped but nonetheless we are going to use our model now for inference to predict on cat and dog images in our test set given the less than decent results that we solve from the validation performance our expectation is that the model is not going to do so well on the test set either it's probably going to perform at around the same 70 percent rate but this is still going to give us exposure about how we can use a CNN for inference using the Charis sequential API alright so we are back in our jupiter notebook and we need to make sure that we have all the code in place from the last couple of episodes as we will be continuing to make it use of both our model that we built last time as well as our test data from whenever we prepared the data sets so the first thing we're going to do is get a batch of test data from our test batches and then we're going to plot that batch we're going to plot the images specifically and then we're going to print out the corresponding labels for those images and we're using before we do that just a reminder this plot images function that we introduced in the last couple of episodes all right so if we scroll down we have our test batches recall we have the discussion about why the image data looks the way that it does in terms of the color being skewed last time but we can see that just by looking even though we have kind of distorted color we have these are all actually cats here and by looking at the corresponding label for these images we can see that they are all labeled with the one hot and coded vector of one zero which we know is the label for cat so if you're wondering why we have all cats as our first 10 images in our first batch here that is because recall whenever we created the test set we specified that we did not want it to be shuffled and that was so that we could do the following if we come to our next cell and we run test batches dot classes then we can get a array that has all of the corresponding labels for each image in the test set so given that we have access to the unshelled labels for the test set that's why we don't want to shuffle the test set directly because we want to be able to have this one one direct mapping from the unshelled labels to the test data set and if we were to shuffle the test data set every time we generated a batch then we wouldn't be able to have the correct mapping between labels and samples so we care about having the correct mapping because later after we get our predictions from the model we're going to want to plot our predictions to a confusion matrix and so we want the corresponding labels that belong to the samples in the test set alright so next we're actually going to go ahead and obtain our predictions by calling model dot predict just as we have in earlier episodes for other data sets and 2x we are specifying our test batches so all of our test data set and we are choosing verbose to be 0 to give no output whenever we run our predictions so here we are just printing out the rounded predictions from the model so the way that we can read this is first each one of these arrays is a prediction for a single sample so if we just look at the first one this is the prediction for the first sample and the test set so this one wherever there is a one with each prediction the one is the index for the output class that had the highest probability from the model so in this case we see that the 0th index had the highest probability so we can just say that the label that the model predicted for this first sample was a zero because we see that there is a 1 here in the 0th index and if we look at the first element or if we look at the first label here up here for our first test sample it is indeed a zero so we can eyeball that and see that the model did accurately predict all the way down to here because that's the first 1 2 3 4 5 6 and 1 2 3 4 5 6 ok so but then whenever we see that the model predicted the first index to be the highest probability that means that the problem that the model predicted an output label of a 1 and so that corresponds to dog so it's hard for us to kind of draw an overall conclusion about the prediction accuracy for this test set just eyeballing the results like this but if we scroll down then we know that we have the tool of a confusion matrix that we can use to make visualizing these results much easier like we've seen in previous episodes of this course already so we are going to do that now we're going to create a confusion matrix using this confusion matrix function from scikit-learn which we've already been introduced to and we are passing in our true labels using test batches classes recall that we just touched on that a few minutes ago and for our predicted labels we are passing in the predictions from our model we are getting the we're actually passing in the index of each we're actually using Arg max to pass in the index of where the most probable prediction was from our predictions list so this is something that we've already covered in previous episodes for why we do that so if we run that we're now going to bring in at this plot confusion matrix which we've discussed is directly from scikit-learn website link to that is in the corresponding blog for this episode on beefless or calm this is just going to allow us to plot our confusion matrix in a moment and now if we look at the class indices we see that cat is first and dog is second so we just need to look at that so that we understand in which order we should put our plot labels for our confusion matrix and next we call plot confusion matrix and pass in the confusion matrix itself as well as the labels for the confusion matrix and a title for the entire matrix so let's check that out all right so from what we learned about how we can easily interpret a confusion matrix we know that we can just look at this diagonal here running from top left to bottom right to see what the model predicted correctly so not that great the model is definitely overfitting at this point so like I said if this was the model that we were really concerned about then we would definitely want to combat that overfitting problem but for now we are going to move on to a new model using a pre trained state-of-the-art model called vgg 16 in the next episode so that we can see how well that model does on classifying images of cats and dogs by the way we are currently in Vietnam filming this episode if you didn't know we also have a vlog channel where we document our travels and share a little bit more about ourselves so check that out at Peebles our vlog on YouTube also be sure to check out the corresponding blog for this episode along with other resources available on deep loser calm and check out the people's archive mine where you can gain exclusive access to perks and rewards thanks for contributing to collective intelligence I'll see you next time [Music] [Music]