The code for obtaining the loss, accuracy, and confusion matrix for the test data is as follows:

# Loss and accuracy
model %>% evaluate(testx, testy)
$loss
[1] 4.437256
$acc
[1] 0.768

# Confusion matrix
pred <- model %>% predict_classes(testx)
table(Predicted = pred, Actual = data$test$y[1:2000,])
         Actual
Predicted   0   1   2   3   4   5   6   7   8   9
        0 158   1  12   0   5   1   6   2  15   0
        1   3 142   0   2   0   2   3   1   9   2
        2   2   0 139   8   6   3   6   0   0   0
        3   0   0   3  86   5  13   6   1   0   0
        4   4   0  14   6 138   5  10   4   1   0
        5   0   0  15  47   6 148   2  12   0   0
        6   0   0   4  12   9   3 178   0   0   0
        7   2   0   4  23  27   9   3 169   0   0
        8  13   1   1   5   1   0   0   0 179   2
        9  14  54   3  10   1   1   2   4  13 199

# Accuracy for each category
100*diag(tab)/colSums(tab)
       0        1        2        3        4 
80.61224 71.71717 71.28205 43.21608 69.69697 
       5        6        7        8        9 
80.00000 82.40741 87.56477 82.48848 98.02956 

From the preceding output, we can make the following observations:

• The loss and accuracy based on the test data are 4.437 and 0.768, respectively.
• Although this performance based on the test data is inferior to the results based on the training data, it is a significant improvement over the results from the first model.

• The confusion matrix provides further insights into the model's performance. The best performance is for category 9 (truck), with 199 correct classifications and an accuracy of 98%.
• For the test data, the model seems to be the most confused regarding category-3 (cat), which has the most misclassifications. The accuracy of this category can be as low as 43.2%.
• The highest misclassification for a single category (54 images) is for category-1 (automobile), which is misclassified as category-9 (truck).

With 76.8% accuracy, we can say that this image classification performance is decent. The use of a pretrained model has allowed us to transfer our learning of a model trained on data involving over 1 million images to new data containing 2,000 images from the CIFAR10 dataset. This is a huge advantage compared to building an image classification model totally from scratch, which would involve more time and computing costs. Now that we've achieved a decent performance from the model, we can explore how to improve this even further.