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

# Loss and accuracy
model %>% evaluate(trainx, trainy)
$loss
[1] 1.954347
$acc
[1] 0.8785

# Confusion matrix
pred <- model %>%   predict_classes(trainx)
table(Predicted=pred, Actual=data$train$y[1:2000,])
         Actual
Predicted   0   1   2   3   4   5   6   7   8   9
        0 182   0   5   2   3   0   2   0  10   1
        1   1 156   1   1   1   0   2   0   4   0
        2   2   0 172   3   4   0   4   0   1   0
        3   0   0   1 133   2  12   2   1   0   0
        4   1   0   8   4 188   3   4   2   0   0
        5   1   0   4  22   3 162   1   3   0   0
        6   0   0   3   9   3   0 192   1   1   0
        7   3   0   5  10  10   5   0 188   0   0
        8   5   0   3   3   0   1   0   1 182   0
        9   7  35   1   8   0   0   0   3   5 202

# Accuracy for each category
100*diag(tab)/colSums(tab)
       0        1        2        3        4 
90.09901 81.67539 84.72906 68.20513 87.85047 
       5        6        7        8        9 
88.52459 92.75362 94.47236 89.65517 99.50739 

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

• The loss and accuracy based on the training data are 1.954 and 0.879, respectively.
• Both of these numbers are an improvement over the corresponding results based on the previous model.
• The confusion matrix shows a decent image classification performance.
• The best image classification performance is seen for category 9 (truck), where only one image is misclassified as category-0 (airplane) and provides an accuracy of 99.5%.
• This model is the most confused regarding category-3 (cat), which is mostly classified as category-5 (dog) or category-7 (horse) and provides an accuracy of only 68.2% for this category.
• Among the misclassifications, the highest case (35 images) is when category-1 (automobile) is misclassified as category-9 (truck).

Next, we will assess the model's performance using the test data.