Loss, accuracy, and confusion matrices for training data

We will now obtain loss and accuracy values for the training data and then create a confusion matrix using the following code:

# Model evaluation
model %>% evaluate(trainx, trainLabels)

OUTPUT
12/12 [==============================] - 0s 87us/step
$loss
[1] 0.055556579307

$acc
[1] 1

# Confusion matrix
pred <- model %>%   predict_classes(trainx)
table(Predicted=pred, Actual=trainy)

OUTPUT
         Actual
Predicted 0 1 2
        0 3 0 0
        1 0 3 0
        2 0 0 3

As you can see from the preceding output, the loss and accuracy values are 0.056 and 1 respectively. The confusion matrix based on the training data indicates that all nine images are correctly classified into three categories, and therefore the resulting accuracy is 1.

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