Navigate to the Evaluation view and watch the model train. You can see how the loss is getting lower epoch by epoch.

Interpreting the confusion matrix

When you created the model, you selected the binary crossentropy loss function. This is a loss function used on problems involving yes/no (binary) decisions. In your model, the decisions apply to the individual pixels in the input images. These pixels should be classified as either black or white.

The output from the last activation function (sigmoid) is a value between 0 and 1. Values below 0.5 are considered black and values equal to or above 0.5 are considered white. The model output is a grayscale image where the pixel intensity is represented by a value between 0 and 255.

Number of predictions in the confusion matrix

There are 60,000 input images with the dimension 28 x 28 and 20% of those are included in the validation set. This means that an approximate of the total number of values in the confusion matrix can be calculated as follows:

28 x 28 x 60,000 x 0.2 ≈ 9,408,000

To see the actual number of predictions, click Cells under the confusion matrix and select Count.

Figure 4. Model evaluation - Confusion matrix displays actual number of predictions

What about denoising?