6-poissonCNN.R

# Compute probabilities and predictions on test set
predictions <-  predict_classes(model, test_array)
probabilities <- predict_proba(model, test_array)

# Visual inspection of 32 cases
set.seed(100)
random <- sample(1:nrow(testData), 32)
preds <- predictions[random,]
probs <- as.vector(round(probabilities[random,], 2))

par(mfrow = c(4, 8), mar = rep(0, 4))
for(i in 1:length(random)){
      image(t(apply(test_array[random[i],,,], 2, rev)),
            col = gray.colors(12), axes = F)
      legend("topright", legend = ifelse(preds[i] == 0, "Cat", "Dog"),
             text.col = ifelse(preds[i] == 0, 2, 4), bty = "n", text.font = 2)
      legend("topleft", legend = probs[i], bty = "n", col = "white")
}

# Save model
save(model, file = "CNNmodel.RData")