Output order in Keras predict_generator

I have followed online tutorial about image recognition using Keras in R ending up with the following code:

library(keras)

view_list <- c("Inside", "Outside")
output_n <- length(view_list)

# image size to scale down to (original images are 100 x 100 px)
img_width <- 20
img_height <- 20
target_size <- c(img_width, img_height)

# RGB = 3 channels
channels <- 3

train_image_files_path <- "C:/Users/Tomek/Desktop/Photos"
valid_image_files_path <- "C:/Users/Tomek/Desktop/Photos valid"
test_image_files_path <- "C:/Users/Tomek/Desktop/Photos test"

# optional data augmentation
train_data_gen = image_data_generator(rescale = 1/255 )

# Validation data shouldn't be augmented! But it should also be scaled.
valid_data_gen <- image_data_generator(rescale = 1/255)  
test_data_gen <- image_data_generator(rescale = 1/255) 

# training images
train_image_array_gen <- flow_images_from_directory(train_image_files_path, 
                                                    train_data_gen,
                                                    target_size = target_size,
                                                    class_mode = "categorical",
                                                    classes = view_list,
                                                    seed = 42)


# validation images
valid_image_array_gen <- flow_images_from_directory(valid_image_files_path, 
                                                    valid_data_gen,
                                                    target_size = target_size,
                                                    class_mode = "categorical",
                                                    classes = view_list,
                                                    seed = 42)

# test images
test_image_array_gen <- flow_images_from_directory(test_image_files_path, 
                                                   test_data_gen,
                                                   target_size = target_size,
                                                   class_mode = "categorical",
                                                   classes = view_list,
                                                   seed = 42)

cat("Number of images per class:")
table(factor(train_image_array_gen$classes))
train_image_array_gen$class_indices

views_classes_indices <- train_image_array_gen$class_indices
save(views_classes_indices, file = "C:/Users/Tomek/Desktop/views_classes_indices.RData")

# number of training samples
train_samples <- train_image_array_gen$n
# number of validation samples
valid_samples <- valid_image_array_gen$n
# number of test samples
test_samples <- test_image_array_gen$n

# define batch size and number of epochs
batch_size <- 1
epochs <- 10

# initialise model
model <- keras_model_sequential()

# add layers
model %>%
  layer_conv_2d(filter = 32, kernel_size = c(3,3), padding = "same", input_shape = c(img_width, img_height, channels)) %>%
  layer_activation("relu") %>%

  # Second hidden layer
  layer_conv_2d(filter = 16, kernel_size = c(3,3), padding = "same") %>%
  layer_activation_leaky_relu(0.5) %>%
  layer_batch_normalization() %>%

  # Use max pooling
  layer_max_pooling_2d(pool_size = c(2,2)) %>%
  layer_dropout(0.25) %>%

  # Flatten max filtered output into feature vector 
  # and feed into dense layer
  layer_flatten() %>%
  layer_dense(100) %>%
  layer_activation("relu") %>%
  layer_dropout(0.5) %>%

  # Outputs from dense layer are projected onto output layer
  layer_dense(output_n) %>% 
  layer_activation("softmax")

# compile
model %>% compile(
  loss = "categorical_crossentropy",
  optimizer = optimizer_rmsprop(lr = 0.0001, decay = 1e-6),
  metrics = "accuracy"
)

summary(model)

# fit
hist <- model %>% fit_generator(
  # training data
  train_image_array_gen,

  # epochs
  steps_per_epoch = as.integer(train_samples / batch_size), 
  epochs = epochs, 

  # validation data
  validation_data = valid_image_array_gen,
  validation_steps = as.integer(valid_samples / batch_size),

  # print progress
  verbose = 2,
  callbacks = list(
    # save best model after every epoch
    callback_model_checkpoint("C:/Users/Tomek/Desktop/views_checkpoints.h5", save_best_only = TRUE),
    # only needed for visualising with TensorBoard
    callback_tensorboard(log_dir = "C:/Users/Tomek/Desktop/keras/logs")
  )
)
plot(hist)

#prediction
a <- model %>% predict_generator(test_image_array_gen, steps = 5, verbose = 1, workers = 1)
a <- round(a, digits = 4)

The classification model (with two output classes) seems to work quite nicely. The accuracy on the train and the validation sets is equal to ~99% and ~95% respectively. However, I am not sure about the results of predictions on the test set. It looks like the predictions for observations are shuffled and I am not able to find a way to check which prediction refers to which image(observation). I have seen some threads on that issue: github medium 1 medium 2.

Nevertheless, I am really new to Keras and Python and I have hard time applying the suggested solutions in R. What is the easiest way to track which prediction refers to which image from the test set in predict_generator output?