Understanding Keras prediction output of a rnn model in R

dir.create("~/Downloads/jena_climate", recursive = TRUE) download.file( "https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip", "~/Downloads/jena_climate/jena_climate_2009_2016.csv.zip" ) unzip( "~/Downloads/jena_climate/jena_climate_2009_2016.csv.zip", exdir = "~/Downloads/jena_climate" ) library(readr) data_dir <- "~/Downloads/jena_climate" fname <- file.path(data_dir, "jena_climate_2009_2016.csv") data <- read_csv(fname) data <- data.matrix(data[,-1]) train_data <- data[1:200000,] mean <- apply(train_data, 2, mean) std <- apply(train_data, 2, sd) data <- scale(data, center = mean, scale = std) generator <- function(data, lookback, delay, min_index, max_index, shuffle = FALSE, batch_size = 128, step = 6) { if (is.null(max_index)) max_index <- nrow(data) - delay - 1 i <- min_index + lookback function() { if (shuffle) { rows <- sample(c((min_index+lookback):max_index), size = batch_size) } else { if (i + batch_size >= max_index) i <<- min_index + lookback rows <- c(i:min(i+batch_size, max_index)) i <<- i + length(rows) } samples <- array(0, dim = c(length(rows), lookback / step, dim(data)[[-1]])) targets <- array(0, dim = c(length(rows))) for (j in 1:length(rows)) { indices <- seq(rows[[j]] - lookback, rows[[j]], length.out = dim(samples)[[2]]) samples[j,,] <- data[indices,] targets[[j]] <- data[rows[[j]] + delay,2] } list(samples, targets) } } lookback <- 1440 step <- 6 delay <- 144 batch_size <- 128 train_gen <- generator( data, lookback = lookback, delay = delay, min_index = 1, max_index = 200000, shuffle = TRUE, step = step, batch_size = batch_size ) val_gen = generator( data, lookback = lookback, delay = delay, min_index = 200001, max_index = 300000, step = step, batch_size = batch_size ) test_gen <- generator( data, lookback = lookback, delay = delay, min_index = 300001, max_index = NULL, step = step, batch_size = batch_size ) # How many steps to draw from val_gen in order to see the entire validation set val_steps <- (300000 - 200001 - lookback) / batch_size # How many steps to draw from test_gen in order to see the entire test set test_steps <- (nrow(data) - 300001 - lookback) / batch_size library(keras) model <- keras_model_sequential() %>% layer_flatten(input_shape = c(lookback / step, dim(data)[-1])) %>% layer_dense(units = 32, activation = "relu") %>% layer_dense(units = 1) model %>% compile( optimizer = optimizer_rmsprop(), loss = "mae" ) history <- model %>% fit_generator( train_gen, steps_per_epoch = 500, epochs = 20, validation_data = val_gen, validation_steps = val_steps )

error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 2 arrays: [array([[[ 0.50394005, 0.6441838 , 0.5990761 , ..., 0.22060473, 0.2018686 , -1.7336458 ], [ 0.5475698 , 0.63853574, 0.5890239 , ..., -0.45618412, -0.45030192, -1.724062...

Note: my familiarity with syntax of R is very little, so unfortunately I can't give you an answer using R. Instead, I am using Python in my answer. I hope you could easily translate back, my words at least, to R.

... If I am correct, this should give me the normalized predicted temperature for every batch.

Yes, that's right. The predictions would be normalized since you have trained it with normalized labels:

data <- scale(data, center = mean, scale = std)

Therefore, you would need to denormalize the values using the computed mean and std to find the real predictions:

pred = model.predict(test_data)
denorm_pred = pred * std + mean

... for which time is then predicted (latest observation time + delay?)

That's right. Concretely, since in this particular dataset every ten minutes a new obeservation is recorded and you have set delay=144, it would mean that the predicted value is the temperature 24 hours ahead (i.e. 144 * 10 = 1440 minutes = 24 hours) from the last given observation.

Also, what is the correct way to use keras::predict_generator() and the test_gen() function?

predict_generator takes a generator that gives as output only test samples and not the labels (since we don't need labels when we are performing prediction; the labels are needed when training, i.e. fit_generator(), and when evaluating the model, i.e. evaluate_generator()). That's why the error mentions that you need to pass one array instead of two arrays. So you need to define a generator that only gives test samples or one alternative way, in Python, is to wrap your existing generator inside another function that gives only the input samples (I don't know whether you can do this in R or not):

def pred_generator(gen):
    for data, labels in gen:
        yield data  # discards labels

preds = model.predict_generator(pred_generator(test_generator), number_of_steps)

You need to provide one other argument which is the number of steps of generator to cover all the samples in test data. Actually we have num_steps = total_number_of_samples / batch_size. For example, if you have 1000 samples and each time the generator generate 10 samples, you need to use generator for 1000 / 10 = 100 steps.

Bonus: To see how good your model performs you can use evaluate_generator using the existing test generator (i.e. test_gen):

loss = model.evaluate_generator(test_gen, number_of_steps)

The given loss is also normalized and to denormalize it (to get a better sense of prediction error) you just need to multiply it by std (you don't need to add mean since you are using mae, i.e. mean absolute error, as the loss function):

denorm_loss = loss * std

This would tell you how much your predictions are off on average. For example, if you are predicting the temperature, a denorm_loss of 5 means that the predictions are on average 5 degrees off (i.e. are either less or more than the actual value).

Update: For prediction, you can define a new generator using an existing generator in R like this:

pred_generator <- function(gen) {
  function() { # wrap it in a function to make it callable
    gen()[1]  # call the given generator and get the first element (i.e. samples)
  }
}

preds <- model %>% 
  predict_generator(
    generator = pred_generator(test_gen), # pass test_gen directly to pred_generator without calling it
    steps = test_steps
  )

evaluate_generator(model, test_gen, test_steps)

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