src/test/scripts/functions/federated/paramserv/FederatedParamservTest.dml

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source("src/test/scripts/functions/federated/paramserv/TwoNN.dml") as TwoNN
source("src/test/scripts/functions/federated/paramserv/CNN.dml") as CNN

# create federated input matrices
features = federated(addresses=list($X0, $X1),
    ranges=list(list(0, 0), list($examples_per_worker, $num_features),
                list($examples_per_worker, 0), list($examples_per_worker * 2, $num_features)))

labels = federated(addresses=list($y0, $y1),
    ranges=list(list(0, 0), list($examples_per_worker, $num_labels),
                list($examples_per_worker, 0), list($examples_per_worker * 2, $num_labels)))

epochs = $epochs
batch_size = $batch_size
learning_rate = $eta
utype = $utype
freq = $freq
network_type = $network_type

# currently ignored parameters
workers = 1
scheme = "DISJOINT_CONTIGUOUS"
paramserv_mode = "LOCAL"

# config for the cnn
channels = $channels
hin = $hin
win = $win

if(network_type == "TwoNN") {
  model = TwoNN::train_paramserv(features, labels, matrix(0, rows=0, cols=0), matrix(0, rows=0, cols=0), epochs, workers, utype, freq, batch_size, scheme, paramserv_mode, learning_rate)
}
else {
  model = CNN::train_paramserv(features, labels, matrix(0, rows=0, cols=0), matrix(0, rows=0, cols=0), channels, hin, win, epochs, workers, utype, freq, batch_size, scheme, paramserv_mode, learning_rate)
}
print(toString(model))