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

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


# create federated input matrices
features = read($features)
labels = read($labels)

if($network_type == "TwoNN") {
  if(!as.logical($modelAvg)) {
    model = TwoNN::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $seed)
    print("Test results:")
    [loss_test, accuracy_test] = TwoNN::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, list())
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
  else if (as.logical($modelAvg)){
    model = TwoNNModelAvg::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $seed, $modelAvg)
    print("Test results:")
    [loss_test, accuracy_test] = TwoNNModelAvg::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, list())
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
}
else if($network_type == "CNN") {
  if(!as.logical($modelAvg)) {
    model = CNN::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $channels, $hin, $win, $seed)
    print("Test results:")
    hyperparams = list(learning_rate=$eta, C=$channels, Hin=$hin, Win=$win)
    [loss_test, accuracy_test] = CNN::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, hyperparams)
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
  else if (as.logical($modelAvg)){
    model = CNNModelAvg::train_paramserv(features, labels, matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), 0, $epochs, $utype, $freq, $batch_size, $scheme, $runtime_balancing, $weighting, $eta, $channels, $hin, $win, $seed, $modelAvg)
    print("Test results:")
    hyperparams = list(learning_rate=$eta, C=$channels, Hin=$hin, Win=$win)
    [loss_test, accuracy_test] = CNNModelAvg::validate(matrix(0, rows=100, cols=784), matrix(0, rows=100, cols=10), model, hyperparams)
    print("[+] test loss: " + loss_test + ", test accuracy: " + accuracy_test + "\n")
  }
}