src/test/scripts/functions/paramserv/paramserv-minimum-version.dml

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source("src/test/scripts/functions/paramserv/mnist_lenet_paramserv_minimum_version.dml") as mnist_lenet
source("scripts/nn/layers/cross_entropy_loss.dml") as cross_entropy_loss

# Generate the training data
[images, labels, C, Hin, Win] = mnist_lenet::generate_dummy_data()
n = nrow(images)

# Generate the training data
[X, Y, C, Hin, Win] = mnist_lenet::generate_dummy_data()

# Split into training and validation
val_size = n * 0.1
X = images[(val_size+1):n,]
X_val = images[1:val_size,]
Y = labels[(val_size+1):n,]
Y_val = labels[1:val_size,]

# Arguments
epochs = 10
workers = 2
batchsize = 64

# Train
[W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, Y, X_val, Y_val, C, Hin, Win, epochs, workers)

# Compute validation loss & accuracy
probs_val = mnist_lenet::predict(X_val, C, Hin, Win, batchsize, W1, b1, W2, b2, W3, b3, W4, b4)
loss_val = cross_entropy_loss::forward(probs_val, Y_val)
accuracy_val = mean(rowIndexMax(probs_val) == rowIndexMax(Y_val))

# Output results
print("Val Loss: " + loss_val + ", Val Accuracy: " + accuracy_val)