# Keras-like API in ggmlR

ggmlR provides a high-level Keras-like interface for building and training neural networks on top of the ggml C tensor library. This vignette walks through the main APIs using the built-in `iris` and `mtcars` datasets.

## Installation

    # install.packages("ggmlR")   # once on CRAN
    library(ggmlR)

## 1. Sequential API

The sequential API mirrors `keras.Sequential`: stack layers with `|>`, compile, fit, evaluate, predict.

### 1.1 Prepare data — iris (3-class classification)

    data(iris)
    set.seed(42)

    x <- as.matrix(iris[, 1:4])
    x <- scale(x)                        # standardise

    # one-hot encode species
    y <- model.matrix(~ Species - 1, iris)  # [150 x 3]

    idx   <- sample(nrow(x))
    n_tr  <- 120L
    x_tr  <- x[idx[1:n_tr],  ]
    y_tr  <- y[idx[1:n_tr],  ]
    x_val <- x[idx[(n_tr+1):150], ]
    y_val <- y[idx[(n_tr+1):150], ]

### 1.2 Build and compile

    model <- ggml_model_sequential() |>
      ggml_layer_dense(32L, activation = "relu", input_shape = 4L) |>
      ggml_layer_batch_norm() |>
      ggml_layer_dropout(0.3, stochastic = TRUE) |>
      ggml_layer_dense(16L, activation = "relu") |>
      ggml_layer_dense(3L,  activation = "softmax") |>
      ggml_compile(
        optimizer = "adam",
        loss      = "categorical_crossentropy",
        metrics   = c("accuracy")
      )

    print(model)

    ## ggmlR Sequential Model
    ## ============================================================
    ## Layer                Output Shape         Params
    ## ------------------------------------------------------------
    ## dense                (32)                 160
    ## batch_norm           (32)                 64
    ## dropout              (32)                 0
    ## dense                (16)                 528
    ## dense                (3)                  51
    ## ============================================================
    ## Total parameters: 803
    ## Compiled: yes

### 1.3 Train

    model <- ggml_fit(
      model,
      x_tr, y_tr,
      epochs           = 100L,
      batch_size       = 32L,
      validation_split = 0.0,   # we supply our own val set below
      verbose          = 0L
    )

    ## Note: dropping last 24 sample(s) (120 -> 96) because batch_size=32 must divide evenly. Training metrics are computed on 96 samples only.

### 1.4 Evaluate and predict

    score <- ggml_evaluate(model, x_val, y_val, batch_size = 16L)
    cat(sprintf("Val loss: %.4f   Val accuracy: %.4f\n", score$loss, score$accuracy))

    ## Val loss: 0.1843   Val accuracy: 0.9333

    probs   <- ggml_predict(model, x_val, batch_size = 16L)
    classes <- apply(probs, 1, which.max)
    true    <- apply(y_val, 1, which.max)
    cat("Confusion matrix:\n")

    ## Confusion matrix:

    print(table(true = true, predicted = classes))

    ##     predicted
    ## true  1  2  3
    ##    1  9  0  0
    ##    2  0 10  1
    ##    3  0  1  9

### 1.5 Save and reload

    path <- tempfile(fileext = ".rds")
    ggml_save_model(model, path)
    cat(sprintf("Saved: %s (%.1f KB)\n", path, file.size(path) / 1024))

    ## Saved: /tmp/RtmpDfjZgK/file1ed0d67563463.rds (4.3 KB)

    model2 <- ggml_load_model(path)
    score2 <- ggml_evaluate(model2, x_val, y_val, batch_size = 16L)
    cat(sprintf("Reloaded model accuracy: %.4f\n", score2$accuracy))

    ## Reloaded model accuracy: 0.9333

## 2. Functional API

The functional API lets you build DAGs with multiple inputs/branches/outputs. Use `ggml_input()` to declare inputs, pipe through layers, then call `ggml_model()`.

### 2.1 Single-input DAG — residual-style network on iris

    inp  <- ggml_input(shape = 4L)

    h    <- inp |> ggml_layer_dense(32L, activation = "relu") |> ggml_layer_batch_norm()
    h    <- h   |> ggml_layer_dense(16L, activation = "relu")
    out  <- h   |> ggml_layer_dense(3L,  activation = "softmax")

    model_fn <- ggml_model(inputs = inp, outputs = out) |>
      ggml_compile(optimizer = "adam", loss = "categorical_crossentropy",
                   metrics = c("accuracy"))

    model_fn <- ggml_fit(model_fn, x_tr, y_tr,
                         epochs = 100L, batch_size = 32L, verbose = 0L)

    ## Truncating data from 120 to 96 samples (batch_size=32 must divide evenly)

    score_fn <- ggml_evaluate(model_fn, x_val, y_val, batch_size = 16L)
    cat(sprintf("Functional model accuracy: %.4f\n", score_fn$accuracy))

    ## Functional model accuracy: 0.9333

### 2.2 Multi-input model — mtcars regression

Predict `mpg` from two input groups: engine features and transmission features.

    data(mtcars)
    set.seed(7)

    # Input group 1: engine (disp, hp, wt)
    # Input group 2: transmission / gearbox (cyl, gear, carb, am)
    engine <- as.matrix(scale(mtcars[, c("disp","hp","wt")]))
    trans  <- as.matrix(scale(mtcars[, c("cyl","gear","carb","am")]))
    y_mpg  <- matrix(scale(mtcars$mpg), ncol = 1L)          # [32 x 1]

    # small dataset — use all for training, evaluate on same data for demo
    x1 <- engine;  x2 <- trans

    inp1 <- ggml_input(shape = 3L, name = "engine")
    inp2 <- ggml_input(shape = 4L, name = "transmission")

    branch1 <- inp1 |> ggml_layer_dense(16L, activation = "relu")
    branch2 <- inp2 |> ggml_layer_dense(16L, activation = "relu")

    merged  <- ggml_layer_add(list(branch1, branch2))        # element-wise add
    out_reg <- merged |>
      ggml_layer_dense(8L, activation = "relu") |>
      ggml_layer_dense(1L)

    model_reg <- ggml_model(inputs = list(inp1, inp2), outputs = out_reg) |>
      ggml_compile(optimizer = "adam", loss = "mse")

    model_reg <- ggml_fit(model_reg,
                          x = list(x1, x2), y = y_mpg,
                          epochs = 200L, batch_size = 16L, verbose = 0L)

    preds <- ggml_predict(model_reg, x = list(x1, x2), batch_size = 16L)
    cat(sprintf("Pearson r (scaled mpg): %.4f\n", cor(preds, y_mpg)))

    ## Pearson r (scaled mpg): 0.9070

## 3. Callbacks

Callbacks plug into `ggml_fit()` via the `callbacks` argument.

### 3.1 Early stopping

    cb_stop <- ggml_callback_early_stopping(
      monitor   = "val_loss",
      patience  = 15L,
      min_delta = 1e-4
    )

    model_cb <- ggml_model_sequential() |>
      ggml_layer_dense(32L, activation = "relu", input_shape = 4L) |>
      ggml_layer_dense(3L,  activation = "softmax") |>
      ggml_compile(optimizer = "adam", loss = "categorical_crossentropy")

    model_cb <- ggml_fit(model_cb, x_tr, y_tr,
                         epochs           = 300L,
                         batch_size       = 32L,
                         validation_split = 0.1,
                         callbacks        = list(cb_stop),
                         verbose          = 0L)

    ## Note: dropping last 24 sample(s) (120 -> 96) because batch_size=32 must divide evenly. Training metrics are computed on 96 samples only.

### 3.2 Learning-rate schedulers

    # Cosine annealing
    cb_cosine <- ggml_schedule_cosine_decay(T_max = 100L, eta_min = 1e-5)

    # Step decay: halve LR every 30 epochs
    cb_step <- ggml_schedule_step_decay(step_size = 30L, gamma = 0.5)

    # Reduce on plateau
    cb_plateau <- ggml_schedule_reduce_on_plateau(
      monitor  = "val_loss",
      factor   = 0.5,
      patience = 10L,
      min_lr   = 1e-6
    )

    model_lr <- ggml_model_sequential() |>
      ggml_layer_dense(32L, activation = "relu", input_shape = 4L) |>
      ggml_layer_dense(3L,  activation = "softmax") |>
      ggml_compile(optimizer = "adam", loss = "categorical_crossentropy")

    model_lr <- ggml_fit(model_lr, x_tr, y_tr,
                         epochs           = 150L,
                         batch_size       = 32L,
                         validation_split = 0.1,
                         callbacks        = list(cb_cosine),
                         verbose          = 0L)

    ## Note: dropping last 24 sample(s) (120 -> 96) because batch_size=32 must divide evenly. Training metrics are computed on 96 samples only.

## 4. Autograd Engine (`ag_*`)

The autograd engine provides PyTorch-style dynamic computation graphs. Tensors are column-major: shape is `[features, batch]`.

### 4.1 Prepare data (iris, col-major)

    # transpose: rows = features, cols = samples
    x_tr_ag  <- t(x_tr)    # [4, 120]
    y_tr_ag  <- t(y_tr)    # [3, 120]
    x_val_ag <- t(x_val)   # [4, 30]
    y_val_ag <- t(y_val)

### 4.2 Build a model with `ag_sequential`

    ag_mod <- ag_sequential(
      ag_linear(4L,  32L, activation = "relu"),
      ag_batch_norm(32L),
      ag_dropout(0.3),
      ag_linear(32L, 16L, activation = "relu"),
      ag_linear(16L,  3L)
    )

    params <- ag_mod$parameters()
    opt    <- optimizer_adam(params, lr = 1e-3)

### 4.3 Training loop

    BS <- 32L
    n  <- ncol(x_tr_ag)

    ag_train(ag_mod)
    set.seed(42)

    for (ep in seq_len(150L)) {
      perm <- sample(n)
      for (b in seq_len(ceiling(n / BS))) {
        idx <- perm[seq((b-1L)*BS + 1L, min(b*BS, n))]
        xb  <- ag_tensor(x_tr_ag[, idx, drop = FALSE])
        yb  <- y_tr_ag[, idx, drop = FALSE]

        with_grad_tape({
          loss <- ag_softmax_cross_entropy_loss(ag_mod$forward(xb), yb)
        })
        grads <- backward(loss)
        opt$step(grads)
        opt$zero_grad()
      }

      if (ep %% 50L == 0L)
        cat(sprintf("epoch %d  loss %.4f\n", ep, loss$data[1]))
    }

    ## epoch 50  loss 0.2152
    ## epoch 100  loss 0.0587
    ## epoch 150  loss 0.0081

### 4.4 Inference and accuracy

    ag_eval(ag_mod)

    # forward in chunks, apply softmax manually
    ag_predict_cm <- function(model, x_cm, chunk = 64L) {
      n   <- ncol(x_cm)
      out <- matrix(0.0, nrow(model$forward(ag_tensor(x_cm[,1,drop=FALSE]))$data), n)
      for (s in seq(1L, n, by = chunk)) {
        e  <- min(s + chunk - 1L, n)
        lg <- model$forward(ag_tensor(x_cm[, s:e, drop = FALSE]))$data
        ev <- exp(lg - apply(lg, 2, max))
        out[, s:e] <- ev / colSums(ev)
      }
      out
    }

    probs_ag <- ag_predict_cm(ag_mod, x_val_ag)          # [3, 30]
    pred_ag  <- apply(probs_ag, 2, which.max)
    true_ag  <- apply(y_val_ag, 1, which.max)            # col-major: rows = classes
    acc_ag   <- mean(pred_ag == true_ag)
    cat(sprintf("Autograd val accuracy: %.4f\n", acc_ag))

    ## Autograd val accuracy: 0.1000

### 4.5 LR scheduler + gradient clipping

    ag_mod2 <- ag_sequential(
      ag_linear(4L,  64L, activation = "relu"),
      ag_batch_norm(64L),
      ag_dropout(0.3),
      ag_linear(64L, 32L, activation = "relu"),
      ag_linear(32L,  3L)
    )
    params2 <- ag_mod2$parameters()
    opt2    <- optimizer_adam(params2, lr = 1e-3)
    sch2    <- lr_scheduler_cosine(opt2, T_max = 150L, lr_min = 1e-5)

    ag_train(ag_mod2)
    set.seed(42)

    for (ep in seq_len(150L)) {
      perm <- sample(n)
      for (b in seq_len(ceiling(n / BS))) {
        idx <- perm[seq((b-1L)*BS + 1L, min(b*BS, n))]
        xb  <- ag_tensor(x_tr_ag[, idx, drop = FALSE])
        yb  <- y_tr_ag[, idx, drop = FALSE]

        with_grad_tape({
          loss2 <- ag_softmax_cross_entropy_loss(ag_mod2$forward(xb), yb)
        })
        grads2 <- backward(loss2)
        clip_grad_norm(params2, grads2, max_norm = 5.0)
        opt2$step(grads2)
        opt2$zero_grad()
      }
      sch2$step()
    }

    ag_eval(ag_mod2)
    probs2 <- ag_predict_cm(ag_mod2, x_val_ag)
    acc2   <- mean(apply(probs2, 2, which.max) == true_ag)
    cat(sprintf("ag + cosine + clip  val accuracy: %.4f\n", acc2))

    ## ag + cosine + clip  val accuracy: 0.1000

### 4.6 Manual model from raw `ag_param`

For full control, build a network directly from parameters:

    make_net <- function(n_in, n_hidden, n_out) {
      W1 <- ag_param(matrix(rnorm(n_hidden * n_in) * sqrt(2/n_in),  n_hidden, n_in))
      b1 <- ag_param(matrix(0.0, n_hidden, 1L))
      W2 <- ag_param(matrix(rnorm(n_out * n_hidden) * sqrt(2/n_hidden), n_out, n_hidden))
      b2 <- ag_param(matrix(0.0, n_out, 1L))

      list(
        forward    = function(x)
          ag_add(ag_matmul(W2, ag_relu(ag_add(ag_matmul(W1, x), b1))), b2),
        parameters = function() list(W1=W1, b1=b1, W2=W2, b2=b2)
      )
    }

    set.seed(1)
    net    <- make_net(4L, 32L, 3L)
    opt_r  <- optimizer_adam(net$parameters(), lr = 1e-3)

    for (ep in seq_len(200L)) {
      perm <- sample(n)
      for (b in seq_len(ceiling(n / BS))) {
        idx <- perm[seq((b-1L)*BS+1L, min(b*BS, n))]
        xb  <- ag_tensor(x_tr_ag[, idx, drop = FALSE])
        yb  <- y_tr_ag[, idx, drop = FALSE]
        with_grad_tape({ loss_r <- ag_softmax_cross_entropy_loss(net$forward(xb), yb) })
        gr <- backward(loss_r)
        opt_r$step(gr);  opt_r$zero_grad()
      }
    }

    probs_r <- ag_predict_cm(net, x_val_ag)
    acc_r   <- mean(apply(probs_r, 2, which.max) == true_ag)
    cat(sprintf("Raw ag_param val accuracy: %.4f\n", acc_r))

    ## Raw ag_param val accuracy: 0.1333

## 5. GPU / device selection

    # Use Vulkan GPU if available, fall back to CPU
    device <- tryCatch({
      ag_device("gpu")
      "gpu"
    }, error = function(e) "cpu")

    cat("Running on:", device, "\n")

    ## Running on: gpu

    # Mixed precision (f16 on GPU, f32 on CPU)
    ag_dtype(if (device == "gpu") "f16" else "f32")

After calling `ag_device()` and `ag_dtype()`, all subsequent `ag_param` and `ag_tensor` calls use the selected device and dtype. The sequential API (`ggml_fit`) picks up the backend automatically.

## API summary

Task | Sequential / Functional | Autograd
--- | --- | ---
Build model | `ggml_model_sequential()` + `ggml_layer_*` | `ag_sequential()` or raw `ag_param`
Functional DAG | `ggml_input()` → `ggml_model()` | —
Compile | `ggml_compile()` | manual optimizer
Train | `ggml_fit()` | `with_grad_tape` + `backward` + `opt$step`
Evaluate | `ggml_evaluate()` | manual loop
Predict | `ggml_predict()` | manual forward
Save / load | `ggml_save_model()` / `ggml_load_model()` | —
Callbacks | `ggml_callback_early_stopping()`, `ggml_schedule_*` | `lr_scheduler_*`, `clip_grad_norm`
Device | automatic | `ag_device()` / `ag_dtype()`