R torch Deep Learning Cheat Sheet for Tensors and Training Loops
A practical R torch reference for tensors, automatic differentiation, modules, training loops, devices and model saving.
What this reference covers
This sheet follows an R torch workflow from tensors and forward computation to loss, gradients, optimizers, batches, device placement and model saving. Starting with a small sample makes shape, type and device errors visible before a long training run.
Batch dimensions, labels, numeric ranges and device placement all affect the result. Validate one forward and backward pass, then add batches, layers and hardware. A checkpoint is only useful when its model structure, preprocessing, seed, data version and software environment are recorded with it.
A reproducible training loop
Use the order clear gradients, forward pass, loss, backward pass and parameter update. Compare training and validation metrics, then load the saved state in a clean session and run a fixed sample to confirm the output.
Maintenance note
The reference does not replace model selection, experiment design or data governance. Hardware, randomness and software versions influence results; protect datasets, model files and logs according to their sensitivity. Content review date: 2026-08-23.
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R torch deep-learning study guide
Validate tensor shapes and one forward pass, complete a small training loop, then record the model state and environment before scaling to a larger device.
Before you start
- Know R functions, matrix operations and basic machine-learning concepts.
- Prepare a small sanitized and repeatable training sample.
- Record expected input shape, label type and output before training.
Quick start
- 01
Check tensor shapes
Confirm input, label and batch dimensions, data types, device placement and missing values before building the model.
- 02
Run a forward pass
Use a simple module to inspect output shape, numeric range and activation values before attaching the loss function.
- 03
Complete the training loop
Run gradient clearing, forward pass, loss, backward pass and parameter update in order while recording training and validation metrics.
- 04
Save and reproduce
Save state, configuration, seed and preprocessing, load them in a clean session and compare output on a fixed sample.
Usage tips
- Apply identical preprocessing to training and validation data without using validation statistics during training.
- Confirm logic on CPU and a small model before evaluating GPU, batch and memory settings.
- Record model, data, dependency and hardware versions as one experiment bundle.
Troubleshooting and uninstall
Why are gradients zero or NaN?
Check input range, learning rate, loss, activation and tensor shapes, then reproduce with a smaller sample and lower learning rate.
Why do CPU and GPU results differ?
Confirm model, inputs and labels share the intended device, fix the random seed and inspect precision and batch ordering.
Frequently asked questions
Where should an R torch workflow begin?
Validate tensor shape, device and a simple forward pass before adding loss, gradients, an optimizer and a full training loop.
Why train on a small sample first?
A small sample exposes shape, type, gradient and loss errors quickly, before time is spent on a long training run.
What belongs with a saved model?
Keep model state, structure configuration, preprocessing, seed, software environment and training-data version together.