Cheatsheet

docs/tutorials/Cheatsheet.md

@@ -1,3 +1,73 @@
 # Cheatsheet
 
 This page contains all the info you need to develop your models using Shkyera Grad.
+
+## Types
+
+Almost all of the classes in _Shkyera Grad_ are implemented using templates. To simplify creation of these objects, we introduced a standard way to instantiate objects with floating-point template parameters, i.e.
+
+```cpp
+Linear32 = Linear<float>
+Optimizer32 = Optimizer<Type::float32>>
+Loss::MSE64 = Loss::MSE<double>
+Adam64 = Adam<Type::f64>
+
+{Class}32 = {Class}<Type::float32> = {Class}<float>
+{Class}64 = {Class}<Type::float64> = {Class}<double>
+```
+
+## Layers
+
+Here's a full list of available layers:
+
+```cpp
+auto linear = Linear32::create(inputSize, outputSize);
+auto dropout = Dropout32::create(inputSize, outputSize, dropoutRate);
+```
+
+## Optimizers
+
+These are all implemented optimizers:
+
+```cpp
+auto simple = Optimizer32(network->parameters(), learningRate);
+auto sgdWithMomentum = SGD32(network->parameters(), learningRate, momentum = 0.9);
+auto adam = Adam32(network->parameters(), learningRate, beta1 = 0.9, beta2=0.999, epsilon=1e-8);
+```
+
+## Loss functions
+
+Optimization can be performed according to these predefined loss functions:
+
+```cpp
+auto L1 = Loss::MAE32;
+auto L2 = Loss::MSE32;
+auto crossEntropy = Loss::CrossEntropy32;
+```
+
+## Generic Training Loop
+
+Simply copy-pase this code to quickly train your network:
+
+```cpp
+using T = Type::float32; // feel free to change it to float64
+
+auto optimizer = Adam<T>(network->parameters(), 0.05);
+auto lossFunction = Loss::MSE<T>;
+
+for (size_t epoch = 0; epoch < 100; epoch++) {
+    auto epochLoss = Value<T>::create(0);
+
+    optimizer.reset();
+    for (size_t sample = 0; sample < xs.size(); ++sample) {
+        Vector<T> pred = network->forward(xs[sample]);
+        auto loss = lossFunction(pred, ys[sample]);
+
+        epochLoss = epochLoss + loss;
+    }
+    optimizer.step();
+
+    auto averageLoss = epochLoss / Value<T>::create(xs.size());
+    std::cout << "Epoch: " << epoch + 1 << " Loss: " << averageLoss->getValue() << std::endl;
+}
+```