easy_df_adam.cpp

#include "micrograd.hpp"
#include "value.hpp"
#include "mlp.hpp"
using namespace microgradCpp;
int main()
{
    // DatasetType dataset = get_iris();
    DataFrame df;
    df.from_csv("./data/iris.csv");
    df.normalize();
    df.encode_column("variety");
    df.print();
    df.shuffle();
    df.print();
    //  return 0;
    // stop();
    // return 0;
    // shuffle(dataset);
    double TRAIN_SIZE{0.8};
    // Create MLP model
    // Input: 4 features, hidden layers: [7,7], output: 3 classes
    // Define the model and hyperparameters
    // MLP model(4, {10, 10, 3});
    MLP model(4, {7, 7, 3});
    auto params = model.parameters();
    double learning_rate = 0.01;
    int epochs; //  = 100;
    std::cout << "Epoch : ?";
    std::cin >> epochs;
    // Initialize Adam optimizer
    AdamOptimizer optimizer(params, learning_rate);
    // Train and evaluate the model
    // train_eval(dataset, TRAIN_SIZE, model, learning_rate, epochs);
    // train_eval(dataset, TRAIN_SIZE, model, optimizer, epochs);
    // Train and evaluate the model
    train_eval(df, TRAIN_SIZE, model, optimizer, epochs);
    return 0;
}
/*
    */
/*
Notes
-----------
g++ -std=c++17 -Iinclude -O2 -o main easy_df_adam.cpp
// or
make run
*/