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- [Using the hub](inference/hub.md)
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- [Error management](error_manage.md)
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- [Training](training/README.md)
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- [Simplified](training/simplified.md)
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- [MNIST](training/mnist.md)
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- [Fine-tuning]()
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- [Serialization]()
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@ -1,3 +1,4 @@
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mod simplified;
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#[cfg(test)]
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mod tests {
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use anyhow::Result;
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//! #A simplified example in Rust of training a neural network and then using it based on the Candle Framework by Hugging Face.
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//! Author: Evgeny Igumnov 2023 igumnovnsk@gmail.com
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//! This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
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//!
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//! ##Basic moments:
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//!
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//! A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
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//! The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
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//! The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
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//! For training, samples with real data on the results of the first and second stages of different elections are used.
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//! The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
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//! Model parameters (weights of neurons) are initialized randomly, then optimized during training.
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//! After training, the model is tested on a deferred sample to evaluate the accuracy.
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//! If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
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//! Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.
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#[rustfmt::skip]
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mod tests {
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use candle::{DType, Result, Tensor, D, Device};
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use candle_nn::{loss, ops, Linear, Module, VarBuilder, VarMap, Optimizer};
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// ANCHOR: book_training_simplified1
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const VOTE_DIM: usize = 2;
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const RESULTS: usize = 1;
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const EPOCHS: usize = 10;
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const LAYER1_OUT_SIZE: usize = 4;
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const LAYER2_OUT_SIZE: usize = 2;
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const LEARNING_RATE: f64 = 0.05;
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#[derive(Clone)]
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pub struct Dataset {
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pub train_votes: Tensor,
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pub train_results: Tensor,
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pub test_votes: Tensor,
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pub test_results: Tensor,
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}
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struct MultiLevelPerceptron {
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ln1: Linear,
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ln2: Linear,
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ln3: Linear,
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}
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impl MultiLevelPerceptron {
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fn new(vs: VarBuilder) -> Result<Self> {
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let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?;
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let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?;
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let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?;
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Ok(Self { ln1, ln2, ln3 })
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}
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let xs = self.ln1.forward(xs)?;
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let xs = xs.relu()?;
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let xs = self.ln2.forward(&xs)?;
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let xs = xs.relu()?;
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self.ln3.forward(&xs)
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}
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}
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// ANCHOR_END: book_training_simplified1
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#[tokio::test]
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// ANCHOR: book_training_simplified3
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async fn simplified() -> anyhow::Result<()> {
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let dev = Device::cuda_if_available(0)?;
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let train_votes_vec: Vec<u32> = vec![
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15, 10,
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10, 15,
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5, 12,
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30, 20,
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16, 12,
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13, 25,
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6, 14,
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31, 21,
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];
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let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
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let train_results_vec: Vec<u32> = vec![
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1,
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0,
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0,
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1,
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1,
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0,
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0,
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1,
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];
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let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?;
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let test_votes_vec: Vec<u32> = vec![
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13, 9,
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8, 14,
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3, 10,
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];
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let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
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let test_results_vec: Vec<u32> = vec![
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1,
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0,
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0,
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];
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let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?;
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let m = Dataset {
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train_votes: train_votes_tensor,
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train_results: train_results_tensor,
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test_votes: test_votes_tensor,
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test_results: test_results_tensor,
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};
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let trained_model: MultiLevelPerceptron;
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loop {
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println!("Trying to train neural network.");
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match train(m.clone(), &dev) {
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Ok(model) => {
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trained_model = model;
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break;
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},
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Err(e) => {
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println!("Error: {}", e);
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continue;
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}
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}
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}
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let real_world_votes: Vec<u32> = vec![
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13, 22,
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];
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let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
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let final_result = trained_model.forward(&tensor_test_votes)?;
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let result = final_result
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.argmax(D::Minus1)?
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.to_dtype(DType::F32)?
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.get(0).map(|x| x.to_scalar::<f32>())??;
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println!("real_life_votes: {:?}", real_world_votes);
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println!("neural_network_prediction_result: {:?}", result);
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Ok(())
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}
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// ANCHOR_3: book_training_simplified3
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// ANCHOR: book_training_simplified2
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fn train(m: Dataset, dev: &Device) -> anyhow::Result<MultiLevelPerceptron> {
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let train_results = m.train_results.to_device(dev)?;
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let train_votes = m.train_votes.to_device(dev)?;
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let varmap = VarMap::new();
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let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev);
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let model = MultiLevelPerceptron::new(vs.clone())?;
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let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?;
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let test_votes = m.test_votes.to_device(dev)?;
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let test_results = m.test_results.to_device(dev)?;
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let mut final_accuracy: f32 = 0.0;
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for epoch in 1..EPOCHS + 1 {
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let logits = model.forward(&train_votes)?;
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let log_sm = ops::log_softmax(&logits, D::Minus1)?;
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let loss = loss::nll(&log_sm, &train_results)?;
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sgd.backward_step(&loss)?;
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let test_logits = model.forward(&test_votes)?;
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let sum_ok = test_logits
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.argmax(D::Minus1)?
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.eq(&test_results)?
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.to_dtype(DType::F32)?
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.sum_all()?
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.to_scalar::<f32>()?;
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let test_accuracy = sum_ok / test_results.dims1()? as f32;
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final_accuracy = 100. * test_accuracy;
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println!("Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%",
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loss.to_scalar::<f32>()?,
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final_accuracy
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);
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if final_accuracy == 100.0 {
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break;
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}
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}
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if final_accuracy < 100.0 {
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Err(anyhow::Error::msg("The model is not trained well enough."))
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} else {
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Ok(model)
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}
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}
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// ANCHOR_END: book_training_simplified2
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}
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@ -0,0 +1,47 @@
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# Simplified
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## How its works
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This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
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Basic moments:
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1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
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2. The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
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3. The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
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4. For training, samples with real data on the results of the first and second stages of different elections are used.
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5. The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
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6. Model parameters (weights of neurons) are initialized randomly, then optimized during training.
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7. After training, the model is tested on a deferred sample to evaluate the accuracy.
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8. If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
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Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.
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```rust,ignore
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{{#include ../simplified.rs:book_training_simplified1}}
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```
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```rust,ignore
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{{#include ../simplified.rs:book_training_simplified2}}
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```
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```rust,ignore
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{{#include ../simplified.rs:book_training_simplified3}}
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```
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## Example output
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```
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Trying to train neural network.
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Epoch: 1 Train loss: 4.42555 Test accuracy: 0.00%
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Epoch: 2 Train loss: 0.84677 Test accuracy: 33.33%
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Epoch: 3 Train loss: 2.54335 Test accuracy: 33.33%
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Epoch: 4 Train loss: 0.37806 Test accuracy: 33.33%
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Epoch: 5 Train loss: 0.36647 Test accuracy: 100.00%
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real_life_votes: [13, 22]
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neural_network_prediction_result: 0.0
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```
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