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88 lines
2.8 KiB
88 lines
2.8 KiB
#![feature(generic_arg_infer)]
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#![feature(generic_const_exprs)]
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use neuramethyst::algebra::NeuraVector;
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use neuramethyst::derivable::reduce::{Average, Max};
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use rust_mnist::Mnist;
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use neuramethyst::derivable::activation::{Linear, Relu};
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use neuramethyst::derivable::loss::CrossEntropy;
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use neuramethyst::{cycle_shuffling, one_hot, prelude::*};
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fn main() {
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const TRAIN_SIZE: usize = 1000;
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let Mnist {
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train_data: train_images,
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train_labels,
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test_data: test_images,
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test_labels,
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..
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} = Mnist::new("data/");
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let train_images = train_images
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.into_iter()
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.map(|raw| {
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raw.into_iter()
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.map(|x| x as f64 / 255.0)
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.collect::<NeuraVector<{ 28 * 28 }, f64>>()
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})
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.take(TRAIN_SIZE);
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let train_labels = train_labels
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.into_iter()
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.map(|x| one_hot::<10>(x as usize))
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.take(TRAIN_SIZE);
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let test_images = test_images
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.into_iter()
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.map(|raw| {
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raw.into_iter()
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.map(|x| x as f64 / 255.0)
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.collect::<NeuraVector<{ 28 * 28 }, f64>>()
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})
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.take(TRAIN_SIZE / 6);
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let test_labels = test_labels
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.into_iter()
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.map(|x| one_hot::<10>(x as usize))
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.take(TRAIN_SIZE / 6);
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let train_iter = cycle_shuffling(
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train_images.zip(train_labels.into_iter()),
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rand::thread_rng(),
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);
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let test_inputs: Vec<_> = test_images.zip(test_labels.into_iter()).collect();
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let mut network = neura_sequential![
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neura_layer!("unstable_reshape", 1, { 28 * 28 }),
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neura_layer!("conv2d_pad", 1, {28 * 28}; 28, 3; neura_layer!("dense", {1 * 3 * 3}, 3; Relu)),
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// neura_layer!("conv2d_block", 7, 7; 4; neura_layer!("dense", {3 * 4 * 4}, 8; Relu)),
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// neura_layer!("conv2d_pad"; 28, 1; neura_layer!("dense", {30 * 1 * 1}, 10; Relu)),
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neura_layer!("unstable_flatten"),
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neura_layer!("dropout", 0.33),
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neura_layer!("unstable_reshape", 3, { 28 * 28 }),
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neura_layer!("conv2d_block", 14, 14; 2; neura_layer!("dense", {3 * 2 * 2}, 2; Relu)),
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// neura_layer!("unstable_flatten"),
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// neura_layer!("dropout", 0.33),
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// neura_layer!("unstable_reshape", 2, { 14 * 14 }),
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// neura_layer!("conv2d_pad"; 14, 5; neura_layer!("dense", {2 * 5 * 5}, 20; Relu)),
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// neura_layer!("pool_global"; Max),
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// neura_layer!("pool1d", {14 * 2}, 7; Max),
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neura_layer!("unstable_flatten"),
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neura_layer!("dropout", 0.2),
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neura_layer!("dense", 10; Linear),
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neura_layer!("softmax")
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];
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let mut trainer = NeuraBatchedTrainer::with_epochs(0.03, 100, 128, TRAIN_SIZE);
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trainer.learning_momentum = 0.001;
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trainer.train(
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NeuraBackprop::new(CrossEntropy),
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&mut network,
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train_iter,
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&test_inputs,
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);
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}
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