From 3e1e96c29c09f052315ec7d2cfc21f4f0958cd4f Mon Sep 17 00:00:00 2001
From: bokutotu <mushin.hudoushin@gmail.com>
Date: Sat, 16 Nov 2024 04:46:16 +0900
Subject: [PATCH] update zenu readme

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+
+# ZeNu
+
+**ZeNu** is a high-performance deep learning framework implemented in pure Rust. It features an intuitive API and high extensibility.
+
+## Features
+
+- 🦀 **Pure Rust implementation**: Maximizes safety and performance
+- ⚡ **GPU performance**: Comparable to PyTorch (supports CUDA 12.3 + cuDNN 9)
+- 🔧 **Simple and intuitive API**
+- 📦 **Modular design**: Easy to extend
+
+## Installation
+
+Add the following to your Cargo.toml:
+
+```toml
+[dependencies]
+zenu = "0.1"
+
+# To enable CUDA support:
+[dependencies.zenu]
+version = "0.1"
+features = ["nvidia"]
+```
+
+## Supported Features
+
+### Layers
+- Linear
+- Convolution 2D
+- Batch Normalization 2D
+- LSTM
+- RNN
+- GRU
+- MaxPool 2D
+- Dropout
+
+### Optimizers
+- SGD
+- Adam
+- AdamW
+
+### Device Support
+- CPU
+- CUDA (NVIDIA GPU)
+  - CUDA 12.3
+  - cuDNN 9
+
+## Project Structure
+
+```
+zenu/
+├── zenu               # Main library
+├── zenu-autograd      # Automatic differentiation engine
+├── zenu-layer         # Neural network layers
+├── zenu-matrix        # Matrix operations
+├── zenu-optimizer     # Optimization algorithms
+├── zenu-cuda          # CUDA implementation
+└── Other support crates
+```
+
+## Examples
+
+Check the `examples/` directory for detailed implementations:
+- MNIST classification
+- CIFAR10 classification
+- ResNet implementation
+
+### Simple Usage Example
+
+Here is a simple example of defining and training a model using ZeNu:
+
+```rust
+use zenu::{
+    dataset::{train_val_split, DataLoader, Dataset},
+    mnist::minist_dataset,
+    update_parameters, Model,
+};
+use zenu_autograd::{
+    creator::from_vec::from_vec,
+    functions::{activation::sigmoid::sigmoid, loss::cross_entropy::cross_entropy},
+    Variable,
+};
+use zenu_layer::{layers::linear::Linear, Layer};
+use zenu_optimizer::sgd::SGD;
+
+struct SingleLayerModel {
+    linear: Linear<f32>,
+}
+
+impl SingleLayerModel {
+    fn new() -> Self {
+        let mut linear = Linear::new(784, 10);
+        linear.init_parameters(None);
+        Self { linear }
+    }
+}
+
+impl Model<f32> for SingleLayerModel {
+    fn predict(&self, inputs: &[Variable<f32>]) -> Variable<f32> {
+        let x = &inputs[0];
+        let x = self.linear.call(x.clone());
+        sigmoid(x)
+    }
+}
+
+fn main() {
+    let (train, test) = minist_dataset().unwrap();
+    let (train, val) = train_val_split(&train, 0.8, true);
+
+    let test_dataloader = DataLoader::new(MnistDataset { data: test }, 1);
+
+    let sgd = SGD::new(0.01);
+    let model = SingleLayerModel::new();
+
+    for epoch in 0..10 {
+        let mut train_dataloader = DataLoader::new(
+            MnistDataset {
+                data: train.clone(),
+            },
+            16,
+        );
+        let val_dataloader = DataLoader::new(MnistDataset { data: val.clone() }, 16);
+
+        train_dataloader.shuffle();
+
+        let mut epoch_loss_train: f32 = 0.;
+        let mut num_iter_train = 0;
+        for batch in train_dataloader {
+            let input = batch[0].clone();
+            let target = batch[1].clone();
+            let y_pred = model.predict(&[input]);
+            let loss = cross_entropy(y_pred, target);
+            update_parameters(loss.clone(), &sgd);
+            epoch_loss_train += loss.get_data().index_item([]);
+            num_iter_train += 1;
+        }
+
+        let mut epoch_loss_val = 0.;
+        let mut num_iter_val = 0;
+        for batch in val_dataloader {
+            let input = batch[0].clone();
+            let target = batch[1].clone();
+            let y_pred = model.predict(&[input]);
+            let loss = cross_entropy(y_pred, target);
+            epoch_loss_val += loss.get_data().index_item([]);
+            num_iter_val += 1;
+        }
+
+        println!(
+            "Epoch: {}, Train Loss: {}, Val Loss: {}",
+            epoch,
+            epoch_loss_train / num_iter_train as f32,
+            epoch_loss_val / num_iter_val as f32
+        );
+    }
+
+    let mut test_loss = 0.;
+    let mut num_iter_test = 0;
+    let mut correct = 0;
+    let mut total = 0;
+    for batch in test_dataloader {
+        let input = batch[0].clone();
+        let target = batch[1].clone();
+        let y_pred = model.predict(&[input]);
+        let loss = cross_entropy(y_pred.clone(), target.clone());
+        test_loss += loss.get_data().index_item([]);
+        num_iter_test += 1;
+        let y_pred = y_pred.get_data();
+        let max_idx = y_pred.to_view().max_idx()[0];
+        let target = target.get_data();
+        let target = target.to_view().max_idx()[0];
+        if max_idx == target {
+            correct += 1;
+        }
+        total += 1;
+    }
+
+    println!("Accuracy: {}", correct as f32 / total as f32);
+    println!("Test Loss: {}", test_loss / num_iter_test as f32);
+}
+```
+
+## Contributing
+
+Contributions to ZeNu are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the [GitHub repository](https://github.com/bokutotu/zenu).
+
+## License
+
+ZeNu is licensed under the [MIT License](LICENSE).