formatting

Author: wangjiawen2013

examples/modern-lstm/src/cli.rs

@@ -39,4 +39,4 @@ pub enum Commands {
         #[arg(long)]
         artifact_dir: String,
     },
-}
+}

examples/modern-lstm/src/dataset.rs

@@ -8,7 +8,7 @@ use burn::{
 use rand::Rng;
 use rand_distr::{Distribution, Normal};
 use serde::{Deserialize, Serialize};
-    
+
 // Dataset parameters
 pub const NUM_SEQUENCES: usize = 1000;
 pub const SEQ_LENGTH: usize = 10;
@@ -26,7 +26,7 @@ impl SequenceDatasetItem {
     pub fn new(seq_length: usize, noise_level: f32) -> Self {
         // Start with two random numbers between 0 and 1
         let mut seq = vec![rand::thread_rng().gen(), rand::thread_rng().gen()];
-        
+
         // Generate sequence
         for _i in 0..seq_length {
             // Next number is sum of previous two plus noise

examples/modern-lstm/src/inference.rs

@@ -42,4 +42,4 @@ pub fn infer<B: Backend>(artifact_dir: &str, device: B::Device) {
     ]
     .unwrap();
     println!("{}", &results.head(Some(10)));
-}
+}

examples/modern-lstm/src/model.rs

@@ -142,7 +142,7 @@ impl<B: Backend> LstmCell<B> {
     pub fn init_state(&self, batch_size: usize, device: &B::Device) -> LstmState<B, 2> {
         let cell = Tensor::zeros([batch_size, self.hidden_size], device);
         let hidden = Tensor::zeros([batch_size, self.hidden_size], device);
-        
+
         LstmState::new(cell, hidden)
     }
 }

examples/modern-lstm/src/training.rs

@@ -7,9 +7,8 @@ use burn::{
     record::CompactRecorder,
     tensor::backend::AutodiffBackend,
 };
-
 use crate::dataset::{
-    SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH, 
+    SequenceBatcher, SequenceDataset, NOISE_LEVEL, NUM_SEQUENCES, RANDOM_SEED, SEQ_LENGTH,
 };
 use crate::model::{LstmNetwork, LstmNetworkConfig};