13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch • Deep Learning & PyTorch Neural Networks Architecture • Rishabh Ki Kaksha

Deep Learning & PyTorch Neural Networks Architecture › 13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch

45 mins

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Course Syllabus

Module 1: PyTorch Tensors, Autograd & Computational Graphs

01. PyTorch Tensor Architecture: Strides, Storage Buffers, Memory Layout (Contiguous vs Non-Contiguous) 02. Dynamic Computational Graphs & The PyTorch Autograd Engine (grad_fn, requires_grad, backward) 03. GPU Acceleration with CUDA: Tensor Device Placement (to("cuda")), CUDA Streams & Pinned Memory

Module 2: Multi-Layer Perceptrons (MLPs) & Custom Training Loops

04. Building Custom nn.Module Classes, Custom Parameters & Activation Functions (ReLU, GELU, Swish) 05. Custom Datasets & High-Performance DataLoaders (num_workers, collate_fn, pin_memory) 06. The Enterprise Training Loop: Forward Pass, Loss Computation, Zero Grad, Backward Pass & Optimizer Step 07. Neural Regularization Techniques: Dropout, Batch Normalization (BatchNorm1d) & Layer Normalization (LayerNorm)

Module 3: Convolutional Neural Networks (CNNs) & Computer Vision Architecture

08. Convolutional Operations: Kernels, Filters, Strides, Padding & Feature Map Dilation 09. Modern CNN Architectures: Residual Connections (ResNet), Squeeze-and-Excitation & ConvNeXt 10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models

Module 4: Recurrent Architectures: RNNs, LSTMs & GRUs for Sequential Data

11. Sequential Modeling: Vanilla RNNs & The Vanishing / Exploding Gradient Problem 12. Long Short-Term Memory (LSTM) Networks: Forget Gate, Input Gate, Candidate State & Output Gate Math 13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch

Module 5: Modern Transformer Architecture from Scratch in PyTorch

14. Scaled Dot-Product Attention: Query, Key, Value Matrices & Attention Softmax Calculation 15. Multi-Head Attention (MHA) & Causal Masking Implementation from Scratch in Pure PyTorch 16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch

Module 6: PyTorch Distributed (DDP), Mixed Precision (AMP) & Model Checkpointing

17. Automatic Mixed Precision (torch.cuda.amp.autocast & GradScaler) for 2x Faster GPU Training 18. Distributed Data Parallel (DDP) Architecture: Multi-GPU Synchronization with torch.distributed 19. Robust Checkpointing: Saving State Dicts, Resuming Training & Early Stopping Callbacks 20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation

Interactive Lesson Studio

# 13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch

> Deep Learning & PyTorch Notes by Rishabh - Topic 13

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### 1. GRU: The Streamlined LSTM (Cho et al. ⚡)

LSTM ke 4 gates ko combine karke sirf **2 gates** me simplify kiya gaya:
1. **Reset Gate ($r_t$):** $r_t = \sigma(W_r [h_{t-1}, x_t])$.
2. **Update Gate ($z_t$):** $z_t = \sigma(W_z [h_{t-1}, x_t])$.
3. **Hidden State Interpolation:**
   $$\tilde{h}_t = \tanh(W [r_t \odot h_{t-1}, x_t])$$
   $$h_t = (1 - z_t) \odot h_{t-1} + z_t \odot \tilde{h}_t$$

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### 2. Bidirectional GRU (Bi-GRU) in PyTorch

```python
import torch
import torch.nn as nn

# Bidirectional GRU: Processes sequence Forward AND Backward!
gru = nn.GRU(input_size=128, hidden_size=256, num_layers=2, batch_first=True, bidirectional=True)
x = torch.randn(32, 50, 128) # [Batch, SeqLen, Dim]
out, hidden = gru(x)
print(out.shape) # [32, 50, 512] (256 forward + 256 backward concatenated!)
```

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### 3. Master Comparison Matrix

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### 4. Interview Ke 3 Sawal Is Topic Se

1. **Bidirectional models (Bi-LSTM / Bi-GRU) Next-Token Generation me kyu fail hote hain?** -> Kyunki Autoregressive LLMs me future tokens decode time par exist hi nahi karte; Bi-directional models sirf Masked/Classification tasks (BERT, Sentiment analysis) me kaam aate hain.
2. **`pack_padded_sequence` PyTorch me variable length batches ke liye kyu use hota hai?** -> Padding tokens `<PAD>` par unnecessary computation compute hone se rokkta hai.
3. **Hidden state tensor shape Bi-GRU me `[num_layers * 2, batch, hidden_dim]` kyu hota hai?** -> Har layer ke liye 2 direction states (Forward + Backward) stack hoti hain.

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**Next Topic:** 14. Scaled Dot-Product Attention: Query, Key, Value Matrices & Attention Softmax Calculation

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