nn.RNN layer in PyTorch - Model Pipeline Trace

Model Pipeline - nn.RNN layer

This pipeline shows how a simple Recurrent Neural Network (RNN) layer processes sequential data to learn patterns over time steps. It starts with input sequences, transforms them through the RNN layer, trains the model to reduce error, and finally makes predictions on new sequences.

Data Flow - 3 Stages

1Input Data

1000 sequences x 10 time steps x 5 features→Raw sequential data representing 1000 samples, each with 10 time steps and 5 features per step→1000 sequences x 10 time steps x 5 features

[[0.1, 0.2, 0.3, 0.4, 0.5], ..., [0.5, 0.4, 0.3, 0.2, 0.1]] for 10 time steps

↓

2RNN Layer

1000 sequences x 10 time steps x 5 features→Processes input sequences through nn.RNN with hidden size 8, producing hidden states for each time step→1000 sequences x 10 time steps x 8 features

Hidden states like [[0.01, 0.02, ..., 0.08], ..., [0.05, 0.04, ..., 0.01]] for 10 time steps

↓

3Output Layer

1000 sequences x 10 time steps x 8 features→Takes last hidden state and applies a linear layer to predict output class→1000 sequences x 3 classes

[0.2, 0.5, 0.3] representing class probabilities

Training Trace - Epoch by Epoch

Loss
1.2 |*****
0.9 |****
0.7 |***
0.5 |**
0.4 |*

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Initial training with high loss and low accuracy
2	0.9	0.60	Loss decreased, accuracy improved
3	0.7	0.72	Model learning meaningful patterns
4	0.5	0.80	Good convergence, loss continues to drop
5	0.4	0.85	Training stabilizes with high accuracy

Prediction Trace - 5 Layers

Layer 1: Input Sequence

Layer 2: RNN Layer

Layer 3: Last Hidden State Extraction

Layer 4: Linear Layer

Layer 5: Softmax Activation

Model Quiz - 3 Questions

What shape does the RNN layer output if input is (1000, 10, 5) and hidden size is 8?

A (1000, 8, 10)

B (1000, 10, 8)

C (1000, 5, 8)

D (10, 1000, 8)

Key Insight

The nn.RNN layer processes sequential data step-by-step, updating a hidden state that captures information from all previous time steps. Using the last hidden state allows the model to summarize the entire sequence for tasks like classification. Training shows loss decreasing and accuracy increasing, indicating the model learns meaningful sequence patterns.