nn.LSTM layer in PyTorch - Model Pipeline Trace

Model Pipeline - nn.LSTM layer

This pipeline shows how a simple LSTM layer processes sequence data to learn patterns over time. The LSTM layer helps the model remember important information from earlier steps to make better predictions.

Data Flow - 3 Stages

1Input Data

100 sequences x 10 time steps x 5 features→Raw sequential data representing 100 samples, each with 10 time steps and 5 features per step→100 sequences x 10 time steps x 5 features

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

↓

2LSTM Layer

100 sequences x 10 time steps x 5 features→Processes sequences to capture time dependencies, outputs hidden states for each time step→100 sequences x 10 time steps x 8 hidden units

[[0.05, 0.1, ..., 0.2], ..., repeated for 10 time steps]

↓

3Fully Connected Layer

100 sequences x 8 hidden units→Takes last hidden state from LSTM and maps to output classes→100 sequences x 3 classes

[[0.3, 0.5, 0.2], ..., for each sequence]

Training Trace - Epoch by Epoch

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

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.40	Loss starts high, accuracy low as model begins learning
2	0.9	0.55	Loss decreases, accuracy improves as model learns sequence patterns
3	0.7	0.65	Continued improvement, model captures temporal dependencies better
4	0.5	0.75	Loss lowers further, accuracy rises showing good learning progress
5	0.4	0.80	Model converges with stable loss and high accuracy

Prediction Trace - 5 Layers

Layer 1: Input Sequence

Layer 2: LSTM Layer

Layer 3: Select Last Hidden State

Layer 4: Fully Connected Layer

Layer 5: Softmax Activation

Model Quiz - 3 Questions

What does the LSTM layer output for each input sequence?

A Only the first time step features

B Hidden states for each time step

C Random noise

D Final prediction classes

Key Insight

The nn.LSTM layer helps the model remember important information across time steps in sequences. By capturing temporal patterns, it improves predictions on sequence data. Training shows loss decreasing and accuracy increasing, indicating the model learns to understand time dependencies.