# Technical Document Extraction: Neural Network Architecture Diagram
## Diagram Overview
The image depicts a convolutional neural network (CNN) architecture with residual blocks and dense layers. The diagram uses standardized layer icons and directional arrows to represent data flow.
## Component Breakdown
### Input Layer
- **Label**: `Input: (d, n)`
- **Description**: Represents input dimensions (depth `d`, height/width `n`)
### Feature Extraction Path
1. **Conv2D Layer**
- Icon: 
- Description: Initial convolutional operation
2. **Batch Normalisation**
- Icon: 
- Description: Normalizes layer outputs
3. **ReLU Activation**
- Icon: 
- Description: Rectified Linear Unit activation function
4. **Max Pooling**
- Icon: 
- Description: Spatial downsampling operation
### Residual Blocks (21x)
- **Label**: `21 x Residual Blocks`
- **Structure**:
- **Loop Body**:
1. Conv2D → Batch Normalisation → ReLU
2. Conv2D → Batch Normalisation → ReLU
- **Skip Connection**: Purple arrow bypassing first ReLU
- **Output**: `x1` (residual output)
### Global Pooling & Classification
1. **Global Average Pooling**
- Icon: 
- Description: Reduces spatial dimensions to 1x1
2. **Dense Layer Stack**
- `Dense(50)` → `Dense(40)` → `Dense(30)` → `Dense(20)` → `Dense(10)`
- Description: Fully connected layers with decreasing units
### Output Layer
- **Label**: `Output: (m, 1)`
- **Description**: Final output dimensions (batch size `m`, single feature)
## Data Flow Summary
