# Technical Document Extraction: Feed Forward Network (FFN) Architecture
## Diagram Description
The image depicts a **Feed Forward Network (FFN)** architecture with a **residual connection**. The diagram is enclosed within a dashed-line boundary labeled "FFN". Below is a detailed breakdown of components, flow, and connections:
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### **Components**
1. **Input**:
- Labeled as `X` (input tensor).
- Positioned at the leftmost edge of the diagram.
2. **Convolutional Layers**:
- **Conv1**: First convolutional layer.
- Receives input `X`.
- **Conv2**: Second convolutional layer.
- Receives output from ReLU.
- Outputs `X'` (final output tensor).
3. **Activation Function**:
- **ReLU**: Rectified Linear Unit applied after Conv1.
- Ensures non-linearity in the network.
4. **Residual Connection**:
- Labeled "Residual add".
- Connects input `X` directly to the output `X'` (bypassing Conv1, ReLU, and Conv2).
- Implements skip connection for gradient stabilization.
5. **Output**:
- Labeled `X'`.
- Result of the residual addition: `X' = Conv2(ReLU(Conv1(X))) + X`.
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### **Flow of Data**
1. **Forward Pass**:
- `X` → Conv1 → ReLU → Conv2 → `X'`.
- Residual connection: `X` is added to `X'` (output of Conv2).
2. **Key Operations**:
- **Conv1**: Extracts features from input `X`.
- **ReLU**: Introduces non-linearity.
- **Conv2**: Further processes features.
- **Residual Add**: Combines original input `X` with processed output to mitigate vanishing gradients.
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### **Architectural Notes**
- The FFN block is explicitly bounded by a dashed-line box.
- No explicit hyperparameters (e.g., kernel size, stride) are labeled in the diagram.
- The residual connection is critical for training deep networks by preserving gradient flow.
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### **Textual Elements**
- All labels are in English.
- No non-English text or data tables are present.
- No legends, axis titles, or numerical data points are included.
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### **Conclusion**
This diagram illustrates a simplified FFN with residual learning, emphasizing modularity and gradient preservation. The absence of numerical data or categorical labels suggests it is a conceptual representation rather than an empirical analysis.
