195 行
9.2 KiB
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195 行
9.2 KiB
Markdown
# Article-24337-Working-With-ONNX-Models-In-MQL5-Part-3
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This repository is an article-derived reference project based on the original MQL5 article. It does not claim to reproduce the full original source code unless files are explicitly attached.
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## Overview
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This repository is derived from the MQL5 article *Working with ONNX Models in MQL5 (Part 3): Drawing the Neurons and the Weights Between Them)*. The article extends an ONNX model viewer for MetaTrader 5 by moving from layer-level graph visualization to neuron-level rendering of dense layers and their weight matrices.
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The main focus is on interpreting stored ONNX weight tensors as connection maps between neuron rows, handling matrix orientation correctly, drawing weighted links with sign- and magnitude-based styling, and allowing the user to switch between graph view and neuron view. As an article-derived project, this repository serves as a reference for studying the described architecture and, where attached files are available, reusing the provided implementation.
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## Original Article
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- **Article ID:** 24337
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- **Title:** Working with ONNX Models in MQL5 (Part 3): Drawing the Neurons and the Weights Between Them
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- **Author:** Allan Munene Mutiiria
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- **Publication date:** Not available
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- **Categories:** Expert Advisors, Statistics
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- **Article URL:** https://www.mql5.com/en/articles/24337
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- **Author URL:** https://www.mql5.com/en/users/29210372
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## Repository Purpose
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This repository exists to preserve and organize the technical ideas presented in the article as a reusable MQL5 reference project.
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A reader can use it to learn how to:
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- inspect ONNX dense-layer weights inside MetaTrader 5,
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- map weight matrices to neuron-to-neuron connections,
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- distinguish graph structure from neuron-level structure,
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- handle transposed ONNX weight storage using operator attributes,
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- build chart-based visualizations with pan, zoom, hover, and view switching,
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- design honest fallback behavior when a model cannot be represented as dense neuron pairs.
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## Key Concepts
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- ONNX model visualization
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- Expert Advisors
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- Statistics
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- Dense neural layers
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- Weight matrices
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- Neuron layout construction
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- Graph view vs neuron view
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- MQL5 chart rendering
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- ONNX attribute handling
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- MetaTrader 5 UI interaction
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## Algorithm / Architecture Summary
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1. **Start from the previously parsed ONNX model**
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- The article assumes a model parser already exists from earlier parts.
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- Nodes, tensors, attributes, emitted values, and layer relationships are read from the ONNX model.
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2. **Represent neuron rows separately from graph nodes**
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- A row structure is used to store:
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- neuron count,
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- label,
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- position,
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- width,
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- rank/depth in the arrangement.
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- A row does not necessarily equal one layer, because some operations modify values in place without changing row width.
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3. **Represent connection runs between rows**
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- A second structure records the connection relationship between two rows.
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- It stores source row, target row, the tensor index holding weights, target offset, and whether the stored matrix must be treated as transposed.
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4. **Classify supported operators**
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- Dense operators are explicitly treated as those that carry a 2D weight matrix between neuron rows.
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- Unary operators are treated as operations that transform values on an existing row without creating a new row.
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- Unsupported or ambiguous operators are declined instead of guessed.
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5. **Build neuron rows from dense layers**
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- When a dense operator is encountered, the code:
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- reads the weight tensor,
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- checks whether the `transB` attribute indicates transposed storage,
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- determines the produced neuron count from the appropriate tensor dimension,
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- opens a new neuron row,
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- records a connection run from the feeding row to the new row.
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6. **Fold activations and similar operators onto existing rows**
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- Unary, binary, and shape-only operations do not create new neuron rows.
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- Their emitted values are mapped back to the same row.
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- Labels are extended so the row reflects the applied operations, such as a dense operator followed by an activation.
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7. **Reject unsupported neuron-view cases honestly**
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- If a dense layer obtains weights dynamically rather than from a stored tensor, the article's logic declines neuron rendering.
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- Models composed of operators such as convolutions, recurrent structures, or attention are also not forced into an incorrect dense-connection visualization.
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8. **Compute rank/depth of each row**
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- The arrangement is refined by repeatedly pushing each target row one level deeper than the row feeding it.
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- This produces a depth-based layout similar in spirit to the graph layout from the previous article.
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9. **Measure and place rows**
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- Each row width is calculated from its neuron count and spacing settings.
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- Rows sharing the same rank are arranged side by side.
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- The widest rank is used as the centering reference so the full arrangement stays visually balanced.
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10. **Draw weighted connections**
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- For each pair of source and target neurons, the corresponding weight is fetched from the tensor.
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- The indexing rule depends on whether the matrix is stored transposed.
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- Line color reflects sign:
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- positive weights use one color,
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- negative weights use another.
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- Line opacity is scaled relative to the largest absolute weight in the tensor.
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11. **Apply practical visualization limits**
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- The article notes that tensors are capped when read, with the strongest structure preserved first.
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- This means the visualization may show a reduced but meaningful subset of connections for very large matrices rather than every single weight.
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12. **Draw neuron rows and labels**
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- Each neuron is rendered as a circle.
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- Labels are centered above their own row and include the operation label and neuron count.
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13. **Provide explicit failure messages**
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- If the neuron arrangement cannot be built, the panel displays a reason instead of leaving the canvas blank.
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14. **Support graph/neuron view switching**
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- A footer control toggles between the existing graph view and the new neuron view.
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- Sizing, fitting, scrolling, hover text, and tooltips all depend on the active view.
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- If neuron view is unavailable, the control remains dimmed and explains why.
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## Mentioned or Attached Files
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### Explicitly attached files
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The processed article input explicitly lists these attachments:
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1. **make_model.py** — Python script
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Builds the sample classification network and saves it as `model.onnx` under `MQL5\Files\ONNX Model Viewer Part 3\`
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2. **OMV Protobuf.mqh** — Include file
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Defines protobuf wire-type constants and byte-decoding logic for ONNX parsing
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3. **OMV Model.mqh** — Include file
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Defines node, tensor, and port structures and implements model parsing logic
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4. **OMV Canvas.mqh** — Include file
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Provides the drawing surface and rendering helpers
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5. **OMV Panel.mqh** — Include file
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Arranges graph and neuron views, draws both, and handles mouse/UI interaction
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6. **ONNX Model Viewer Part 3.mq5** — Expert Advisor
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Main program entry point that initializes the panel and forwards chart events
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7. **MQL5.zip** — Archive
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Ready-to-extract archive containing the MQL5 folder structure and project files
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### ZIP attachment
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- An attached ZIP archive is available: https://www.mql5.com/en/articles/download/24337_270086.zip?s=d0028f3bb4f47dfde7f5dfc917902de7c95a5fd0d6ed782b6f7af0bed76d2f6d&t=1791568242
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### Files mentioned in text
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- `model.onnx` is mentioned as the generated ONNX model file produced by the Python script.
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## Statistics
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- **Word count:** Not available
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- **Reading time:** Not available
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- **Image count:** Not available
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- **Code block count:** Not available
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- **File count:** Not available
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## Tags
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- mql5
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- metatrader-5
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- expert-advisors
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- statistics
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- onnx
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- neural-networks
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- model-visualization
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- difficulty-advanced
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## Difficulty
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**Advanced**
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Reason: the article combines ONNX model interpretation, tensor-orientation handling, custom layout construction, chart-based rendering, UI interaction, and careful support/rejection logic for multiple operator types.
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## Limitations
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- This repository is based on an article and should be treated as a reference or reconstruction unless the attached files are actually included here.
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- Full functionality depends on the source files and archives actually attached or provided by the article author.
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- The neuron view described in the article is intentionally limited to cases that can be represented honestly as dense neuron-to-neuron connections.
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- The article explicitly notes that convolutions, recurrent layers, attention layers, and dynamically supplied dense weights are not suitable for this visualization approach.
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- The visualization may not render every stored weight in very large tensors, because the article describes capped tensor retention and prioritization of stronger connections.
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- The project should not be assumed production-ready unless validated independently in your own MetaTrader 5 environment.
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## Reference
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- Original article: https://www.mql5.com/en/articles/24337
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- Author profile: https://www.mql5.com/en/users/29210372
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