Model Parameter Inspection And Logging

---
name: model-parameter-inspection-and-logging
description: Use when after instantiating a neural network model (such as TransG-Net)
  with multimodal inputs but before beginning training, to validate that the model
  architecture correctly accepts graph features and SMILES embeddings as separate
  modalities and produces expected output tensor shapes.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3435
  edam_topics:
  - http://edamontology.org/topic_0081
  - http://edamontology.org/topic_3474
  tools:
  - RDKit 2020.03.4
  - torch
  - numpy
  - RDKit
  - scikit-learn
  - CUDA
  - cuDNN
  - TransGNet.py
  - data_prep.py
  license_tier: restricted
  provenance_tier: literature
derived_from:
- doi: 10.1007/s10489-022-04351-0
  title: Mass Spectrum Transformer
evidence_spans:
- RDKit == 2020.03.4
- torch >= 1.4.0
- numpy == 1.19.1
- scikit-learn == 0.23.2
- cuda >= 9.0
- cudnn >= 7.0
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_mass_spectrum_transformer_cq
    doi: 10.1007/s10489-022-04351-0
    title: Mass Spectrum Transformer
  dedup_kept_from: coll_mass_spectrum_transformer_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1007/s10489-022-04351-0
  all_source_dois:
  - 10.1007/s10489-022-04351-0
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# model-parameter-inspection-and-logging

> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
## Summary

Systematically inspect and log the architecture, parameter counts, and input/output tensor dimensions of an instantiated neural network model to verify correctness before training. This skill ensures the model's structure matches the intended multimodal input specification and documents the computational footprint for reproducibility.

## When to use

After instantiating a neural network model (such as TransG-Net) with multimodal inputs but before beginning training, to validate that the model architecture correctly accepts graph features and SMILES embeddings as separate modalities and produces expected output tensor shapes.

## When NOT to use

- Model is already in production or post-training analysis; use this skill during development and validation phases only.
- Input data is unavailable or incompatible with the model; first ensure data preparation pipeline (data_prep.py) has executed successfully.
- The model definition is unstable or contains syntax errors; debug and fix the model code before instantiation.

## Inputs

- TransGNet.py model definition file
- Model architecture parameters (layer sizes, embedding dimensions, graph feature dimensions)
- Sample multimodal input tensors: molecular graph representations and SMILES embeddings
- Data preparation pipeline output format specification

## Outputs

- Model architecture summary (layer names and types)
- Total parameter count
- Input tensor shape(s)
- Output tensor shape(s)
- Logged inspection report (text file or console output)

## How to apply

Load the model definition file (e.g., TransGNet.py), instantiate it with architecture parameters matching the multimodal input specification, generate or load sample tensors consistent with the data preparation pipeline output (molecular graphs and SMILES representations), pass samples through the model's forward pass to verify execution, and record the model summary, total parameter count, and input/output tensor dimensions. Log these outputs to a file or console for documentation. The rationale is to catch architectural mismatches early, confirm that the model can handle the dimensionality of graph and SMILES embeddings produced by data_prep.py, and establish a baseline for computational requirements and reproducibility.

## Related tools

- **torch** (Framework for defining, instantiating, and inspecting neural network model architectures and parameters)
- **RDKit** (Generate and preprocess molecular graph features and SMILES embeddings as multimodal inputs for model validation)
- **numpy** (Create and manipulate sample tensor arrays for model forward pass testing)
- **TransGNet.py** (Source file containing the TransG-Net model definition to be instantiated and inspected) — github.com/chensaian/TransG-Net
- **data_prep.py** (Defines the multimodal dataset production pipeline whose output format constrains model input specification) — github.com/chensaian/TransG-Net

## Examples

```
model = TransGNet.TransGNet(input_dim_graph=..., input_dim_smiles=..., hidden_dim=...); sample_graph = torch.randn(batch_size, num_nodes, graph_feature_dim); sample_smiles = torch.randn(batch_size, smiles_embedding_dim); output = model(sample_graph, sample_smiles); print(f'Parameters: {sum(p.numel() for p in model.parameters())}'); print(f'Output shape: {output.shape}')
```

## Evaluation signals

- Model instantiates without errors and accepts sample multimodal tensors (graph features + SMILES embeddings) in forward pass
- Output tensor shapes match the expected dimensionality for downstream loss computation and training
- Total parameter count is logged and matches theoretical expectation based on layer configuration
- Input/output tensor dimensions are documented and consistent with data_prep.py output format
- Model summary can be printed or exported (e.g., via torch.nn.Module.parameters() or third-party summary tools) with no missing or unexpected layers

## Limitations

- This skill does not validate model correctness for the target task; it only confirms architectural integrity and tensor shape compatibility.
- Parameter counts and tensor dimensions may vary depending on batch size and data preprocessing choices; ensure sample inputs reflect actual training conditions.
- The skill assumes TransGNet.py is syntactically correct and dependencies (torch, RDKit, numpy) are installed at the specified versions (torch >= 1.4.0, numpy == 1.19.1, RDKit == 2020.03.4); version mismatches may cause instantiation failures.
- No changelog or version history is available in the repository to track architectural changes over time; each inspection is a snapshot of the current code state.

## Evidence

- [readme] the code of TransG-Net is in TransGNet.py: "the code of TransG-Net is in TransGNet.py"
- [readme] the process of multimodal dataset production is in data_prep.py: "the process of multimodal dataset production is in data_prep.py"
- [other] Instantiate the TransG-Net model with the appropriate architecture parameters matching the multimodal input specification: "Instantiate the TransG-Net model with the appropriate architecture parameters matching the multimodal input specification (graph features and SMILES embeddings)."
- [other] Pass the sample inputs through the instantiated model to verify forward pass execution and output tensor shapes: "Pass the sample inputs through the instantiated model to verify forward pass execution and output tensor shapes."
- [other] Log and record the model architecture summary, parameter count, and input/output tensor dimensions: "Log and record the model architecture summary, parameter count, and input/output tensor dimensions."
- [readme] torch >= 1.4.0 (please upgrade your torch version in order to reduce the training time): "torch >= 1.4.0 (please upgrade your torch version in order to reduce the training time)"