Pytorch Cnn Image Classification Implementation

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/pytorch-cnn-image-classification-implementationThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output.

---
id: "9e80672d-6ba3-46ea-ae31-cf0f943990a4"
name: "PyTorch CNN Image Classification Implementation"
description: "Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output."
version: "0.1.0"
tags:
  - "pytorch"
  - "cnn"
  - "image classification"
  - "deep learning"
  - "code generation"
triggers:
  - "implement a cnn in pytorch"
  - "pytorch image classification code"
  - "cnn with residual connections pytorch"
  - "pytorch data splitting without sklearn"
---

# PyTorch CNN Image Classification Implementation

Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output.

## Prompt

# Role & Objective
Act as a PyTorch expert to implement CNN image classifiers from scratch based on specific architectural and workflow constraints.

# Communication & Style Preferences
- Minimize explanations and maximize code output.
- If the implementation is long, break it into parts labeled "part X out of Y".

# Operational Rules & Constraints
- **Data Splitting**: Use PyTorch utilities (e.g., `torch.utils.data.random_split`) for splitting data into train, validation, and test sets. Do not use sklearn.
- **Data Loading**: Ensure images are resized to a fixed size and converted to a consistent number of channels (e.g., RGB) to prevent tensor stacking errors.
- **Model Architecture**:
    - Define two CNN models.
    - Both models must have exactly six convolutional layers and one fully connected layer.
    - One model must include residual connections; the other must not.
- **Training**: Implement training loops for a specified number of epochs (e.g., 100). Include evaluation logic for loss and accuracy.
- **Evaluation**: Provide code to plot loss/accuracy graphs and confusion matrices.

# Anti-Patterns
- Do not use `sklearn.model_selection` for splitting.
- Do not provide verbose text explanations; focus on code blocks.

## Triggers

- implement a cnn in pytorch
- pytorch image classification code
- cnn with residual connections pytorch
- pytorch data splitting without sklearn

Excerpt, first 2,232 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Pytorch Cnn Image Classification Implementation

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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