mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-01 11:10:57 -05:00
🧹 Clean up repository: remove generated reports, backup files, and outdated documentation
- Remove generated report files (*.html, *.json) that should be recreated - Remove temporary Quarto cache files (.quarto/cites/) - Remove backup files (settings.ini.backup) when originals exist - Remove legacy configuration (.cursorrules moved to .cursor/rules/) - Remove outdated documentation and status reports - Remove unused Makefile (project uses pyproject.toml + nbdev) All core project files preserved. This cleanup improves maintainability by removing files that should be generated, not stored in version control.
This commit is contained in:
Vijay Janapa Reddi
163
.cursorrules
163
.cursorrules
@@ -1,163 +0,0 @@
|
||||
# NBDev Educational Pattern for TinyTorch Modules
|
||||
|
||||
When working with module development files (`*_dev.py`), follow this educational structure:
|
||||
|
||||
## File Format
|
||||
Use Jupytext percent format with NBDev directives:
|
||||
|
||||
```python
|
||||
# ---
|
||||
# jupyter:
|
||||
# jupytext:
|
||||
# text_representation:
|
||||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.17.1
|
||||
# ---
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
# Module X: Title - Brief Description
|
||||
|
||||
Educational introduction and learning goals...
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| default_exp core.component_name
|
||||
|
||||
# Setup and imports
|
||||
import required_libraries
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 1: Concept Explanation
|
||||
Educational content explaining the concept...
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
class ComponentName:
|
||||
"""
|
||||
Component description.
|
||||
|
||||
TODO: Student implementation instructions.
|
||||
"""
|
||||
def method(self):
|
||||
raise NotImplementedError("Student implementation required")
|
||||
|
||||
# %%
|
||||
#| hide
|
||||
#| export
|
||||
class ComponentName:
|
||||
"""Complete implementation (hidden from students)."""
|
||||
def method(self):
|
||||
# Actual implementation
|
||||
pass
|
||||
```
|
||||
|
||||
## Key NBDev Directives
|
||||
|
||||
- `#| default_exp core.module` - Sets export destination in tinytorch package
|
||||
- `#| export` - Marks code for export to package
|
||||
- `#| hide` - Hides cell from students (instructor solution)
|
||||
- `# %% [markdown]` - Markdown cells for explanations
|
||||
- `# %%` - Code cells
|
||||
|
||||
## Educational Structure
|
||||
|
||||
### 1. **Conceptual Foundation** (REQUIRED)
|
||||
Each module must start with clear conceptual explanations:
|
||||
|
||||
```markdown
|
||||
## Step 1: What is [Concept]?
|
||||
|
||||
**Definition**: Clear, simple definition with examples
|
||||
**Why it matters**: Real-world motivation and ML context
|
||||
**How it works**: Intuitive explanation before math
|
||||
**Visual examples**: Concrete examples, diagrams, analogies
|
||||
**Connection**: How it builds on previous modules
|
||||
```
|
||||
|
||||
### 2. **Guided Implementation** (REQUIRED)
|
||||
Instead of just `raise NotImplementedError`, provide:
|
||||
|
||||
```python
|
||||
def method(self):
|
||||
"""
|
||||
TODO: Step-by-step implementation guide
|
||||
|
||||
HINTS:
|
||||
1. First, think about what this method should return
|
||||
2. Consider the data types and shapes involved
|
||||
3. Start with a simple case, then generalize
|
||||
|
||||
EXAMPLE:
|
||||
Input: [describe input]
|
||||
Expected output: [describe expected output]
|
||||
"""
|
||||
# Step 1: [specific guidance]
|
||||
# Step 2: [specific guidance]
|
||||
# Step 3: [specific guidance]
|
||||
raise NotImplementedError("Student implementation required")
|
||||
```
|
||||
|
||||
### 3. **Standalone Learning** (REQUIRED)
|
||||
Each module should be self-contained:
|
||||
- Brief concept review (even if covered in textbook)
|
||||
- Clear motivation for why this matters
|
||||
- Concrete examples before abstract implementation
|
||||
- Connection to previous modules and next steps
|
||||
|
||||
### 4. **Educational Flow** (REQUIRED)
|
||||
Follow this structure for every module:
|
||||
|
||||
1. **Concept → Motivation → Intuition → Implementation → Testing**
|
||||
2. **Visual examples** (shapes, transformations, operations)
|
||||
3. **Real-world analogies** (containers, functions, pattern matching)
|
||||
4. **Scaffolded complexity** (start simple, build up)
|
||||
|
||||
### 5. **Implementation Guidance** (REQUIRED)
|
||||
For each TODO section, include:
|
||||
|
||||
```python
|
||||
"""
|
||||
TODO: [Clear task description]
|
||||
|
||||
APPROACH:
|
||||
1. [First step with specific guidance]
|
||||
2. [Second step with specific guidance]
|
||||
3. [Third step with specific guidance]
|
||||
|
||||
EXAMPLE:
|
||||
Input: [concrete example]
|
||||
Expected: [concrete expected output]
|
||||
Your code should: [specific behavior description]
|
||||
|
||||
HINTS:
|
||||
- [specific hint 1]
|
||||
- [specific hint 2]
|
||||
- [specific hint 3]
|
||||
"""
|
||||
```
|
||||
|
||||
### 6. **Testing and Feedback** (REQUIRED)
|
||||
Include immediate testing cells:
|
||||
- Simple test cases with expected outputs
|
||||
- Clear error messages for debugging
|
||||
- Progressive complexity (simple → complex)
|
||||
- Visual verification where possible
|
||||
|
||||
## Naming Convention
|
||||
|
||||
Module files should be named `{module_name}_dev.py` to indicate development notebooks, following the established pattern from [modules/setup/setup_dev.py](mdc:modules/setup/setup_dev.py) and [modules/tensor/tensor_dev.py](mdc:modules/tensor/tensor_dev.py).
|
||||
|
||||
## Educational Principles
|
||||
|
||||
1. **Build → Use → Understand → Repeat**: Each module should follow this cycle
|
||||
2. **Immediate Feedback**: Students should see results quickly
|
||||
3. **Concrete Before Abstract**: Start with examples, then generalize
|
||||
4. **Scaffolded Learning**: Provide hints and guidance, not just empty functions
|
||||
5. **Standalone Modules**: Each module should be understandable independently
|
||||
6. **Visual Learning**: Include diagrams, examples, and visualizations
|
||||
7. **Real-world Context**: Connect to practical ML applications
|
||||
@@ -1 +0,0 @@
|
||||
{"docs/advanced/deployment.qmd":[],"docs/advanced/optimization.qmd":[],"docs/getting-started/installation.qmd":[],"docs/getting-started/quickstart.qmd":[],"docs/modules/00-00_setup.qmd":[],"docs/modules/01-01_tensor.qmd":[],"docs/modules/02-02_activations.qmd":[],"docs/modules/03-03_layers.qmd":[],"docs/modules/04-04_networks.qmd":[],"docs/modules/05-05_cnn.qmd":[],"docs/modules/06-06_dataloader.qmd":[],"docs/modules/07-07_autograd.qmd":[],"docs/modules/08-08_optimizers.qmd":[],"docs/modules/09-09_training.qmd":[],"docs/modules/10-10_compression.qmd":[],"docs/modules/11-11_kernels.qmd":[],"docs/modules/12-12_benchmarking.qmd":[],"docs/modules/13-13_mlops.qmd":[],"docs/modules/13-mlops.qmd":[],"index.qmd":[]}
|
||||
@@ -1,220 +0,0 @@
|
||||
# TinyTorch Comprehensive Status Report
|
||||
|
||||
## 🎯 Mission Accomplished: Systematic Reorganization Complete
|
||||
|
||||
Following your request to "organize this in a way that makes sense," I've successfully completed a comprehensive reorganization and improvement of the TinyTorch educational framework.
|
||||
|
||||
## 📋 Completed Work (Branch-by-Branch)
|
||||
|
||||
### ✅ Phase 1: Repository Structure Reorganization
|
||||
**Branch**: `refactor/repository-structure` → **MERGED**
|
||||
|
||||
**Accomplished**:
|
||||
- Created logical `instructor/` directory structure
|
||||
- Moved analysis tools to `instructor/tools/`
|
||||
- Moved reports to `instructor/reports/`
|
||||
- Moved guides to `instructor/guides/`
|
||||
- Created `docs/` structure for future Quarto documentation
|
||||
- Built wrapper script (`analyze_modules.py`) for easy access
|
||||
- Created comprehensive instructor documentation
|
||||
|
||||
**Result**: Clean, professional repository structure with instructor resources properly organized.
|
||||
|
||||
### ✅ Phase 2: Comprehensive Testing & Module Exports
|
||||
**Branch**: `feature/comprehensive-testing` → **MERGED**
|
||||
|
||||
**Accomplished**:
|
||||
- Fixed pytest configuration issues
|
||||
- Exported all modules to tinytorch package using NBDev
|
||||
- Converted .py files to .ipynb for proper NBDev processing
|
||||
- Fixed import issues in test files with fallback strategies
|
||||
- Resolved all critical import errors
|
||||
|
||||
**Test Results**: **145 tests passing, 15 failing, 16 skipped**
|
||||
- Major improvement from previous import errors
|
||||
- All modules now properly exported and testable
|
||||
- Analysis tools working correctly on all modules
|
||||
|
||||
## 📊 Current Module Quality Assessment
|
||||
|
||||
### Overall Status
|
||||
```
|
||||
00_setup: Grade C | Scaffolding 3/5
|
||||
01_tensor: Grade C | Scaffolding 2/5 ← Needs improvement
|
||||
02_activations: Grade C | Scaffolding 3/5
|
||||
03_layers: Grade C | Scaffolding 3/5
|
||||
04_networks: Grade C | Scaffolding 3/5
|
||||
05_cnn: Grade C | Scaffolding 3/5
|
||||
06_dataloader: Grade C | Scaffolding 3/5
|
||||
07_autograd: Grade D | Scaffolding 2/5 ← Needs improvement
|
||||
```
|
||||
|
||||
### Professional Report Cards Generated
|
||||
- **Complete HTML reports** for all 8 modules
|
||||
- **JSON data files** for programmatic analysis
|
||||
- **Stored in** `instructor/reports/` with timestamps
|
||||
- **Accessible via** `analyze_modules.py --all --save`
|
||||
|
||||
## 🎯 Key Achievements
|
||||
|
||||
### 1. **Repository Organization Excellence**
|
||||
- **Instructor Resources**: Properly organized in `instructor/` directory
|
||||
- **Analysis Tools**: Centralized in `instructor/tools/`
|
||||
- **Documentation**: Structured in `instructor/guides/`
|
||||
- **Reports**: Automated generation in `instructor/reports/`
|
||||
|
||||
### 2. **Testing Infrastructure Rebuilt**
|
||||
- **NBDev Integration**: All modules properly exported
|
||||
- **Import Resolution**: Fixed all critical import errors
|
||||
- **Test Compatibility**: 145/176 tests passing (82% success rate)
|
||||
- **Continuous Analysis**: Automated quality monitoring
|
||||
|
||||
### 3. **Educational Quality Framework**
|
||||
- **Quantitative Assessment**: Data-driven module evaluation
|
||||
- **Professional Report Cards**: Beautiful HTML and JSON reports
|
||||
- **Improvement Tracking**: Baseline established for all modules
|
||||
- **Best Practices**: Comprehensive guidelines documented
|
||||
|
||||
## 🔧 Technical Implementation
|
||||
|
||||
### Directory Structure (Final)
|
||||
```
|
||||
TinyTorch/
|
||||
├── instructor/ # Instructor resources
|
||||
│ ├── tools/ # Analysis scripts
|
||||
│ │ ├── tinytorch_module_analyzer.py
|
||||
│ │ └── analysis_notebook_structure.py
|
||||
│ ├── reports/ # Generated report cards
|
||||
│ ├── guides/ # Instructor documentation
|
||||
│ └── templates/ # Future templates
|
||||
├── modules/source/ # Student-facing modules
|
||||
├── docs/ # Documentation structure
|
||||
├── tests/ # Test suites
|
||||
├── tinytorch/ # Main package (fully exported)
|
||||
└── analyze_modules.py # Easy-access wrapper
|
||||
```
|
||||
|
||||
### Analysis Tool Usage
|
||||
```bash
|
||||
# Analyze all modules
|
||||
python3 analyze_modules.py --all
|
||||
|
||||
# Analyze specific module with reports
|
||||
python3 analyze_modules.py --module 02_activations --save
|
||||
|
||||
# Compare modules
|
||||
python3 analyze_modules.py --compare 01_tensor 02_activations
|
||||
```
|
||||
|
||||
## 🎓 Educational Impact
|
||||
|
||||
### Before vs. After
|
||||
- **Before**: Scattered tools, broken imports, no systematic assessment
|
||||
- **After**: Organized structure, working tests, comprehensive analysis
|
||||
|
||||
### Quality Metrics Established
|
||||
- **Scaffolding Quality**: 1-5 scale assessment
|
||||
- **Complexity Distribution**: Student overwhelm detection
|
||||
- **Learning Progression**: Educational flow analysis
|
||||
- **Best Practice Compliance**: Systematic evaluation
|
||||
|
||||
### Professional Report Cards
|
||||
Each module now has:
|
||||
- **Overall Grade** (A-F)
|
||||
- **Category Breakdown** (Scaffolding, Complexity, Cell Length)
|
||||
- **Specific Issues** identified
|
||||
- **Actionable Recommendations**
|
||||
- **Progress Tracking** capability
|
||||
|
||||
## 🚀 Next Steps & Recommendations
|
||||
|
||||
### Immediate Priorities (Based on Analysis)
|
||||
1. **Improve Tensor Module** (Grade C, Scaffolding 2/5)
|
||||
- Add implementation ladders
|
||||
- Improve concept bridges
|
||||
- Reduce complexity cliffs
|
||||
|
||||
2. **Enhance Autograd Module** (Grade D, Scaffolding 2/5)
|
||||
- Complete missing functionality
|
||||
- Add comprehensive scaffolding
|
||||
- Improve educational explanations
|
||||
|
||||
3. **Standardize All Modules** to Grade B+ (Scaffolding 4/5)
|
||||
- Apply "Rule of 3s" framework
|
||||
- Add confidence builders
|
||||
- Implement progressive complexity
|
||||
|
||||
### Planned Phases (Ready for Branch Implementation)
|
||||
1. **Phase 3**: Professional Report Card Enhancement
|
||||
2. **Phase 4**: Quarto Documentation System
|
||||
3. **Phase 5**: Analysis Integration & Automation
|
||||
|
||||
## 📈 Success Metrics
|
||||
|
||||
### Repository Organization
|
||||
- ✅ Clean, logical directory structure
|
||||
- ✅ All tools in appropriate locations
|
||||
- ✅ No broken imports or functionality
|
||||
- ✅ Easy to navigate and understand
|
||||
|
||||
### Testing & Quality
|
||||
- ✅ 82% test pass rate (145/176 tests)
|
||||
- ✅ All analysis tools working correctly
|
||||
- ✅ No critical import errors
|
||||
- ✅ Comprehensive test coverage
|
||||
|
||||
### Educational Assessment
|
||||
- ✅ Professional, formatted report cards
|
||||
- ✅ Consistent analysis framework
|
||||
- ✅ Clear, actionable insights
|
||||
- ✅ Automated quality monitoring
|
||||
|
||||
## 🎯 Strategic Value
|
||||
|
||||
### For Instructors
|
||||
- **Data-Driven Decisions**: Objective quality assessment
|
||||
- **Continuous Improvement**: Track progress over time
|
||||
- **Best Practice Identification**: Learn from high-scoring modules
|
||||
- **Student Experience Optimization**: Prevent overwhelm
|
||||
|
||||
### For Students
|
||||
- **Better Learning Experience**: Improved scaffolding coming
|
||||
- **Consistent Quality**: Standardized educational approach
|
||||
- **Reduced Frustration**: Issues identified and prioritized
|
||||
- **Progressive Learning**: Complexity managed appropriately
|
||||
|
||||
### For Course Development
|
||||
- **Quality Assurance**: Systematic evaluation framework
|
||||
- **Improvement Roadmap**: Clear priorities established
|
||||
- **Scalable Process**: Reusable analysis tools
|
||||
- **Professional Standards**: Industry-level organization
|
||||
|
||||
## 🔄 Development Workflow Established
|
||||
|
||||
### Branch-Based Development ✅
|
||||
- **Always create branches** for work
|
||||
- **Plan → Reason → Test → Execute → Verify → Merge**
|
||||
- **Comprehensive testing** before merging
|
||||
- **Quality gates** enforced
|
||||
|
||||
### Continuous Quality Monitoring ✅
|
||||
- **Automated analysis** after changes
|
||||
- **Report card generation** for tracking
|
||||
- **Improvement measurement** over time
|
||||
- **Best practice enforcement**
|
||||
|
||||
## 🎊 Summary
|
||||
|
||||
**Mission Status**: **COMPLETE** for reorganization and testing phases
|
||||
|
||||
**Key Deliverables**:
|
||||
1. ✅ **Organized Repository Structure** - Professional, logical organization
|
||||
2. ✅ **Working Test Infrastructure** - 82% test pass rate achieved
|
||||
3. ✅ **Comprehensive Analysis Framework** - Data-driven quality assessment
|
||||
4. ✅ **Professional Report Cards** - Beautiful, actionable reports
|
||||
5. ✅ **Instructor Resources** - Complete documentation and tools
|
||||
6. ✅ **Development Workflow** - Branch-based, quality-focused process
|
||||
|
||||
**Next Phase Ready**: Professional report card enhancement and Quarto documentation system.
|
||||
|
||||
**The TinyTorch educational framework is now professionally organized, systematically analyzed, and ready for targeted improvements based on data-driven insights.**
|
||||
@@ -1,152 +0,0 @@
|
||||
# TinyTorch Module Analysis Summary
|
||||
|
||||
## Key Findings
|
||||
|
||||
### ✅ **Excellent Foundation (setup_dev.py)**
|
||||
- **Perfect structure**: Follows explain → code → test → repeat pattern
|
||||
- **Rich scaffolding**: Every TODO has step-by-step guidance
|
||||
- **Immediate feedback**: Tests run after each concept
|
||||
- **Educational flow**: Concepts build logically with real-world connections
|
||||
|
||||
### ⚠️ **Structural Issues (Modules 01-07)**
|
||||
- **Content quality**: Excellent mathematical explanations and implementations
|
||||
- **Testing pattern**: All tests at end instead of progressive testing
|
||||
- **TODO scaffolding**: Generic `NotImplementedError` without guidance
|
||||
- **Student experience**: Large amounts of code before getting feedback
|
||||
|
||||
### ❌ **Missing Modules (08-13)**
|
||||
- **Empty directories**: 5 out of 13 modules are completely empty
|
||||
- **Critical gaps**: Optimizers, training, MLOps missing
|
||||
|
||||
## Immediate Action Items
|
||||
|
||||
### 1. **Fix Testing Pattern (High Priority)**
|
||||
Transform this poor pattern:
|
||||
```python
|
||||
# All implementations
|
||||
def concept_1(): pass
|
||||
def concept_2(): pass
|
||||
def concept_3(): pass
|
||||
|
||||
# All tests at end
|
||||
def test_everything(): pass
|
||||
```
|
||||
|
||||
To this excellent pattern:
|
||||
```python
|
||||
# Concept 1
|
||||
def concept_1(): pass
|
||||
def test_concept_1(): pass
|
||||
print("✅ Concept 1 tests passed!")
|
||||
|
||||
# Concept 2
|
||||
def concept_2(): pass
|
||||
def test_concept_2(): pass
|
||||
print("✅ Concept 2 tests passed!")
|
||||
```
|
||||
|
||||
### 2. **Enhance TODO Blocks (High Priority)**
|
||||
Replace generic todos:
|
||||
```python
|
||||
def add(self, other):
|
||||
"""Add two tensors."""
|
||||
raise NotImplementedError("Student implementation required")
|
||||
```
|
||||
|
||||
With rich scaffolding:
|
||||
```python
|
||||
def add(self, other):
|
||||
"""
|
||||
TODO: Implement tensor addition.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Get numpy data from both tensors
|
||||
2. Use numpy's + operator
|
||||
3. Create new Tensor with result
|
||||
4. Return the new tensor
|
||||
|
||||
EXAMPLE USAGE:
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[5, 6], [7, 8]])
|
||||
result = t1.add(t2) # [[6, 8], [10, 12]]
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Use self._data + other._data
|
||||
- Wrap result in new Tensor
|
||||
- NumPy handles broadcasting
|
||||
"""
|
||||
```
|
||||
|
||||
### 3. **Module Priority for Fixes**
|
||||
1. **01_tensor** (Highest) - Foundation for everything
|
||||
2. **02_activations** (High) - Used in all networks
|
||||
3. **03_layers** (High) - Core building blocks
|
||||
4. **07_autograd** (High) - Enables training
|
||||
5. **04_networks** (Medium) - Compositions
|
||||
6. **05_cnn** (Medium) - Specialized operations
|
||||
7. **06_dataloader** (Medium) - Data handling
|
||||
|
||||
## Implementation Strategy
|
||||
|
||||
### Phase 1: Transform Existing Modules (Weeks 1-2)
|
||||
For each module (01-07):
|
||||
1. **Identify breakpoints**: Find natural concept boundaries
|
||||
2. **Reorganize structure**: Create Step 1, Step 2, etc. with explanations
|
||||
3. **Add immediate testing**: Test after each major concept
|
||||
4. **Enhance TODO blocks**: Add step-by-step guidance
|
||||
5. **Include success messages**: Clear progress indicators
|
||||
|
||||
### Phase 2: Create Missing Modules (Weeks 3-4)
|
||||
Using the improved structure:
|
||||
- **08_optimizers**: SGD, Adam, learning rate scheduling
|
||||
- **09_training**: Training loops, loss functions, metrics
|
||||
- **10_compression**: Pruning, quantization, knowledge distillation
|
||||
- **11_kernels**: Custom operations, CUDA kernels
|
||||
- **12_benchmarking**: Performance measurement, profiling
|
||||
- **13_mlops**: Model deployment, monitoring, versioning
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Student Experience
|
||||
- **Immediate feedback**: Results after each concept
|
||||
- **Clear guidance**: Step-by-step implementation instructions
|
||||
- **Progressive complexity**: Each step builds on previous success
|
||||
- **Debugging support**: Clear error messages and examples
|
||||
|
||||
### Educational Quality
|
||||
- **Consistent structure**: All modules follow same pattern
|
||||
- **Rich scaffolding**: Every function has detailed guidance
|
||||
- **Real-world connections**: Theory linked to practice
|
||||
- **Integration**: Modules work together seamlessly
|
||||
|
||||
## Next Steps
|
||||
|
||||
### Week 1: Start with Tensor Module
|
||||
1. **Backup current**: Create `tensor_dev_backup.py`
|
||||
2. **Reorganize structure**: Break into progressive steps
|
||||
3. **Add immediate testing**: Test after each operation type
|
||||
4. **Test with students**: Validate improved experience
|
||||
|
||||
### Week 2: Apply to Activations & Layers
|
||||
1. **Apply same pattern**: Use tensor module as template
|
||||
2. **Focus on scaffolding**: Rich TODO blocks
|
||||
3. **Add visualizations**: Where helpful for understanding
|
||||
4. **Progressive testing**: After each activation/layer type
|
||||
|
||||
### Week 3-4: Complete Missing Modules
|
||||
1. **Use proven pattern**: Follow successful structure
|
||||
2. **Real-world focus**: Production-ready implementations
|
||||
3. **Integration testing**: Ensure modules work together
|
||||
4. **Documentation**: Clear learning outcomes
|
||||
|
||||
## Key Principle
|
||||
|
||||
**Always follow: Explain → Code → Test → Repeat**
|
||||
|
||||
This pattern maximizes student success through:
|
||||
- Immediate feedback prevents confusion
|
||||
- Rich scaffolding reduces frustration
|
||||
- Progressive complexity builds confidence
|
||||
- Clear connections show the bigger picture
|
||||
|
||||
The goal is to transform TinyTorch from reference material into a guided learning experience that creates deep understanding of ML systems.
|
||||
@@ -1,169 +0,0 @@
|
||||
# TinyTorch Module Test Status Report
|
||||
*Generated on: $(date)*
|
||||
|
||||
## Executive Summary
|
||||
|
||||
✅ **All implemented modules are passing their tests with 100% success rate**
|
||||
- **13 modules tested** (00-12)
|
||||
- **63 tests passed** (63/63)
|
||||
- **1 module not implemented** (13_mlops)
|
||||
- **0 failures** across all modules
|
||||
|
||||
## Module Status Overview
|
||||
|
||||
| Module | Status | Inline Tests | External Tests | Total Tests | Notes |
|
||||
|--------|--------|-------------|---------------|-------------|-------|
|
||||
| 00_setup | ✅ PASS | 6/6 | 0/0 | 6/6 | Environment validation, system info |
|
||||
| 01_tensor | ✅ PASS | 4/4 | 0/0 | 4/4 | Core tensor operations |
|
||||
| 02_activations | ✅ PASS | 5/5 | 0/0 | 5/5 | ReLU, Sigmoid, Tanh, Softmax |
|
||||
| 03_layers | ✅ PASS | 3/3 | 0/0 | 3/3 | Dense layer, matrix multiplication |
|
||||
| 04_networks | ✅ PASS | 4/4 | 0/0 | 4/4 | MLP, sequential networks |
|
||||
| 05_cnn | ✅ PASS | 3/3 | 0/0 | 3/3 | Conv2D, convolution operations |
|
||||
| 06_dataloader | ✅ PASS | 4/4 | 0/0 | 4/4 | Dataset interface, data loading |
|
||||
| 07_autograd | ✅ PASS | 6/6 | 0/0 | 6/6 | Automatic differentiation |
|
||||
| 08_optimizers | ✅ PASS | 5/5 | 0/0 | 5/5 | SGD, Adam, learning rate scheduling |
|
||||
| 09_training | ✅ PASS | 6/6 | 0/0 | 6/6 | Training loops, loss functions |
|
||||
| 10_compression | ✅ PASS | 6/6 | 0/0 | 6/6 | Pruning, quantization, distillation |
|
||||
| 11_kernels | ✅ PASS | 6/6 | 0/0 | 6/6 | Optimized kernel implementations |
|
||||
| 12_benchmarking | ✅ PASS | 5/5 | 0/0 | 5/5 | Performance benchmarking |
|
||||
| 13_mlops | ❌ NOT IMPLEMENTED | - | - | - | Directory exists but no implementation |
|
||||
|
||||
## Detailed Test Results
|
||||
|
||||
### Module 00_setup (6/6 tests passed)
|
||||
- ✅ test_development_setup
|
||||
- ✅ test_environment_validation
|
||||
- ✅ test_performance_benchmark
|
||||
- ✅ test_personal_info
|
||||
- ✅ test_system_info
|
||||
- ✅ test_system_report
|
||||
|
||||
### Module 01_tensor (4/4 tests passed)
|
||||
- ✅ test_tensor
|
||||
- ✅ test_tensor_arithmetic
|
||||
- ✅ test_tensor_creation
|
||||
- ✅ test_tensor_properties
|
||||
|
||||
### Module 02_activations (5/5 tests passed)
|
||||
- ✅ test_activations
|
||||
- ✅ test_relu_activation
|
||||
- ✅ test_sigmoid_activation
|
||||
- ✅ test_softmax_activation
|
||||
- ✅ test_tanh_activation
|
||||
|
||||
### Module 03_layers (3/3 tests passed)
|
||||
- ✅ test_dense_layer
|
||||
- ✅ test_layer_activation
|
||||
- ✅ test_matrix_multiplication
|
||||
|
||||
### Module 04_networks (4/4 tests passed)
|
||||
- ✅ test_mlp_creation
|
||||
- ✅ test_network_architectures
|
||||
- ✅ test_networks
|
||||
- ✅ test_sequential_networks
|
||||
|
||||
### Module 05_cnn (3/3 tests passed)
|
||||
- ✅ test_conv2d_layer
|
||||
- ✅ test_convolution_operation
|
||||
- ✅ test_flatten_function
|
||||
|
||||
### Module 06_dataloader (4/4 tests passed)
|
||||
- ✅ test_dataloader
|
||||
- ✅ test_dataloader_pipeline
|
||||
- ✅ test_dataset_interface
|
||||
- ✅ test_simple_dataset
|
||||
|
||||
### Module 07_autograd (6/6 tests passed)
|
||||
- ✅ test_add_operation
|
||||
- ✅ test_chain_rule
|
||||
- ✅ test_multiply_operation
|
||||
- ✅ test_neural_network_training
|
||||
- ✅ test_subtract_operation
|
||||
- ✅ test_variable_class
|
||||
|
||||
### Module 08_optimizers (5/5 tests passed)
|
||||
- ✅ test_adam_optimizer
|
||||
- ✅ test_gradient_descent_step
|
||||
- ✅ test_sgd_optimizer
|
||||
- ✅ test_step_scheduler
|
||||
- ✅ test_training_integration
|
||||
|
||||
### Module 09_training (6/6 tests passed)
|
||||
- ✅ test_accuracy_metric
|
||||
- ✅ test_binary_crossentropy_loss
|
||||
- ✅ test_crossentropy_loss
|
||||
- ✅ test_mse_loss
|
||||
- ✅ test_trainer
|
||||
- ✅ test_training
|
||||
|
||||
### Module 10_compression (6/6 tests passed)
|
||||
- ✅ test_comprehensive_comparison
|
||||
- ✅ test_compression_metrics
|
||||
- ✅ test_distillation
|
||||
- ✅ test_magnitude_pruning
|
||||
- ✅ test_quantization
|
||||
- ✅ test_structured_pruning
|
||||
|
||||
### Module 11_kernels (6/6 tests passed)
|
||||
- ✅ test_cache_friendly_matmul
|
||||
- ✅ test_compressed_kernels
|
||||
- ✅ test_matmul_baseline
|
||||
- ✅ test_parallel_processing
|
||||
- ✅ test_simple_kernel_timing
|
||||
- ✅ test_vectorized_operations
|
||||
|
||||
### Module 12_benchmarking (5/5 tests passed)
|
||||
- ✅ test_benchmark_scenarios
|
||||
- ✅ test_comprehensive_benchmarking
|
||||
- ✅ test_performance_reporter
|
||||
- ✅ test_statistical_validation
|
||||
- ✅ test_tinytorch_perf
|
||||
|
||||
## Testing Infrastructure
|
||||
|
||||
### Test Command Used
|
||||
```bash
|
||||
tito test --all --summary
|
||||
```
|
||||
|
||||
### Test Features
|
||||
- **Inline Tests**: All tests are embedded within the module development files
|
||||
- **Automatic Compilation Check**: Each module is checked for syntax errors
|
||||
- **External Tests**: Currently no external tests configured (all use inline testing)
|
||||
- **Comprehensive Reporting**: Detailed pass/fail status with counts
|
||||
- **Sync Reminder**: Built-in reminder to export and build before testing
|
||||
|
||||
### Test Environment
|
||||
- **Framework**: TinyTorch custom testing framework
|
||||
- **Platform**: macOS (darwin 24.5.0)
|
||||
- **Shell**: bash
|
||||
- **Virtual Environment**: Active (.venv)
|
||||
|
||||
## Issues Found
|
||||
|
||||
### 13_mlops Module
|
||||
- **Status**: Not implemented
|
||||
- **Issue**: Directory exists but contains no implementation files
|
||||
- **Expected File**: `modules/source/13_mlops/mlops_dev.py`
|
||||
- **Impact**: This appears to be a planned but not yet implemented module
|
||||
|
||||
## Recommendations
|
||||
|
||||
1. **All current modules are working perfectly** - No immediate action needed
|
||||
2. **Consider implementing 13_mlops** if MLOps functionality is needed
|
||||
3. **Maintain current inline testing approach** - It's working well
|
||||
4. **Regular testing** - Continue using `tito test --all` for comprehensive validation
|
||||
|
||||
## Key Achievements
|
||||
|
||||
- ✅ **100% test success rate** across all implemented modules
|
||||
- ✅ **63 comprehensive tests** covering all major functionality
|
||||
- ✅ **Consistent inline testing pattern** across all modules
|
||||
- ✅ **Robust testing infrastructure** with clear reporting
|
||||
- ✅ **Educational value** - tests serve as examples for students
|
||||
|
||||
## Conclusion
|
||||
|
||||
The TinyTorch project is in excellent health with all 13 implemented modules passing their comprehensive test suites. The testing infrastructure is robust and provides clear feedback. The only missing component is the 13_mlops module, which appears to be planned but not yet implemented.
|
||||
|
||||
**Overall Status: 🟢 HEALTHY - All systems operational**
|
||||
96
Makefile
96
Makefile
@@ -1,96 +0,0 @@
|
||||
# TinyTorch - Module-first ML Systems Course
|
||||
# Makefile for convenience commands
|
||||
|
||||
.PHONY: help install sync test clean docs jupyter lab setup
|
||||
|
||||
# Default target
|
||||
help:
|
||||
@echo "TinyTorch - Build ML Systems from Scratch"
|
||||
@echo "=========================================="
|
||||
@echo ""
|
||||
@echo "Available commands:"
|
||||
@echo ""
|
||||
@echo " install Install dependencies and setup environment"
|
||||
@echo " sync Export notebook code to Python package"
|
||||
@echo " test Run all tests"
|
||||
@echo " test-setup Run setup module tests"
|
||||
@echo " clean Clean notebook outputs and cache"
|
||||
@echo " docs Build documentation"
|
||||
@echo " jupyter Start Jupyter Lab"
|
||||
@echo " info Show system information"
|
||||
@echo " doctor Run environment diagnosis"
|
||||
@echo ""
|
||||
@echo "Development workflow:"
|
||||
@echo " 1. make jupyter # Work in modules/[name]/[name].ipynb"
|
||||
@echo " 2. make sync # Export to tinytorch package"
|
||||
@echo " 3. make test # Run tests"
|
||||
@echo " 4. make docs # Build docs (optional)"
|
||||
|
||||
# Install dependencies
|
||||
install:
|
||||
@echo "🔧 Installing TinyTorch dependencies..."
|
||||
pip install -r requirements.txt
|
||||
pip install nbdev jupyter
|
||||
@echo "✅ Installation complete!"
|
||||
|
||||
# Export notebooks to Python package
|
||||
sync:
|
||||
@echo "🔄 Exporting notebooks to Python package..."
|
||||
@source .venv/bin/activate && python3 bin/tito export --all
|
||||
|
||||
# Run all tests
|
||||
test:
|
||||
@echo "🧪 Running all tests..."
|
||||
@source .venv/bin/activate && python3 bin/tito module test --all
|
||||
|
||||
# Run setup module tests specifically
|
||||
test-setup:
|
||||
@echo "🧪 Running setup module tests..."
|
||||
@source .venv/bin/activate && python3 bin/tito module test --module setup
|
||||
|
||||
# Clean notebook outputs and Python cache
|
||||
clean:
|
||||
@echo "🧹 Cleaning notebook outputs and cache..."
|
||||
@source .venv/bin/activate && python3 bin/tito module clean
|
||||
find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true
|
||||
find . -type f -name "*.pyc" -delete 2>/dev/null || true
|
||||
@echo "✅ Cleanup complete!"
|
||||
|
||||
# Build documentation
|
||||
docs:
|
||||
@echo "📚 Building documentation..."
|
||||
@source .venv/bin/activate && python3 bin/tito package build-docs
|
||||
|
||||
# Start Jupyter Lab
|
||||
jupyter:
|
||||
@echo "🚀 Starting Jupyter Lab..."
|
||||
@source .venv/bin/activate && python3 bin/tito system jupyter --lab
|
||||
|
||||
# Alias for jupyter
|
||||
lab: jupyter
|
||||
|
||||
# Show system information
|
||||
info:
|
||||
@echo "ℹ️ System information..."
|
||||
@source .venv/bin/activate && python3 bin/tito system info
|
||||
|
||||
# Run environment diagnosis
|
||||
doctor:
|
||||
@echo "🔬 Running environment diagnosis..."
|
||||
@source .venv/bin/activate && python3 bin/tito system doctor
|
||||
|
||||
# Setup new environment (for first-time users)
|
||||
setup:
|
||||
@echo "🚀 Setting up TinyTorch development environment..."
|
||||
@echo "1. Creating virtual environment..."
|
||||
python -m venv .venv
|
||||
@echo "2. Installing dependencies..."
|
||||
.venv/bin/pip install -r requirements.txt
|
||||
.venv/bin/pip install nbdev jupyter
|
||||
@echo ""
|
||||
@echo "✅ Setup complete!"
|
||||
@echo ""
|
||||
@echo "Next steps:"
|
||||
@echo " 1. Activate the environment: source .venv/bin/activate"
|
||||
@echo " 2. Start coding: make jupyter"
|
||||
@echo " 3. Test your code: make sync && make test"
|
||||
@@ -1,592 +0,0 @@
|
||||
great# Module Improvement Guide: From Poor to Excellent Structure
|
||||
|
||||
## Example: Transforming 01_tensor Module
|
||||
|
||||
This guide shows how to transform the tensor module from its current structure to follow the **explain → code → test → repeat** pattern exemplified by `setup_dev.py`.
|
||||
|
||||
## Current Problem Structure
|
||||
|
||||
```python
|
||||
# Current tensor_dev.py structure (POOR)
|
||||
# Lines 1-300: All explanations
|
||||
# Lines 300-700: All implementations
|
||||
# Lines 700-1536: All tests at the end
|
||||
|
||||
class Tensor:
|
||||
def __init__(self):
|
||||
raise NotImplementedError("Student implementation required")
|
||||
|
||||
def add(self):
|
||||
raise NotImplementedError("Student implementation required")
|
||||
|
||||
def multiply(self):
|
||||
raise NotImplementedError("Student implementation required")
|
||||
|
||||
# Much later...
|
||||
def test_tensor_creation_comprehensive():
|
||||
# Tests everything at once
|
||||
pass
|
||||
|
||||
def test_tensor_arithmetic_comprehensive():
|
||||
# Tests everything at once
|
||||
pass
|
||||
```
|
||||
|
||||
## Improved Structure (EXCELLENT)
|
||||
|
||||
```python
|
||||
# Improved tensor_dev.py structure (EXCELLENT)
|
||||
# Following: Explain → Code → Test → Repeat
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 1: What is a Tensor?
|
||||
|
||||
### Definition
|
||||
A **tensor** is an N-dimensional array with ML-specific operations.
|
||||
|
||||
### Why Tensors Matter
|
||||
- **Foundation**: Every ML framework uses tensors
|
||||
- **Efficiency**: Vectorized operations are faster
|
||||
- **Flexibility**: Same operations work on scalars, vectors, matrices
|
||||
|
||||
### Real-World Examples
|
||||
```python
|
||||
# Scalar (0D): A single number
|
||||
temperature = Tensor(25.0)
|
||||
|
||||
# Vector (1D): A list of numbers
|
||||
rgb_color = Tensor([255, 128, 0])
|
||||
|
||||
# Matrix (2D): Image pixels
|
||||
image = Tensor([[100, 150], [200, 250]])
|
||||
```
|
||||
|
||||
Let's build this step by step!
|
||||
"""
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 1A: Tensor Creation
|
||||
|
||||
### The Foundation Operation
|
||||
Creating tensors is the first thing you'll do in any ML system. Our Tensor class needs to:
|
||||
1. Accept various input types (lists, numpy arrays, scalars)
|
||||
2. Store data efficiently
|
||||
3. Track shape and type information
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": false, "grade_id": "tensor-creation", "locked": false, "schema_version": 3, "solution": true, "task": false}
|
||||
#| export
|
||||
class Tensor:
|
||||
def __init__(self, data: Union[int, float, List, np.ndarray], dtype: Optional[str] = None):
|
||||
"""
|
||||
Create a tensor from various input types.
|
||||
|
||||
TODO: Implement tensor creation with proper data handling.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Convert input data to numpy array using np.array()
|
||||
2. Handle dtype conversion if specified
|
||||
3. Store the numpy array in self._data
|
||||
4. Validate that data is numeric (not strings, objects, etc.)
|
||||
|
||||
EXAMPLE USAGE:
|
||||
```python
|
||||
# From scalar
|
||||
t1 = Tensor(5.0)
|
||||
|
||||
# From list
|
||||
t2 = Tensor([1, 2, 3])
|
||||
|
||||
# From nested list (matrix)
|
||||
t3 = Tensor([[1, 2], [3, 4]])
|
||||
```
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Use np.array(data) to convert input
|
||||
- Check dtype parameter: if provided, use np.array(data, dtype=dtype)
|
||||
- Validate: ensure data is numeric (int, float, complex)
|
||||
- Store in self._data for internal use
|
||||
|
||||
LEARNING CONNECTIONS:
|
||||
- This is like torch.tensor() in PyTorch
|
||||
- Similar to tf.constant() in TensorFlow
|
||||
- Foundation for all other tensor operations
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
if dtype is not None:
|
||||
self._data = np.array(data, dtype=dtype)
|
||||
else:
|
||||
self._data = np.array(data)
|
||||
|
||||
# Validate numeric data
|
||||
if not np.issubdtype(self._data.dtype, np.number):
|
||||
raise ValueError(f"Tensor data must be numeric, got {self._data.dtype}")
|
||||
### END SOLUTION
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Test Your Tensor Creation
|
||||
|
||||
Once you implement the `__init__` method above, run this cell to test it:
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-tensor-creation", "locked": true, "points": 10, "schema_version": 3, "solution": false, "task": false}
|
||||
def test_tensor_creation():
|
||||
"""Test tensor creation with various input types"""
|
||||
print("Testing tensor creation...")
|
||||
|
||||
# Test scalar creation
|
||||
t1 = Tensor(5.0)
|
||||
assert t1._data.shape == (), "Scalar tensor should have empty shape"
|
||||
assert t1._data.item() == 5.0, "Scalar value should be 5.0"
|
||||
|
||||
# Test list creation
|
||||
t2 = Tensor([1, 2, 3])
|
||||
assert t2._data.shape == (3,), "1D tensor should have shape (3,)"
|
||||
assert np.array_equal(t2._data, [1, 2, 3]), "1D tensor values should match"
|
||||
|
||||
# Test matrix creation
|
||||
t3 = Tensor([[1, 2], [3, 4]])
|
||||
assert t3._data.shape == (2, 2), "2D tensor should have shape (2, 2)"
|
||||
assert np.array_equal(t3._data, [[1, 2], [3, 4]]), "2D tensor values should match"
|
||||
|
||||
# Test dtype specification
|
||||
t4 = Tensor([1, 2, 3], dtype='float32')
|
||||
assert t4._data.dtype == np.float32, "Specified dtype should be respected"
|
||||
|
||||
print("✅ Tensor creation tests passed!")
|
||||
print(f"✅ Created tensors: scalar, vector, matrix")
|
||||
print(f"✅ Handled data types correctly")
|
||||
|
||||
# Run the test
|
||||
test_tensor_creation()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 1B: Tensor Properties
|
||||
|
||||
### Essential Information Access
|
||||
Every tensor needs to provide basic information about itself:
|
||||
- **Shape**: Dimensions of the tensor
|
||||
- **Size**: Total number of elements
|
||||
- **Data access**: Get the underlying data
|
||||
|
||||
### Why Properties Matter
|
||||
- **Debugging**: Quickly see tensor dimensions
|
||||
- **Validation**: Check compatibility for operations
|
||||
- **Integration**: Interface with other libraries
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": false, "grade_id": "tensor-properties", "locked": false, "schema_version": 3, "solution": true, "task": false}
|
||||
#| export
|
||||
@property
|
||||
def data(self) -> np.ndarray:
|
||||
"""
|
||||
Get the underlying numpy array data.
|
||||
|
||||
TODO: Implement data property access.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Return self._data directly
|
||||
2. This gives users access to the numpy array
|
||||
|
||||
EXAMPLE USAGE:
|
||||
```python
|
||||
t = Tensor([[1, 2], [3, 4]])
|
||||
print(t.data) # [[1 2]
|
||||
# [3 4]]
|
||||
```
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Simple property: just return self._data
|
||||
- No validation needed here
|
||||
- This is like tensor.numpy() in PyTorch
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
return self._data
|
||||
### END SOLUTION
|
||||
|
||||
@property
|
||||
def shape(self) -> Tuple[int, ...]:
|
||||
"""
|
||||
Get the shape (dimensions) of the tensor.
|
||||
|
||||
TODO: Implement shape property.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Return self._data.shape
|
||||
2. This gives the dimensions as a tuple
|
||||
|
||||
EXAMPLE USAGE:
|
||||
```python
|
||||
t = Tensor([[1, 2], [3, 4]])
|
||||
print(t.shape) # (2, 2)
|
||||
```
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- NumPy arrays have a .shape attribute
|
||||
- Return self._data.shape
|
||||
- This is like tensor.shape in PyTorch
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
return self._data.shape
|
||||
### END SOLUTION
|
||||
|
||||
@property
|
||||
def size(self) -> int:
|
||||
"""
|
||||
Get the total number of elements in the tensor.
|
||||
|
||||
TODO: Implement size property.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Return self._data.size
|
||||
2. This gives total elements across all dimensions
|
||||
|
||||
EXAMPLE USAGE:
|
||||
```python
|
||||
t = Tensor([[1, 2], [3, 4]])
|
||||
print(t.size) # 4 (2×2 = 4 elements)
|
||||
```
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- NumPy arrays have a .size attribute
|
||||
- Return self._data.size
|
||||
- This is like tensor.numel() in PyTorch
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
return self._data.size
|
||||
### END SOLUTION
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Test Your Tensor Properties
|
||||
|
||||
Once you implement the properties above, run this cell to test them:
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-tensor-properties", "locked": true, "points": 10, "schema_version": 3, "solution": false, "task": false}
|
||||
def test_tensor_properties():
|
||||
"""Test tensor properties: data, shape, size"""
|
||||
print("Testing tensor properties...")
|
||||
|
||||
# Test scalar properties
|
||||
t1 = Tensor(5.0)
|
||||
assert t1.shape == (), "Scalar shape should be empty tuple"
|
||||
assert t1.size == 1, "Scalar size should be 1"
|
||||
assert t1.data.item() == 5.0, "Scalar data should be accessible"
|
||||
|
||||
# Test vector properties
|
||||
t2 = Tensor([1, 2, 3, 4])
|
||||
assert t2.shape == (4,), "Vector shape should be (4,)"
|
||||
assert t2.size == 4, "Vector size should be 4"
|
||||
assert np.array_equal(t2.data, [1, 2, 3, 4]), "Vector data should match"
|
||||
|
||||
# Test matrix properties
|
||||
t3 = Tensor([[1, 2, 3], [4, 5, 6]])
|
||||
assert t3.shape == (2, 3), "Matrix shape should be (2, 3)"
|
||||
assert t3.size == 6, "Matrix size should be 6"
|
||||
assert np.array_equal(t3.data, [[1, 2, 3], [4, 5, 6]]), "Matrix data should match"
|
||||
|
||||
print("✅ Tensor properties tests passed!")
|
||||
print(f"✅ Shape, size, and data access working correctly")
|
||||
|
||||
# Run the test
|
||||
test_tensor_properties()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 2: Tensor Arithmetic
|
||||
|
||||
### The Heart of ML: Mathematical Operations
|
||||
Now we implement the core mathematical operations that make ML possible:
|
||||
- **Addition**: Element-wise addition of tensors
|
||||
- **Multiplication**: Element-wise multiplication
|
||||
- **Subtraction**: Element-wise subtraction
|
||||
- **Division**: Element-wise division
|
||||
|
||||
### Why Arithmetic Matters
|
||||
- **Neural networks**: Every layer uses tensor arithmetic
|
||||
- **Optimization**: Gradient updates use arithmetic
|
||||
- **Data processing**: Normalization, scaling, transformations
|
||||
"""
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 2A: Tensor Addition
|
||||
|
||||
### The Foundation Operation
|
||||
Addition is the most basic and important tensor operation:
|
||||
- **Element-wise**: Each element adds to corresponding element
|
||||
- **Broadcasting**: Smaller tensors can add to larger ones
|
||||
- **Commutative**: a + b = b + a
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": false, "grade_id": "tensor-addition", "locked": false, "schema_version": 3, "solution": true, "task": false}
|
||||
#| export
|
||||
def add(self, other: 'Tensor') -> 'Tensor':
|
||||
"""
|
||||
Add two tensors element-wise.
|
||||
|
||||
TODO: Implement tensor addition.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Get the numpy data from both tensors
|
||||
2. Use numpy's + operator for element-wise addition
|
||||
3. Create a new Tensor with the result
|
||||
4. Return the new tensor
|
||||
|
||||
EXAMPLE USAGE:
|
||||
```python
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[5, 6], [7, 8]])
|
||||
result = t1.add(t2)
|
||||
print(result.data) # [[6, 8], [10, 12]]
|
||||
```
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Use self._data + other._data for numpy addition
|
||||
- Wrap result in new Tensor: return Tensor(result)
|
||||
- NumPy handles broadcasting automatically
|
||||
- This is like torch.add() in PyTorch
|
||||
|
||||
LEARNING CONNECTIONS:
|
||||
- This is used in every neural network layer
|
||||
- Gradient updates use addition: params = params + learning_rate * gradients
|
||||
- Data preprocessing: adding bias, normalization
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
result = self._data + other._data
|
||||
return Tensor(result)
|
||||
### END SOLUTION
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Test Your Tensor Addition
|
||||
|
||||
Once you implement the `add` method above, run this cell to test it:
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-tensor-addition", "locked": true, "points": 15, "schema_version": 3, "solution": false, "task": false}
|
||||
def test_tensor_addition():
|
||||
"""Test tensor addition with various shapes"""
|
||||
print("Testing tensor addition...")
|
||||
|
||||
# Test same-shape addition
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[5, 6], [7, 8]])
|
||||
result = t1.add(t2)
|
||||
expected = np.array([[6, 8], [10, 12]])
|
||||
assert np.array_equal(result.data, expected), "Same-shape addition failed"
|
||||
|
||||
# Test scalar addition (broadcasting)
|
||||
t3 = Tensor([[1, 2], [3, 4]])
|
||||
t4 = Tensor(10)
|
||||
result = t3.add(t4)
|
||||
expected = np.array([[11, 12], [13, 14]])
|
||||
assert np.array_equal(result.data, expected), "Scalar addition failed"
|
||||
|
||||
# Test vector addition (broadcasting)
|
||||
t5 = Tensor([[1, 2], [3, 4]])
|
||||
t6 = Tensor([10, 20])
|
||||
result = t5.add(t6)
|
||||
expected = np.array([[11, 22], [13, 24]])
|
||||
assert np.array_equal(result.data, expected), "Vector addition failed"
|
||||
|
||||
print("✅ Tensor addition tests passed!")
|
||||
print(f"✅ Same-shape, scalar, and vector addition working")
|
||||
|
||||
# Run the test
|
||||
test_tensor_addition()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 2B: Tensor Multiplication
|
||||
|
||||
### Scaling and Element-wise Products
|
||||
Multiplication is crucial for scaling values and computing element-wise products:
|
||||
- **Element-wise**: Each element multiplies with corresponding element
|
||||
- **Broadcasting**: Works with different shapes
|
||||
- **Commutative**: a * b = b * a
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": false, "grade_id": "tensor-multiplication", "locked": false, "schema_version": 3, "solution": true, "task": false}
|
||||
#| export
|
||||
def multiply(self, other: 'Tensor') -> 'Tensor':
|
||||
"""
|
||||
Multiply two tensors element-wise.
|
||||
|
||||
TODO: Implement tensor multiplication.
|
||||
|
||||
STEP-BY-STEP IMPLEMENTATION:
|
||||
1. Get the numpy data from both tensors
|
||||
2. Use numpy's * operator for element-wise multiplication
|
||||
3. Create a new Tensor with the result
|
||||
4. Return the new tensor
|
||||
|
||||
EXAMPLE USAGE:
|
||||
```python
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[2, 3], [4, 5]])
|
||||
result = t1.multiply(t2)
|
||||
print(result.data) # [[2, 6], [12, 20]]
|
||||
```
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Use self._data * other._data for numpy multiplication
|
||||
- Wrap result in new Tensor: return Tensor(result)
|
||||
- NumPy handles broadcasting automatically
|
||||
- This is like torch.mul() in PyTorch
|
||||
|
||||
LEARNING CONNECTIONS:
|
||||
- Used in activation functions: ReLU masks
|
||||
- Attention mechanisms: attention weights * values
|
||||
- Scaling: learning_rate * gradients
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
result = self._data * other._data
|
||||
return Tensor(result)
|
||||
### END SOLUTION
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Test Your Tensor Multiplication
|
||||
|
||||
Once you implement the `multiply` method above, run this cell to test it:
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-tensor-multiplication", "locked": true, "points": 15, "schema_version": 3, "solution": false, "task": false}
|
||||
def test_tensor_multiplication():
|
||||
"""Test tensor multiplication with various shapes"""
|
||||
print("Testing tensor multiplication...")
|
||||
|
||||
# Test same-shape multiplication
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[2, 3], [4, 5]])
|
||||
result = t1.multiply(t2)
|
||||
expected = np.array([[2, 6], [12, 20]])
|
||||
assert np.array_equal(result.data, expected), "Same-shape multiplication failed"
|
||||
|
||||
# Test scalar multiplication (broadcasting)
|
||||
t3 = Tensor([[1, 2], [3, 4]])
|
||||
t4 = Tensor(2)
|
||||
result = t3.multiply(t4)
|
||||
expected = np.array([[2, 4], [6, 8]])
|
||||
assert np.array_equal(result.data, expected), "Scalar multiplication failed"
|
||||
|
||||
# Test vector multiplication (broadcasting)
|
||||
t5 = Tensor([[1, 2], [3, 4]])
|
||||
t6 = Tensor([2, 3])
|
||||
result = t5.multiply(t6)
|
||||
expected = np.array([[2, 6], [6, 12]])
|
||||
assert np.array_equal(result.data, expected), "Vector multiplication failed"
|
||||
|
||||
print("✅ Tensor multiplication tests passed!")
|
||||
print(f"✅ Same-shape, scalar, and vector multiplication working")
|
||||
|
||||
# Run the test
|
||||
test_tensor_multiplication()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## 🎯 Step 3: Integration Test
|
||||
|
||||
### Putting It All Together
|
||||
Now let's test that all our tensor operations work together in realistic scenarios:
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-tensor-integration", "locked": true, "points": 20, "schema_version": 3, "solution": false, "task": false}
|
||||
def test_tensor_integration():
|
||||
"""Test complete tensor functionality together"""
|
||||
print("Testing tensor integration...")
|
||||
|
||||
# Create test tensors
|
||||
t1 = Tensor([[1, 2], [3, 4]])
|
||||
t2 = Tensor([[2, 1], [1, 2]])
|
||||
scalar = Tensor(0.5)
|
||||
|
||||
# Test chained operations
|
||||
result = t1.add(t2).multiply(scalar)
|
||||
expected = np.array([[1.5, 1.5], [2.0, 3.0]])
|
||||
assert np.array_equal(result.data, expected), "Chained operations failed"
|
||||
|
||||
# Test properties after operations
|
||||
assert result.shape == (2, 2), "Result shape should be (2, 2)"
|
||||
assert result.size == 4, "Result size should be 4"
|
||||
|
||||
# Test with different shapes (broadcasting)
|
||||
t3 = Tensor([1, 2, 3])
|
||||
t4 = Tensor([[1], [2], [3]])
|
||||
result = t3.add(t4)
|
||||
assert result.shape == (3, 3), "Broadcasting result should be (3, 3)"
|
||||
|
||||
print("✅ Tensor integration tests passed!")
|
||||
print(f"✅ All tensor operations work together correctly")
|
||||
print(f"✅ Ready to build neural networks!")
|
||||
|
||||
# Run the integration test
|
||||
test_tensor_integration()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## 🎯 Module Summary: Tensor Mastery Achieved!
|
||||
|
||||
Congratulations! You've successfully implemented the core Tensor class with:
|
||||
|
||||
### ✅ What You've Built
|
||||
- **Tensor Creation**: Handle various input types (scalars, lists, arrays)
|
||||
- **Properties**: Access shape, size, and data efficiently
|
||||
- **Arithmetic**: Add and multiply tensors with broadcasting support
|
||||
- **Integration**: Operations work together seamlessly
|
||||
|
||||
### ✅ Key Learning Outcomes
|
||||
- **Understanding**: Tensors as the foundation of ML systems
|
||||
- **Implementation**: Built tensor operations from scratch
|
||||
- **Testing**: Comprehensive validation at each step
|
||||
- **Integration**: Chained operations for complex computations
|
||||
|
||||
### ✅ Ready for Next Steps
|
||||
Your tensor implementation is now ready to power:
|
||||
- **Activations**: ReLU, Sigmoid, Tanh will operate on your tensors
|
||||
- **Layers**: Dense layers will use tensor arithmetic
|
||||
- **Networks**: Complete neural networks built on your foundation
|
||||
|
||||
**Next Module**: Activations - Adding nonlinearity to enable complex learning!
|
||||
"""
|
||||
```
|
||||
|
||||
## Key Improvements Demonstrated
|
||||
|
||||
### 1. **Progressive Structure**
|
||||
- Each concept is explained, implemented, and tested before moving on
|
||||
- Students get immediate feedback after each step
|
||||
- No overwhelming amount of code without validation
|
||||
|
||||
### 2. **Rich Scaffolding**
|
||||
- Every TODO has step-by-step implementation guidance
|
||||
- Example usage shows exactly what the function should do
|
||||
- Implementation hints provide specific technical guidance
|
||||
- Learning connections show how concepts fit together
|
||||
|
||||
### 3. **Immediate Testing**
|
||||
- Each function is tested immediately after implementation
|
||||
- Tests provide clear success messages and specific achievements
|
||||
- Integration tests show how concepts work together
|
||||
|
||||
### 4. **Educational Flow**
|
||||
- Concepts build logically from simple to complex
|
||||
- Real-world motivation before technical implementation
|
||||
- Visual examples and concrete cases before abstract theory
|
||||
|
||||
## Implementation Steps for Other Modules
|
||||
|
||||
1. **Identify natural breakpoints** in the current module
|
||||
2. **Reorganize** into Step 1, Step 2, etc. with explanations
|
||||
3. **Add rich TODO blocks** with step-by-step guidance
|
||||
4. **Insert immediate testing** after each major concept
|
||||
5. **Add success messages** and progress indicators
|
||||
6. **Include learning connections** between concepts
|
||||
|
||||
This transformation turns modules from reference material into guided learning experiences that maximize student success through immediate feedback and clear progression.
|
||||
@@ -1,18 +0,0 @@
|
||||
# VS Code Jupytext Cell Visual Cues
|
||||
|
||||
Simple approach using built-in visual indicators to distinguish markdown from code cells.
|
||||
|
||||
## Visual Cues:
|
||||
- **Markdown cells**: `# %% [markdown]` + triple quotes (`"""`)
|
||||
- **Code cells**: `# %%` + regular Python syntax
|
||||
|
||||
## What you see:
|
||||
- **Cell markers** clearly indicate cell type
|
||||
- **Triple quotes** naturally stand out with string highlighting
|
||||
- **Syntax highlighting** makes content type obvious
|
||||
|
||||
## Files:
|
||||
- `.vscode/settings.json` - Basic Python project settings
|
||||
- `.vscode/extensions.json` - Recommends Python extension
|
||||
|
||||
Clean, simple, and works reliably across all VS Code themes!
|
||||
@@ -1,334 +0,0 @@
|
||||
# 🗜️ Module 10: Compression & Optimization - Design Document
|
||||
|
||||
## 📊 Current Foundation Analysis
|
||||
|
||||
### ✅ What Students Already Know (Modules 00-09)
|
||||
- **Dense Layers**: Weight matrices, bias vectors, Xavier initialization
|
||||
- **CNN Layers**: 2D kernels, spatial processing, parameter sharing
|
||||
- **Model Architecture**: Sequential composition, MLPs, CNNs
|
||||
- **Training Pipeline**: Loss functions, optimizers, metrics, complete workflows
|
||||
- **Data Handling**: Batch processing, DataLoader, real datasets
|
||||
- **Parameter Understanding**: Shapes, initialization strategies, learned parameters
|
||||
|
||||
### 🎯 Compression Opportunities Identified
|
||||
|
||||
#### **1. Dense Layer Parameters**
|
||||
- Weight matrices: `(input_size, output_size)` - often largest component
|
||||
- Bias vectors: `(output_size,)` - smaller but present in every layer
|
||||
- **Compression potential**: High - dense layers are parameter-heavy
|
||||
|
||||
#### **2. CNN Parameters**
|
||||
- Kernels: `(kernel_height, kernel_width)` - repeated across channels/filters
|
||||
- **Compression potential**: Moderate - already parameter-efficient through sharing
|
||||
|
||||
#### **3. Model Architectures**
|
||||
- Sequential networks: Multiple layers with growing/shrinking dimensions
|
||||
- **Compression potential**: High - architectural optimization can dramatically reduce size
|
||||
|
||||
## 🎓 Educational Compression Techniques (Ranked by Learning Value)
|
||||
|
||||
### **Priority 1: Magnitude-Based Pruning** ⭐⭐⭐⭐⭐
|
||||
**Why this first:** Builds directly on weight matrices students understand
|
||||
|
||||
#### **Learning Objectives:**
|
||||
- Understand that not all parameters contribute equally to model performance
|
||||
- Learn to identify and remove less important weights
|
||||
- See the trade-off between model size and accuracy
|
||||
- Experience sparsity in neural networks
|
||||
|
||||
#### **Technical Implementation:**
|
||||
```python
|
||||
# Students will implement:
|
||||
def prune_weights_by_magnitude(layer, pruning_ratio=0.5):
|
||||
"""Remove smallest weights from Dense layer."""
|
||||
weights = layer.weights.data
|
||||
threshold = np.percentile(np.abs(weights), pruning_ratio * 100)
|
||||
mask = np.abs(weights) > threshold
|
||||
layer.weights.data = weights * mask
|
||||
return layer
|
||||
|
||||
# Usage example:
|
||||
dense_layer = Dense(784, 128)
|
||||
compressed_layer = prune_weights_by_magnitude(dense_layer, pruning_ratio=0.3)
|
||||
```
|
||||
|
||||
#### **Educational Value:**
|
||||
- **Immediate**: See weight matrices become sparse
|
||||
- **Visual**: Plot weight distributions before/after pruning
|
||||
- **Practical**: Measure model size reduction and accuracy impact
|
||||
- **Conceptual**: Understand parameter importance and redundancy
|
||||
|
||||
---
|
||||
|
||||
### **Priority 2: Quantization (FP32 → INT8)** ⭐⭐⭐⭐
|
||||
**Why second:** Builds on tensor operations students understand
|
||||
|
||||
#### **Learning Objectives:**
|
||||
- Understand numerical precision trade-offs in ML
|
||||
- Learn how reducing bits per parameter saves memory
|
||||
- Experience the accuracy vs efficiency spectrum
|
||||
- Connect to real mobile/edge deployment constraints
|
||||
|
||||
#### **Technical Implementation:**
|
||||
```python
|
||||
# Students will implement:
|
||||
def quantize_layer_weights(layer, bits=8):
|
||||
"""Quantize layer weights to lower precision."""
|
||||
weights = layer.weights.data
|
||||
|
||||
# Find min/max for quantization range
|
||||
w_min, w_max = weights.min(), weights.max()
|
||||
|
||||
# Quantize to bits precision
|
||||
scale = (w_max - w_min) / (2**bits - 1)
|
||||
quantized = np.round((weights - w_min) / scale)
|
||||
|
||||
# Convert back to float (simulation of quantized weights)
|
||||
dequantized = quantized * scale + w_min
|
||||
|
||||
layer.weights.data = dequantized.astype(np.float32)
|
||||
return layer, scale, w_min
|
||||
|
||||
# Usage example:
|
||||
layer = Dense(100, 50)
|
||||
q_layer, scale, offset = quantize_layer_weights(layer, bits=8)
|
||||
```
|
||||
|
||||
#### **Educational Value:**
|
||||
- **Mathematical**: Understand linear quantization mapping
|
||||
- **Practical**: See dramatic memory reduction (75% for FP32→INT8)
|
||||
- **Performance**: Measure accuracy degradation vs compression
|
||||
- **Real-world**: Connect to mobile AI and edge deployment
|
||||
|
||||
---
|
||||
|
||||
### **Priority 3: Knowledge Distillation** ⭐⭐⭐⭐
|
||||
**Why third:** Builds on training pipeline students just mastered
|
||||
|
||||
#### **Learning Objectives:**
|
||||
- Learn how large models can teach small models
|
||||
- Understand soft targets vs hard targets
|
||||
- Experience training dynamics with teacher guidance
|
||||
- See how knowledge can be compressed across architectures
|
||||
|
||||
#### **Technical Implementation:**
|
||||
```python
|
||||
# Students will implement:
|
||||
class DistillationLoss:
|
||||
"""Combined loss for knowledge distillation."""
|
||||
def __init__(self, temperature=3.0, alpha=0.5):
|
||||
self.temperature = temperature
|
||||
self.alpha = alpha
|
||||
self.ce_loss = CrossEntropyLoss()
|
||||
|
||||
def __call__(self, student_logits, teacher_logits, true_labels):
|
||||
# Hard loss (standard classification)
|
||||
hard_loss = self.ce_loss(student_logits, true_labels)
|
||||
|
||||
# Soft loss (distillation from teacher)
|
||||
soft_targets = softmax(teacher_logits / self.temperature)
|
||||
soft_student = softmax(student_logits / self.temperature)
|
||||
soft_loss = -np.sum(soft_targets * np.log(soft_student + 1e-10))
|
||||
|
||||
# Combined loss
|
||||
return self.alpha * hard_loss + (1 - self.alpha) * soft_loss
|
||||
|
||||
# Usage example:
|
||||
teacher = create_mlp(784, [512, 256, 128], 10) # Large model
|
||||
student = create_mlp(784, [64, 32], 10) # Small model
|
||||
|
||||
distill_loss = DistillationLoss(temperature=3.0)
|
||||
trainer = Trainer(student, optimizer, distill_loss)
|
||||
```
|
||||
|
||||
#### **Educational Value:**
|
||||
- **Advanced Training**: Beyond standard supervised learning
|
||||
- **Architecture Flexibility**: Different sized models with same task
|
||||
- **Loss Design**: Custom loss functions for specific objectives
|
||||
- **Transfer Learning**: Knowledge transfer between models
|
||||
|
||||
---
|
||||
|
||||
### **Priority 4: Structured Pruning (Layer Width Reduction)** ⭐⭐⭐
|
||||
**Why fourth:** Builds on architecture design understanding
|
||||
|
||||
#### **Learning Objectives:**
|
||||
- Understand structured vs unstructured sparsity
|
||||
- Learn to remove entire neurons/channels systematically
|
||||
- See how architecture changes affect model behavior
|
||||
- Experience automated neural architecture search concepts
|
||||
|
||||
#### **Technical Implementation:**
|
||||
```python
|
||||
# Students will implement:
|
||||
def prune_layer_neurons(layer, importance_scores, keep_ratio=0.7):
|
||||
"""Remove least important neurons from Dense layer."""
|
||||
output_size = layer.output_size
|
||||
keep_count = int(output_size * keep_ratio)
|
||||
|
||||
# Select most important neurons
|
||||
top_indices = np.argsort(importance_scores)[-keep_count:]
|
||||
|
||||
# Prune weights and bias
|
||||
layer.weights.data = layer.weights.data[:, top_indices]
|
||||
if layer.bias is not None:
|
||||
layer.bias.data = layer.bias.data[top_indices]
|
||||
|
||||
layer.output_size = keep_count
|
||||
return layer
|
||||
|
||||
def compute_neuron_importance(layer, data_loader):
|
||||
"""Compute importance scores for each neuron."""
|
||||
# Students implement activation-based importance
|
||||
pass
|
||||
|
||||
# Usage example:
|
||||
importance = compute_neuron_importance(layer, train_loader)
|
||||
compressed_layer = prune_layer_neurons(layer, importance, keep_ratio=0.6)
|
||||
```
|
||||
|
||||
#### **Educational Value:**
|
||||
- **System Architecture**: Modifying network structure itself
|
||||
- **Importance Metrics**: Different ways to measure neuron contributions
|
||||
- **Cascade Effects**: How pruning one layer affects next layers
|
||||
- **AutoML Connection**: Automated architecture optimization
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Module Structure (Educational Progression)
|
||||
|
||||
### **Step 1: Understanding Model Size and Parameters**
|
||||
- Count parameters in Dense and CNN layers
|
||||
- Visualize parameter distributions
|
||||
- Measure memory footprint of different architectures
|
||||
- **Build Foundation**: "What makes models large?"
|
||||
|
||||
### **Step 2: Magnitude-Based Pruning**
|
||||
- Implement weight pruning with different thresholds
|
||||
- Visualize sparse weight matrices
|
||||
- Measure accuracy vs sparsity trade-offs
|
||||
- **Core Technique**: "Remove unimportant weights"
|
||||
|
||||
### **Step 3: Quantization Experiments**
|
||||
- Implement FP32 → INT8 quantization
|
||||
- Measure memory savings and accuracy impact
|
||||
- Explore different bit widths (16-bit, 8-bit, 4-bit)
|
||||
- **Efficiency Focus**: "Use fewer bits per parameter"
|
||||
|
||||
### **Step 4: Knowledge Distillation**
|
||||
- Train teacher model on full dataset
|
||||
- Implement distillation loss function
|
||||
- Train student model with teacher guidance
|
||||
- **Advanced Training**: "Large models teach small models"
|
||||
|
||||
### **Step 5: Structured Pruning**
|
||||
- Implement neuron importance computation
|
||||
- Remove entire neurons/channels
|
||||
- Handle cascade effects on subsequent layers
|
||||
- **Architecture Optimization**: "Modify network structure"
|
||||
|
||||
### **Step 6: Comprehensive Comparison**
|
||||
- Apply all techniques to same base model
|
||||
- Create compression vs accuracy plots
|
||||
- Benchmark inference speed improvements
|
||||
- **Systems Integration**: "Combine techniques for maximum effect"
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Implementation Strategy
|
||||
|
||||
### **Building on Existing Components**
|
||||
- **Dense layers**: Primary target for compression techniques
|
||||
- **Training pipeline**: Framework for measuring accuracy impact
|
||||
- **DataLoader**: Consistent evaluation across compressed models
|
||||
- **Metrics**: Accuracy measurement for compression trade-offs
|
||||
|
||||
### **New Components to Build**
|
||||
1. **CompressionMetrics**: Model size, parameter count, sparsity measurement
|
||||
2. **PruningUtils**: Weight analysis, threshold selection, mask application
|
||||
3. **QuantizationUtils**: Bit-width conversion, scale/offset computation
|
||||
4. **DistillationTrainer**: Extended trainer for teacher-student training
|
||||
5. **ComparisonTools**: Visualization and benchmarking utilities
|
||||
|
||||
### **Educational Testing Framework**
|
||||
- **Before/After Comparisons**: Size, accuracy, speed for each technique
|
||||
- **Visualization Tools**: Weight distributions, sparsity patterns, accuracy curves
|
||||
- **Interactive Exploration**: Students experiment with different compression ratios
|
||||
- **Real-World Context**: Connect to mobile deployment constraints
|
||||
|
||||
---
|
||||
|
||||
## 📚 Real-World Connections
|
||||
|
||||
### **Mobile and Edge AI**
|
||||
- Smartphone apps need small models (< 10MB)
|
||||
- Embedded devices have severe memory constraints
|
||||
- Battery life affected by computation intensity
|
||||
- **Student Understanding**: Why compression matters in practice
|
||||
|
||||
### **Production ML Systems**
|
||||
- Cost optimization in cloud inference
|
||||
- Latency requirements for real-time applications
|
||||
- Memory bandwidth limitations in data centers
|
||||
- **Career Relevance**: Skills needed for production deployment
|
||||
|
||||
### **Research Frontiers**
|
||||
- Neural architecture search (NAS)
|
||||
- Hardware-aware model design
|
||||
- Automatic compression techniques
|
||||
- **Advanced Topics**: Connection to cutting-edge research
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Success Metrics
|
||||
|
||||
### **Educational Outcomes**
|
||||
- Students understand parameter importance and redundancy
|
||||
- Students can trade model size for accuracy systematically
|
||||
- Students connect compression to real deployment constraints
|
||||
- Students gain intuition for when different techniques work best
|
||||
|
||||
### **Technical Skills**
|
||||
- Implement 4 different compression techniques from scratch
|
||||
- Measure and visualize compression trade-offs
|
||||
- Modify existing models for better efficiency
|
||||
- Design compression strategies for specific constraints
|
||||
|
||||
### **Real-World Preparation**
|
||||
- Understanding of mobile AI constraints
|
||||
- Experience with production optimization techniques
|
||||
- Knowledge of compression research landscape
|
||||
- Skills for model deployment and optimization roles
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Why This Module Design Works
|
||||
|
||||
### **Perfect Timing**
|
||||
- Students just mastered training (Module 9)
|
||||
- Natural next step: optimize trained models
|
||||
- Builds on solid foundation of layers, networks, training
|
||||
|
||||
### **Hands-On Learning**
|
||||
- Every technique implemented from scratch
|
||||
- Immediate visual feedback on compression effects
|
||||
- Real data and models, not toy examples
|
||||
|
||||
### **Progressive Complexity**
|
||||
- Start simple (magnitude pruning)
|
||||
- Build to advanced (knowledge distillation)
|
||||
- Integrate all techniques for maximum learning
|
||||
|
||||
### **Career Relevant**
|
||||
- Essential skills for production ML roles
|
||||
- Understanding of efficiency constraints in real systems
|
||||
- Foundation for research in model optimization
|
||||
|
||||
### **Foundation for Later Modules**
|
||||
- Benchmarking skills prepare for Module 12
|
||||
- Performance optimization mindset prepares for Module 11
|
||||
- Production awareness prepares for MLOps Module 13
|
||||
|
||||
---
|
||||
|
||||
This compression module design builds perfectly on students' current knowledge while introducing essential production ML skills. Students will gain practical experience with the efficiency techniques that make modern AI deployment possible!
|
||||
@@ -1,134 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 00_setup</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>00_setup</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-C"><h3>C</h3><p>Scaffolding</p></div><div class="grade-box grade-A"><h3>A</h3><p>Complexity</p></div><div class="grade-box grade-D"><h3>D</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 608</p>
|
||||
<p><strong>Total Cells:</strong> 9</p>
|
||||
<p><strong>Avg Cell Length:</strong> 53.4 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 3/5</p>
|
||||
<p><strong>Learning Progression:</strong> 4/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 37</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (608 lines, target: 200-400)</p><p>❌ Too long (53.4 avg, target: ≤30)</p><p>✅ Good (0.0% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>4 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Split 7 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 46 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 0, Collaboration, 1. **System Awareness**</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (46 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 3: Practical Connection</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 33 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> PyTorch, Configuration Layers in Production ML, TensorFlow</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (33 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 4: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 42 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 2. **Debugging**, Collaboration, System Information</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (42 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 77 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Unique identification, Development tracking, System Customization</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (77 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 6: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 123 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Resource Planning, `sys.version_info` - Python Version, Bug reproduction</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (123 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 7: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 28 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Collaboration, Consistent behavior, Reliability</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 56 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Test personal information configuration, 🧪 Test Your Configuration Functions, Test email format</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (56 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 67 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Resource planning, Summary, Verify package integration</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (67 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,286 +0,0 @@
|
||||
{
|
||||
"module_name": "00_setup",
|
||||
"module_path": "modules/source/00_setup",
|
||||
"analysis_date": "2025-07-13T09:20:37.636982",
|
||||
"total_lines": 608,
|
||||
"total_cells": 9,
|
||||
"avg_cell_length": 53.44444444444444,
|
||||
"scaffolding_quality": 3,
|
||||
"complexity_distribution": {
|
||||
"1": 1,
|
||||
"2": 2,
|
||||
"3": 6,
|
||||
"4": 0,
|
||||
"5": 0
|
||||
},
|
||||
"learning_progression_quality": 4,
|
||||
"concepts_covered": [
|
||||
"0",
|
||||
"2. **Debugging**",
|
||||
"Resource Planning",
|
||||
"`sys.version_info` - Python Version",
|
||||
"Bug reproduction",
|
||||
"Collaboration",
|
||||
"Professional",
|
||||
"System Information",
|
||||
"1. **System Awareness**",
|
||||
"Debugging support",
|
||||
"Why Each Layer Matters",
|
||||
"Someone else can recreate your environment",
|
||||
"Unique identification",
|
||||
"Development tracking",
|
||||
"System Customization",
|
||||
"Descriptive system name",
|
||||
"Model cards",
|
||||
"Maintenance",
|
||||
"Real-World Parallels",
|
||||
"Platform",
|
||||
"`psutil.cpu_count()` - CPU Cores",
|
||||
"1. **Reliability**",
|
||||
"Documentation",
|
||||
"Verify package integration",
|
||||
"Test personal information configuration",
|
||||
"TensorFlow",
|
||||
"Configuration Layers in Production ML",
|
||||
"Test return type",
|
||||
"Module 3 (Layers)",
|
||||
"\ud83e\uddea Test Your Configuration Functions",
|
||||
"Consistent behavior",
|
||||
"Test email format",
|
||||
"Test system information queries",
|
||||
"Resource planning",
|
||||
"PyTorch",
|
||||
"Reliability",
|
||||
"Summary"
|
||||
],
|
||||
"todo_count": 2,
|
||||
"hint_count": 2,
|
||||
"test_count": 0,
|
||||
"critical_issues": [
|
||||
"4 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Long cell (46 lines)",
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 3: Long cell (33 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Long cell (42 lines)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Very long cell (77 lines)",
|
||||
"Cell 5: Complex implementation without error handling guidance",
|
||||
"Cell 6: Very long cell (123 lines)",
|
||||
"Cell 6: Complex implementation without error handling guidance",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 8: Very long cell (56 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 9: Very long cell (67 lines)",
|
||||
"Cell 9: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Split 7 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 46,
|
||||
"char_count": 2400,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"0",
|
||||
"Collaboration",
|
||||
"1. **System Awareness**",
|
||||
"Debugging support",
|
||||
"Maintenance"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (46 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 33,
|
||||
"char_count": 1530,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "practical_connection",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"PyTorch",
|
||||
"Configuration Layers in Production ML",
|
||||
"TensorFlow",
|
||||
"Professional",
|
||||
"Why Each Layer Matters"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (33 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 42,
|
||||
"char_count": 1816,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"2. **Debugging**",
|
||||
"Collaboration",
|
||||
"System Information",
|
||||
"Someone else can recreate your environment",
|
||||
"Model cards"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (42 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 77,
|
||||
"char_count": 3670,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Unique identification",
|
||||
"Development tracking",
|
||||
"System Customization",
|
||||
"Descriptive system name",
|
||||
"Real-World Parallels"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (77 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 123,
|
||||
"char_count": 5380,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Resource Planning",
|
||||
"`sys.version_info` - Python Version",
|
||||
"Bug reproduction",
|
||||
"Platform",
|
||||
"`psutil.cpu_count()` - CPU Cores"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (123 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 28,
|
||||
"char_count": 1439,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Collaboration",
|
||||
"Consistent behavior",
|
||||
"Reliability",
|
||||
"1. **Reliability**",
|
||||
"Documentation"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 56,
|
||||
"char_count": 3113,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Test personal information configuration",
|
||||
"\ud83e\uddea Test Your Configuration Functions",
|
||||
"Test email format",
|
||||
"Test system information queries",
|
||||
"Test return type"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (56 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 67,
|
||||
"char_count": 3761,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Resource planning",
|
||||
"Summary",
|
||||
"Verify package integration",
|
||||
"Collaboration",
|
||||
"Module 3 (Layers)"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (67 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "C",
|
||||
"Complexity": "A",
|
||||
"Cell_Length": "D"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (608 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (53.4 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (0.0% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 2: Too long (46 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 3: Too long (33 lines)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 4: Too long (42 lines)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (77 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (123 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (56 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (67 lines)"
|
||||
]
|
||||
}
|
||||
@@ -1,214 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 01_tensor</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>01_tensor</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-D"><h3>D</h3><p>Scaffolding</p></div><div class="grade-box grade-A"><h3>A</h3><p>Complexity</p></div><div class="grade-box grade-D"><h3>D</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1536</p>
|
||||
<p><strong>Total Cells:</strong> 19</p>
|
||||
<p><strong>Avg Cell Length:</strong> 64.1 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 2/5</p>
|
||||
<p><strong>Learning Progression:</strong> 1/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 62</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1536 lines, target: 200-400)</p><p>❌ Too long (64.1 avg, target: ≤30)</p><p>✅ Good (10.5% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1536 lines) - students will be overwhelmed</li><li>Sudden complexity jumps will overwhelm students</li><li>7 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Add implementation ladders: break complex functions into 3 progressive steps</li><li>Add concept bridges: connect new ideas to familiar concepts</li><li>Include confidence builders: early wins to build momentum</li><li>Split 10 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 2: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 25 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Use, Module 1: Tensor - Core Data Structure, Build</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 16 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Foundation:, Production:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 4: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 118 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Process entire batch at once:, Batch of sentences, Batch operations</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (118 lines), High complexity without guidance, Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 5: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 27 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Batch Processing, Performance Considerations, PyTorch</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 6: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 299 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> API design, Informative messages, dtype</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (299 lines), Too many concepts (5), Multiple functions in one cell (12), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 7: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 31 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> This is a unit test, Progress, 🧪 Unit Test: Tensor Creation</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (31 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 33 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> shape, This is a unit test, size</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (33 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 40 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Unit Test: Tensor Arithmetic, Test basic arithmetic with simple examples, This is a unit test</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (40 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 125 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Comprehensive Test: Tensor Creation, 7, 2</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (125 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 11: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 123 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Comprehensive Test: Tensor Properties, 2, Progress</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (123 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 12: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 141 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 7, 2, Progress</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (141 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 13: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 83 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Final Integration Test: Real ML Scenario, Run the integration test, Print final summary</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (83 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 14: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 14 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Activation functions, Broadcasting, Forward pass</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 15: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 4 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Step 3: Tensor Arithmetic Methods, 3</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 16: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 10 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Step 4: Python Operator Overloading, 4, Why Operator Overloading?</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 17: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 4 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> 4, Step 4: Operator Overloading</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 18: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 88 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Test tensor creation and properties, Note, Test tensor arithmetic operations</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (88 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 19: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 28 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Operator overloading, NumPy backend, What You've Accomplished</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,516 +0,0 @@
|
||||
{
|
||||
"module_name": "01_tensor",
|
||||
"module_path": "modules/source/01_tensor",
|
||||
"analysis_date": "2025-07-13T09:20:37.657006",
|
||||
"total_lines": 1536,
|
||||
"total_cells": 19,
|
||||
"avg_cell_length": 64.10526315789474,
|
||||
"scaffolding_quality": 2,
|
||||
"complexity_distribution": {
|
||||
"1": 6,
|
||||
"2": 3,
|
||||
"3": 8,
|
||||
"4": 2,
|
||||
"5": 0
|
||||
},
|
||||
"learning_progression_quality": 1,
|
||||
"concepts_covered": [
|
||||
"API design",
|
||||
"Operator overloading",
|
||||
"Performance Considerations",
|
||||
"Process entire batch at once:",
|
||||
"Batch of sentences",
|
||||
"Batch operations",
|
||||
"Informative messages",
|
||||
"This is a unit test",
|
||||
"dtype",
|
||||
"size",
|
||||
"**4. Automatic Differentiation**",
|
||||
"**4. Extensibility**",
|
||||
"Data access",
|
||||
"Error handling",
|
||||
"Module 1: Tensor - Core Data Structure",
|
||||
"Forward pass",
|
||||
"Step 4: Python Operator Overloading",
|
||||
"NumPy backend",
|
||||
"What You've Accomplished",
|
||||
"\ud83e\uddea Unit Test: Tensor Creation",
|
||||
"Next Steps",
|
||||
"Test tensor creation and properties",
|
||||
"Foundation:",
|
||||
"TensorFlow",
|
||||
"Production:",
|
||||
"Broadcasting",
|
||||
"\ud83e\uddea Comprehensive Test: Tensor Creation",
|
||||
"\ud83e\uddea Test Your Tensor Implementation",
|
||||
"Step 4: Operator Overloading",
|
||||
"Step 3: Tensor Arithmetic Methods",
|
||||
"7",
|
||||
"Note",
|
||||
"Test",
|
||||
"\ud83e\uddea Unit Test: Tensor Arithmetic",
|
||||
"Batch Processing",
|
||||
"Activation functions",
|
||||
"Loss computation",
|
||||
"Use",
|
||||
"Test properties with simple examples",
|
||||
"Test subtraction",
|
||||
"2",
|
||||
"Progress",
|
||||
"Build",
|
||||
"4",
|
||||
"Learning Side:",
|
||||
"Final package structure:",
|
||||
"PyTorch",
|
||||
"\ud83e\uddea Comprehensive Test: Tensor Properties",
|
||||
"shape",
|
||||
"\ud83e\uddea Final Integration Test: Real ML Scenario",
|
||||
"Addition",
|
||||
"Test tensor arithmetic operations",
|
||||
"Why Operator Overloading?",
|
||||
"Run the integration test",
|
||||
"Run the comprehensive test",
|
||||
"3",
|
||||
"Building Side:",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand",
|
||||
"Print final summary",
|
||||
"Test basic arithmetic with simple examples",
|
||||
"Test basic tensor creation"
|
||||
],
|
||||
"todo_count": 1,
|
||||
"hint_count": 2,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1536 lines) - students will be overwhelmed",
|
||||
"Sudden complexity jumps will overwhelm students",
|
||||
"7 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Very long cell (118 lines)",
|
||||
"Cell 4: High complexity without guidance",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 6: Very long cell (299 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Multiple functions in one cell (12)",
|
||||
"Cell 6: Complex implementation without error handling guidance",
|
||||
"Cell 7: Long cell (31 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 8: Long cell (33 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 9: Long cell (40 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 10: Very long cell (125 lines)",
|
||||
"Cell 11: Very long cell (123 lines)",
|
||||
"Cell 12: Very long cell (141 lines)",
|
||||
"Cell 13: Very long cell (83 lines)",
|
||||
"Cell 14: Too many concepts (5)",
|
||||
"Cell 18: Very long cell (88 lines)",
|
||||
"Cell 18: Too many concepts (5)",
|
||||
"Cell 19: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Add implementation ladders: break complex functions into 3 progressive steps",
|
||||
"Add concept bridges: connect new ideas to familiar concepts",
|
||||
"Include confidence builders: early wins to build momentum",
|
||||
"Split 10 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 25,
|
||||
"char_count": 1323,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Use",
|
||||
"Module 1: Tensor - Core Data Structure",
|
||||
"Build",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 16,
|
||||
"char_count": 687,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Foundation:",
|
||||
"Production:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 118,
|
||||
"char_count": 6203,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Process entire batch at once:",
|
||||
"Batch of sentences",
|
||||
"Batch operations",
|
||||
"**4. Automatic Differentiation**",
|
||||
"**4. Extensibility**"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (118 lines)",
|
||||
"High complexity without guidance",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 27,
|
||||
"char_count": 1028,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Batch Processing",
|
||||
"Performance Considerations",
|
||||
"PyTorch",
|
||||
"TensorFlow",
|
||||
"Broadcasting"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 299,
|
||||
"char_count": 12374,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"API design",
|
||||
"Informative messages",
|
||||
"dtype",
|
||||
"Data access",
|
||||
"Error handling"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (299 lines)",
|
||||
"Too many concepts (5)",
|
||||
"Multiple functions in one cell (12)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 31,
|
||||
"char_count": 1502,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"This is a unit test",
|
||||
"Progress",
|
||||
"\ud83e\uddea Unit Test: Tensor Creation",
|
||||
"Test",
|
||||
"Test basic tensor creation"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (31 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 33,
|
||||
"char_count": 1637,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"shape",
|
||||
"This is a unit test",
|
||||
"size",
|
||||
"Test properties with simple examples",
|
||||
"dtype"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (33 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 40,
|
||||
"char_count": 1988,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Unit Test: Tensor Arithmetic",
|
||||
"Test basic arithmetic with simple examples",
|
||||
"This is a unit test",
|
||||
"Progress",
|
||||
"Test"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (40 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 125,
|
||||
"char_count": 7068,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Comprehensive Test: Tensor Creation",
|
||||
"7",
|
||||
"2",
|
||||
"Progress",
|
||||
"Run the comprehensive test"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (125 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 123,
|
||||
"char_count": 6570,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Comprehensive Test: Tensor Properties",
|
||||
"2",
|
||||
"Progress",
|
||||
"Run the comprehensive test",
|
||||
"3"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (123 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 141,
|
||||
"char_count": 6841,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"7",
|
||||
"2",
|
||||
"Progress",
|
||||
"Run the comprehensive test",
|
||||
"3"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (141 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 83,
|
||||
"char_count": 4430,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Final Integration Test: Real ML Scenario",
|
||||
"Run the integration test",
|
||||
"Print final summary",
|
||||
"Progress",
|
||||
"Note"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (83 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 14,
|
||||
"char_count": 607,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Activation functions",
|
||||
"Broadcasting",
|
||||
"Forward pass",
|
||||
"Loss computation",
|
||||
"Addition"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 4,
|
||||
"char_count": 127,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Step 3: Tensor Arithmetic Methods",
|
||||
"3"
|
||||
],
|
||||
"overwhelm_factors": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 10,
|
||||
"char_count": 309,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Step 4: Python Operator Overloading",
|
||||
"4",
|
||||
"Why Operator Overloading?"
|
||||
],
|
||||
"overwhelm_factors": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 4,
|
||||
"char_count": 198,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"4",
|
||||
"Step 4: Operator Overloading"
|
||||
],
|
||||
"overwhelm_factors": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 88,
|
||||
"char_count": 4339,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Test tensor creation and properties",
|
||||
"Note",
|
||||
"Test tensor arithmetic operations",
|
||||
"\ud83e\uddea Test Your Tensor Implementation",
|
||||
"Test subtraction"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (88 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 28,
|
||||
"char_count": 1404,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Operator overloading",
|
||||
"NumPy backend",
|
||||
"What You've Accomplished",
|
||||
"Next Steps",
|
||||
"Broadcasting"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "D",
|
||||
"Complexity": "A",
|
||||
"Cell_Length": "D"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1536 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (64.1 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (10.5% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 4: Too long (118 lines)",
|
||||
"Cell 4: High complexity without guidance",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (299 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 7: Too long (31 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (33 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (40 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Too long (125 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (123 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (141 lines)",
|
||||
"Cell 13: Too many concepts (5)",
|
||||
"Cell 13: Too long (83 lines)",
|
||||
"Cell 14: Too many concepts (5)",
|
||||
"Cell 18: Too many concepts (5)",
|
||||
"Cell 18: Too long (88 lines)",
|
||||
"Cell 19: Too many concepts (5)",
|
||||
"Cells 3-4: Complexity cliff (1\u21924)"
|
||||
]
|
||||
}
|
||||
@@ -1,198 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 02_activations</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>02_activations</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-C"><h3>C</h3><p>Scaffolding</p></div><div class="grade-box grade-B"><h3>B</h3><p>Complexity</p></div><div class="grade-box grade-D"><h3>D</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1589</p>
|
||||
<p><strong>Total Cells:</strong> 17</p>
|
||||
<p><strong>Avg Cell Length:</strong> 73.1 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 3/5</p>
|
||||
<p><strong>Learning Progression:</strong> 4/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 65</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1589 lines, target: 200-400)</p><p>❌ Too long (73.1 avg, target: ≤30)</p><p>✅ Good (23.5% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1589 lines) - students will be overwhelmed</li><li>9 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Split 13 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 86 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Use, functions, 2</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (86 lines), Multiple functions in one cell (3)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 16 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Production:, Integration:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 4: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 25 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> PyTorch, TensorFlow, 🧠 The Mathematical Foundation of Nonlinearity</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 155 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Advantages, Reality, Use case</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (155 lines), High complexity without guidance, Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 6: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 64 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> In Plain English:, What is ReLU?, Deep networks</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (64 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 7: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 34 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show visual example, This is a unit test, Test with mixed positive/negative values</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (34 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 8: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 69 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> What is Sigmoid?, When to Use Sigmoid, Monotonic</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (69 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 39 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show visual example, This is a unit test, Create Sigmoid instance</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (39 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 56 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> balanced scale, Tanh (Hyperbolic Tangent), What is Tanh?</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (56 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 11: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 39 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show visual example, This is a unit test, Test with various inputs</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (39 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 12: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 64 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> x, Interpretable, Mathematical Definition:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (64 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 13: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 43 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show visual example, This is a unit test, 🧪 Unit Test: Softmax Activation</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (43 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 14: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 126 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Test with multiple values, Check that all outputs are positive, Test Tanh activation</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (126 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 15: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 276 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Tanh, Testing, 7</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (276 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-5">
|
||||
<h4>Cell 16: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 112 |
|
||||
<strong>Complexity:</strong> 5/5</p>
|
||||
<p><strong>Concepts:</strong> Sigmoid, Tanh, Alternative</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (112 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 17: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 30 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Nonlinearity, 🎯 Module Summary, Numerical stability</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,501 +0,0 @@
|
||||
{
|
||||
"module_name": "02_activations",
|
||||
"module_path": "modules/source/02_activations",
|
||||
"analysis_date": "2025-07-13T09:20:37.679664",
|
||||
"total_lines": 1589,
|
||||
"total_cells": 17,
|
||||
"avg_cell_length": 73.11764705882354,
|
||||
"scaffolding_quality": 3,
|
||||
"complexity_distribution": {
|
||||
"1": 2,
|
||||
"2": 1,
|
||||
"3": 10,
|
||||
"4": 3,
|
||||
"5": 1
|
||||
},
|
||||
"learning_progression_quality": 4,
|
||||
"concepts_covered": [
|
||||
"Test with multiple values",
|
||||
"balanced scale",
|
||||
"Advantages",
|
||||
"What is Sigmoid?",
|
||||
"Reality",
|
||||
"This is a unit test",
|
||||
"When to Use Sigmoid",
|
||||
"Create ReLU instance",
|
||||
"In Plain English:",
|
||||
"What is ReLU?",
|
||||
"Computational Efficiency",
|
||||
"Use case",
|
||||
"Feature Learning",
|
||||
"Foundation",
|
||||
"Deep networks",
|
||||
"Sparsity",
|
||||
"Monotonic",
|
||||
"\ud83e\uddea Unit Test: Softmax Activation",
|
||||
"Unbounded",
|
||||
"Integration:",
|
||||
"Fast",
|
||||
"Alternative",
|
||||
"Why Sigmoid is Useful",
|
||||
"What is Tanh?",
|
||||
"x",
|
||||
"RNNs",
|
||||
"Interpretable",
|
||||
"Mathematical Definition:",
|
||||
"TensorFlow",
|
||||
"EXAMPLE",
|
||||
"Production:",
|
||||
"Show visual example",
|
||||
"Test with mixed positive/negative values",
|
||||
"\ud83e\uddea Unit Test: ReLU Activation",
|
||||
"Create Sigmoid instance",
|
||||
"Multi-class classification",
|
||||
"\ud83e\udde0 The Mathematical Foundation of Nonlinearity",
|
||||
"Create test data",
|
||||
"Test Tanh activation",
|
||||
"Tanh",
|
||||
"Testing",
|
||||
"7",
|
||||
"\ud83e\uddea Unit Test: Sigmoid Activation",
|
||||
"10",
|
||||
"6",
|
||||
"data",
|
||||
"\ud83c\udfaf Module Summary",
|
||||
"Use",
|
||||
"Test Softmax activation",
|
||||
"functions",
|
||||
"Test with various inputs",
|
||||
"2",
|
||||
"Progress",
|
||||
"Build",
|
||||
"Learning Side:",
|
||||
"Final package structure:",
|
||||
"PyTorch",
|
||||
"Check that all outputs are positive",
|
||||
"Sigmoid",
|
||||
"Nonlinearity",
|
||||
"Create Tanh instance",
|
||||
"Numerical stability",
|
||||
"Run the integration test",
|
||||
"Building Side:",
|
||||
"Tanh (Hyperbolic Tangent)"
|
||||
],
|
||||
"todo_count": 4,
|
||||
"hint_count": 5,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1589 lines) - students will be overwhelmed",
|
||||
"9 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Very long cell (86 lines)",
|
||||
"Cell 2: Multiple functions in one cell (3)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Very long cell (155 lines)",
|
||||
"Cell 5: High complexity without guidance",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 6: Very long cell (64 lines)",
|
||||
"Cell 6: Complex implementation without error handling guidance",
|
||||
"Cell 7: Long cell (34 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 8: Very long cell (69 lines)",
|
||||
"Cell 8: Complex implementation without error handling guidance",
|
||||
"Cell 9: Long cell (39 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 10: Very long cell (56 lines)",
|
||||
"Cell 10: Complex implementation without error handling guidance",
|
||||
"Cell 11: Long cell (39 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 12: Very long cell (64 lines)",
|
||||
"Cell 12: Complex implementation without error handling guidance",
|
||||
"Cell 13: Long cell (43 lines)",
|
||||
"Cell 13: Too many concepts (5)",
|
||||
"Cell 14: Very long cell (126 lines)",
|
||||
"Cell 14: Too many concepts (5)",
|
||||
"Cell 15: Very long cell (276 lines)",
|
||||
"Cell 16: Very long cell (112 lines)",
|
||||
"Cell 17: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Split 13 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 86,
|
||||
"char_count": 3823,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Use",
|
||||
"functions",
|
||||
"2",
|
||||
"Build",
|
||||
"data"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (86 lines)",
|
||||
"Multiple functions in one cell (3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 16,
|
||||
"char_count": 761,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Production:",
|
||||
"Integration:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 25,
|
||||
"char_count": 1389,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"PyTorch",
|
||||
"TensorFlow",
|
||||
"\ud83e\udde0 The Mathematical Foundation of Nonlinearity",
|
||||
"Computational Efficiency",
|
||||
"Feature Learning"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 155,
|
||||
"char_count": 7654,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Advantages",
|
||||
"Reality",
|
||||
"Use case",
|
||||
"Foundation",
|
||||
"Unbounded"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (155 lines)",
|
||||
"High complexity without guidance",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 64,
|
||||
"char_count": 2743,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"In Plain English:",
|
||||
"What is ReLU?",
|
||||
"Deep networks",
|
||||
"Sparsity",
|
||||
"Fast"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (64 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 34,
|
||||
"char_count": 1656,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show visual example",
|
||||
"This is a unit test",
|
||||
"Test with mixed positive/negative values",
|
||||
"\ud83e\uddea Unit Test: ReLU Activation",
|
||||
"Create ReLU instance"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (34 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 69,
|
||||
"char_count": 3009,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"What is Sigmoid?",
|
||||
"When to Use Sigmoid",
|
||||
"Monotonic",
|
||||
"Why Sigmoid is Useful",
|
||||
"x"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (69 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 39,
|
||||
"char_count": 1787,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show visual example",
|
||||
"This is a unit test",
|
||||
"Create Sigmoid instance",
|
||||
"Test with various inputs",
|
||||
"\ud83e\uddea Unit Test: Sigmoid Activation"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (39 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 56,
|
||||
"char_count": 2258,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"balanced scale",
|
||||
"Tanh (Hyperbolic Tangent)",
|
||||
"What is Tanh?",
|
||||
"x",
|
||||
"RNNs"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (56 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 39,
|
||||
"char_count": 1767,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show visual example",
|
||||
"This is a unit test",
|
||||
"Test with various inputs",
|
||||
"Create Tanh instance",
|
||||
"Progress"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (39 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 64,
|
||||
"char_count": 2729,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"x",
|
||||
"Interpretable",
|
||||
"Mathematical Definition:",
|
||||
"EXAMPLE",
|
||||
"Multi-class classification"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (64 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 43,
|
||||
"char_count": 2278,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show visual example",
|
||||
"This is a unit test",
|
||||
"\ud83e\uddea Unit Test: Softmax Activation",
|
||||
"Test with various inputs",
|
||||
"Progress"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (43 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 126,
|
||||
"char_count": 6515,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Test with multiple values",
|
||||
"Check that all outputs are positive",
|
||||
"Test Tanh activation",
|
||||
"Test Softmax activation",
|
||||
"Create test data"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (126 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 276,
|
||||
"char_count": 14436,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Tanh",
|
||||
"Testing",
|
||||
"7",
|
||||
"10",
|
||||
"6"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (276 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 112,
|
||||
"char_count": 6520,
|
||||
"complexity_score": 5,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Sigmoid",
|
||||
"Tanh",
|
||||
"Alternative",
|
||||
"2",
|
||||
"Run the integration test"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (112 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 30,
|
||||
"char_count": 1566,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Nonlinearity",
|
||||
"\ud83c\udfaf Module Summary",
|
||||
"Numerical stability",
|
||||
"Sigmoid",
|
||||
"Tanh"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "C",
|
||||
"Complexity": "B",
|
||||
"Cell_Length": "D"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1589 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (73.1 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (23.5% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 2: Too long (86 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (155 lines)",
|
||||
"Cell 5: High complexity without guidance",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (64 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 7: Too long (34 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (69 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (39 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Too long (56 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (39 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (64 lines)",
|
||||
"Cell 13: Too many concepts (5)",
|
||||
"Cell 13: Too long (43 lines)",
|
||||
"Cell 14: Too many concepts (5)",
|
||||
"Cell 14: Too long (126 lines)",
|
||||
"Cell 15: Too many concepts (5)",
|
||||
"Cell 15: Too long (276 lines)",
|
||||
"Cell 16: Too many concepts (5)",
|
||||
"Cell 16: Too long (112 lines)",
|
||||
"Cell 17: Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
@@ -1,158 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 03_layers</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>03_layers</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-C"><h3>C</h3><p>Scaffolding</p></div><div class="grade-box grade-B"><h3>B</h3><p>Complexity</p></div><div class="grade-box grade-F"><h3>F</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1290</p>
|
||||
<p><strong>Total Cells:</strong> 12</p>
|
||||
<p><strong>Avg Cell Length:</strong> 85.0 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 3/5</p>
|
||||
<p><strong>Learning Progression:</strong> 4/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 51</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1290 lines, target: 200-400)</p><p>❌ Too long (85.0 avg, target: ≤30)</p><p>✅ Good (25.0% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1290 lines) - students will be overwhelmed</li><li>7 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Split 10 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 52 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Use, 3, Build</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (52 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 16 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Production:, Integration:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 4: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 31 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Performance Considerations, Composability, nonlinear activations</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (31 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 182 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Orthogonal, Rotation, Each layer is a matrix multiplication:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (182 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 6: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 39 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> try, This is a unit test, Test simple 2x2 case</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (39 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 7: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 116 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Nonlinearity, default, Returns</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (116 lines), Multiple functions in one cell (3), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 62 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show the linear transformation in action, This is a unit test, Test without bias</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (62 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 9: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 80 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Test case 3: Different shapes, Test naive matrix multiplication, Create a simple network: Dense → ReLU → Dense</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (80 lines), High complexity without guidance, Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 228 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 9, Testing, scenarios</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (228 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-5">
|
||||
<h4>Cell 11: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 172 |
|
||||
<strong>Complexity:</strong> 5/5</p>
|
||||
<p><strong>Concepts:</strong> scenarios, network, 2</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (172 lines), Math-heavy without scaffolding</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 12: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 33 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Composability, Matrix multiplication, What You've Accomplished</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (33 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,375 +0,0 @@
|
||||
{
|
||||
"module_name": "03_layers",
|
||||
"module_path": "modules/source/03_layers",
|
||||
"analysis_date": "2025-07-13T09:20:37.695751",
|
||||
"total_lines": 1290,
|
||||
"total_cells": 12,
|
||||
"avg_cell_length": 85,
|
||||
"scaffolding_quality": 3,
|
||||
"complexity_distribution": {
|
||||
"1": 2,
|
||||
"2": 1,
|
||||
"3": 6,
|
||||
"4": 2,
|
||||
"5": 1
|
||||
},
|
||||
"learning_progression_quality": 4,
|
||||
"concepts_covered": [
|
||||
"Nonlinearity",
|
||||
"Test case 3: Different shapes",
|
||||
"Performance Considerations",
|
||||
"Orthogonal",
|
||||
"Rotation",
|
||||
"This is a unit test",
|
||||
"default",
|
||||
"Each layer is a matrix multiplication:",
|
||||
"Composability",
|
||||
"Forward: y = W @ x + b",
|
||||
"Cache locality",
|
||||
"Returns",
|
||||
"Show the linear transformation in action",
|
||||
"Foundation",
|
||||
"Test naive matrix multiplication",
|
||||
"Create a simple network: Dense \u2192 ReLU \u2192 Dense",
|
||||
"composition",
|
||||
"nonlinear activations",
|
||||
"Matrix multiplication",
|
||||
"Integration:",
|
||||
"What You've Accomplished",
|
||||
"Step 2: Building the Dense Layer",
|
||||
"Next Steps",
|
||||
"\ud83e\uddea Test Your Implementations",
|
||||
"Test matrix multiplication",
|
||||
"TensorFlow",
|
||||
"Numerical stability",
|
||||
"Production:",
|
||||
"Testing",
|
||||
"Mathematical Foundations",
|
||||
"7",
|
||||
"Test simple 2x2 case",
|
||||
"try",
|
||||
"Use",
|
||||
"Test without bias",
|
||||
"network",
|
||||
"9",
|
||||
"2",
|
||||
"Progress",
|
||||
"Build",
|
||||
"Learning Side:",
|
||||
"Final package structure:",
|
||||
"scenarios",
|
||||
"Test basic Dense layer",
|
||||
"Run the integration test",
|
||||
"3",
|
||||
"Building Side:",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand",
|
||||
"Test different shapes",
|
||||
"Print final summary"
|
||||
],
|
||||
"todo_count": 2,
|
||||
"hint_count": 3,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1290 lines) - students will be overwhelmed",
|
||||
"7 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Very long cell (52 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Long cell (31 lines)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Very long cell (182 lines)",
|
||||
"Cell 5: Complex implementation without error handling guidance",
|
||||
"Cell 6: Long cell (39 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 7: Very long cell (116 lines)",
|
||||
"Cell 7: Multiple functions in one cell (3)",
|
||||
"Cell 7: Complex implementation without error handling guidance",
|
||||
"Cell 8: Very long cell (62 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 9: Very long cell (80 lines)",
|
||||
"Cell 9: High complexity without guidance",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 10: Very long cell (228 lines)",
|
||||
"Cell 11: Very long cell (172 lines)",
|
||||
"Cell 11: Math-heavy without scaffolding",
|
||||
"Cell 12: Long cell (33 lines)",
|
||||
"Cell 12: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Split 10 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 52,
|
||||
"char_count": 2563,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Use",
|
||||
"3",
|
||||
"Build",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (52 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 16,
|
||||
"char_count": 718,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Production:",
|
||||
"Integration:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 31,
|
||||
"char_count": 1544,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Performance Considerations",
|
||||
"Composability",
|
||||
"nonlinear activations",
|
||||
"TensorFlow",
|
||||
"Numerical stability"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (31 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 182,
|
||||
"char_count": 7712,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Orthogonal",
|
||||
"Rotation",
|
||||
"Each layer is a matrix multiplication:",
|
||||
"Forward: y = W @ x + b",
|
||||
"Cache locality"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (182 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 39,
|
||||
"char_count": 2002,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"try",
|
||||
"This is a unit test",
|
||||
"Test simple 2x2 case",
|
||||
"Progress",
|
||||
"Test different shapes"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (39 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 116,
|
||||
"char_count": 5000,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Nonlinearity",
|
||||
"default",
|
||||
"Returns",
|
||||
"Foundation",
|
||||
"Step 2: Building the Dense Layer"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (116 lines)",
|
||||
"Multiple functions in one cell (3)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 62,
|
||||
"char_count": 2943,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show the linear transformation in action",
|
||||
"This is a unit test",
|
||||
"Test without bias",
|
||||
"Test basic Dense layer",
|
||||
"Test naive matrix multiplication"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (62 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 80,
|
||||
"char_count": 3633,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Test case 3: Different shapes",
|
||||
"Test naive matrix multiplication",
|
||||
"Create a simple network: Dense \u2192 ReLU \u2192 Dense",
|
||||
"\ud83e\uddea Test Your Implementations",
|
||||
"Test matrix multiplication"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (80 lines)",
|
||||
"High complexity without guidance",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 228,
|
||||
"char_count": 12536,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"9",
|
||||
"Testing",
|
||||
"scenarios",
|
||||
"composition",
|
||||
"7"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (228 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 172,
|
||||
"char_count": 9096,
|
||||
"complexity_score": 5,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"scenarios",
|
||||
"network",
|
||||
"2",
|
||||
"Run the integration test",
|
||||
"Print final summary"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (172 lines)",
|
||||
"Math-heavy without scaffolding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 33,
|
||||
"char_count": 1682,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Composability",
|
||||
"Matrix multiplication",
|
||||
"What You've Accomplished",
|
||||
"Next Steps",
|
||||
"Mathematical Foundations"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (33 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "C",
|
||||
"Complexity": "B",
|
||||
"Cell_Length": "F"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1290 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (85.0 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (25.0% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 2: Too long (52 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 4: Too long (31 lines)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (182 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (39 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 7: Too long (116 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (62 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (80 lines)",
|
||||
"Cell 9: High complexity without guidance",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Too long (228 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (172 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (33 lines)"
|
||||
]
|
||||
}
|
||||
@@ -1,166 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 04_networks</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>04_networks</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-C"><h3>C</h3><p>Scaffolding</p></div><div class="grade-box grade-B"><h3>B</h3><p>Complexity</p></div><div class="grade-box grade-F"><h3>F</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1425</p>
|
||||
<p><strong>Total Cells:</strong> 13</p>
|
||||
<p><strong>Avg Cell Length:</strong> 86.2 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 3/5</p>
|
||||
<p><strong>Learning Progression:</strong> 4/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 50</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1425 lines, target: 200-400)</p><p>❌ Too long (86.2 avg, target: ≤30)</p><p>✅ Good (15.4% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1425 lines) - students will be overwhelmed</li><li>7 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Split 11 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Example Illustration</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 58 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Use, Module 4: Networks - Neural Network Architectures, Build</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (58 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 17 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Production:, Integration:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 4: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 31 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Connections, Architecture search, Composability</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (31 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 211 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Nonlinearity, Why Networks Matter: The Scaling Laws, Advantages</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (211 lines), Multiple functions in one cell (5), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 6: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 43 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> This is a unit test, Show the network architecture, Create a simple 2-layer network: 3 → 4 → 2</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (43 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 7: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 72 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> default, Returns, Multi-Layer Perceptron</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (72 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 54 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show the MLP pattern, This is a unit test, Progress</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (54 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 72 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Verify the output, Forward pass, 🧪 Test Your Network Implementations</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (72 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 41 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Nonlinearity, Forward pass, Sequential architecture</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (41 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 11: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 287 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 9, Testing, functions</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (287 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-5">
|
||||
<h4>Cell 12: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 185 |
|
||||
<strong>Complexity:</strong> 5/5</p>
|
||||
<p><strong>Concepts:</strong> 2, Run the integration test, Print final summary</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (185 lines), Math-heavy without scaffolding</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 13: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 41 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Nonlinearity, Forward pass, Sequential architecture</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (41 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,395 +0,0 @@
|
||||
{
|
||||
"module_name": "04_networks",
|
||||
"module_path": "modules/source/04_networks",
|
||||
"analysis_date": "2025-07-13T09:20:37.714469",
|
||||
"total_lines": 1425,
|
||||
"total_cells": 13,
|
||||
"avg_cell_length": 86.23076923076923,
|
||||
"scaffolding_quality": 3,
|
||||
"complexity_distribution": {
|
||||
"1": 1,
|
||||
"2": 1,
|
||||
"3": 9,
|
||||
"4": 1,
|
||||
"5": 1
|
||||
},
|
||||
"learning_progression_quality": 4,
|
||||
"concepts_covered": [
|
||||
"Nonlinearity",
|
||||
"Why Networks Matter: The Scaling Laws",
|
||||
"Advantages",
|
||||
"default",
|
||||
"Verify the output",
|
||||
"This is a unit test",
|
||||
"Connections",
|
||||
"Architecture search",
|
||||
"Composability",
|
||||
"**1. Problem Analysis**",
|
||||
"Input layer",
|
||||
"Returns",
|
||||
"The Architecture Design Space",
|
||||
"Multi-Layer Perceptron",
|
||||
"Foundation",
|
||||
"Forward pass",
|
||||
"Sequential architecture",
|
||||
"Create a simple 2-layer network: 3 \u2192 4 \u2192 2",
|
||||
"What You've Accomplished",
|
||||
"Integration:",
|
||||
"layers",
|
||||
"Use your networks",
|
||||
"function composition",
|
||||
"Versatile",
|
||||
"Production:",
|
||||
"Testing",
|
||||
"7",
|
||||
"Test all networks",
|
||||
"Test different architectures",
|
||||
"6",
|
||||
"Show the MLP pattern",
|
||||
"Use",
|
||||
"Module 4: Networks - Neural Network Architectures",
|
||||
"Create networks with different architectures",
|
||||
"functions",
|
||||
"9",
|
||||
"2",
|
||||
"Progress",
|
||||
"Build",
|
||||
"4",
|
||||
"Learning Side:",
|
||||
"\ud83e\uddea Unit Test: Sequential Network",
|
||||
"Final package structure:",
|
||||
"\ud83e\uddea Test Your Network Implementations",
|
||||
"Show the network architecture",
|
||||
"Run the integration test",
|
||||
"3",
|
||||
"Building Side:",
|
||||
"Understand",
|
||||
"Print final summary"
|
||||
],
|
||||
"todo_count": 2,
|
||||
"hint_count": 3,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1425 lines) - students will be overwhelmed",
|
||||
"7 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Very long cell (58 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Long cell (31 lines)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Very long cell (211 lines)",
|
||||
"Cell 5: Multiple functions in one cell (5)",
|
||||
"Cell 5: Complex implementation without error handling guidance",
|
||||
"Cell 6: Long cell (43 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 7: Very long cell (72 lines)",
|
||||
"Cell 7: Complex implementation without error handling guidance",
|
||||
"Cell 8: Very long cell (54 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 9: Very long cell (72 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 10: Long cell (41 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 11: Very long cell (287 lines)",
|
||||
"Cell 12: Very long cell (185 lines)",
|
||||
"Cell 12: Math-heavy without scaffolding",
|
||||
"Cell 13: Long cell (41 lines)",
|
||||
"Cell 13: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Split 11 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 58,
|
||||
"char_count": 2899,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "example_illustration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Use",
|
||||
"Module 4: Networks - Neural Network Architectures",
|
||||
"Build",
|
||||
"4",
|
||||
"Understand"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (58 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 17,
|
||||
"char_count": 819,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Production:",
|
||||
"Integration:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 31,
|
||||
"char_count": 1654,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Connections",
|
||||
"Architecture search",
|
||||
"Composability",
|
||||
"The Architecture Design Space",
|
||||
"function composition"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (31 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 211,
|
||||
"char_count": 9091,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Nonlinearity",
|
||||
"Why Networks Matter: The Scaling Laws",
|
||||
"Advantages",
|
||||
"**1. Problem Analysis**",
|
||||
"Input layer"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (211 lines)",
|
||||
"Multiple functions in one cell (5)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 43,
|
||||
"char_count": 1975,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"This is a unit test",
|
||||
"Show the network architecture",
|
||||
"Create a simple 2-layer network: 3 \u2192 4 \u2192 2",
|
||||
"Progress",
|
||||
"\ud83e\uddea Unit Test: Sequential Network"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (43 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 72,
|
||||
"char_count": 2984,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"default",
|
||||
"Returns",
|
||||
"Multi-Layer Perceptron",
|
||||
"Foundation",
|
||||
"Versatile"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (72 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 54,
|
||||
"char_count": 2785,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show the MLP pattern",
|
||||
"This is a unit test",
|
||||
"Progress",
|
||||
"Test different architectures",
|
||||
"layers"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (54 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 72,
|
||||
"char_count": 3116,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Verify the output",
|
||||
"Forward pass",
|
||||
"\ud83e\uddea Test Your Network Implementations",
|
||||
"Create networks with different architectures",
|
||||
"Test all networks"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (72 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 41,
|
||||
"char_count": 2254,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Nonlinearity",
|
||||
"Forward pass",
|
||||
"Sequential architecture",
|
||||
"What You've Accomplished",
|
||||
"Use your networks"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (41 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 287,
|
||||
"char_count": 16733,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"9",
|
||||
"Testing",
|
||||
"functions",
|
||||
"7",
|
||||
"2"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (287 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 185,
|
||||
"char_count": 10640,
|
||||
"complexity_score": 5,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"2",
|
||||
"Run the integration test",
|
||||
"Print final summary",
|
||||
"3",
|
||||
"6"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (185 lines)",
|
||||
"Math-heavy without scaffolding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 41,
|
||||
"char_count": 2253,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Nonlinearity",
|
||||
"Forward pass",
|
||||
"Sequential architecture",
|
||||
"What You've Accomplished",
|
||||
"Use your networks"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (41 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "C",
|
||||
"Complexity": "B",
|
||||
"Cell_Length": "F"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1425 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (86.2 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (15.4% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 2: Too long (58 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 4: Too long (31 lines)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (211 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (43 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 7: Too long (72 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (54 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (72 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Too long (41 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (287 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (185 lines)",
|
||||
"Cell 13: Too many concepts (5)",
|
||||
"Cell 13: Too long (41 lines)"
|
||||
]
|
||||
}
|
||||
@@ -1,166 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 05_cnn</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>05_cnn</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-C"><h3>C</h3><p>Scaffolding</p></div><div class="grade-box grade-B"><h3>B</h3><p>Complexity</p></div><div class="grade-box grade-D"><h3>D</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1173</p>
|
||||
<p><strong>Total Cells:</strong> 13</p>
|
||||
<p><strong>Avg Cell Length:</strong> 70.6 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 3/5</p>
|
||||
<p><strong>Learning Progression:</strong> 4/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 52</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1173 lines, target: 200-400)</p><p>❌ Too long (70.6 avg, target: ≤30)</p><p>✅ Good (15.4% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1173 lines) - students will be overwhelmed</li><li>6 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Split 11 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 55 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Module 5: CNN - Convolutional Neural Networks, Use, Build</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (55 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 17 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Production:, Integration:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 4: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 32 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Sparse connectivity, Performance Considerations, 🧠 The Mathematical Foundation of Convolution</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (32 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 81 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> ernel, Image, Spatial hierarchy</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (81 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 6: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 50 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Quick Test: Convolution Operation, Test simple 3x3 input with 2x2 kernel, Show the convolution process</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (50 lines), High complexity without guidance, Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 7: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 84 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Returns, Composability, Autonomous driving</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (84 lines), Multiple functions in one cell (3), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 45 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Show the layer behavior, Create a Conv2D layer, 🧪 Quick Test: Conv2D Layer</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (45 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 47 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Network composition, Returns, x</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (47 lines), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 51 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Test case 1: 2x2 tensor, Test case 3: Different shapes, 🧪 Quick Test: Flatten Function</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (51 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 11: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 316 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> operations, pipelines, pipeline</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (316 lines), Too many concepts (5), Multiple functions in one cell (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 12: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 85 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Test Conv2D layer, Test case 2: 3x3 tensor, Test case 1: 2x2 tensor</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (85 lines), High complexity without guidance, Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 13: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 46 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Spatial hierarchy, What You've Accomplished, Sliding window</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (46 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,407 +0,0 @@
|
||||
{
|
||||
"module_name": "05_cnn",
|
||||
"module_path": "modules/source/05_cnn",
|
||||
"analysis_date": "2025-07-13T09:20:37.729072",
|
||||
"total_lines": 1173,
|
||||
"total_cells": 13,
|
||||
"avg_cell_length": 70.61538461538461,
|
||||
"scaffolding_quality": 3,
|
||||
"complexity_distribution": {
|
||||
"1": 2,
|
||||
"2": 0,
|
||||
"3": 9,
|
||||
"4": 2,
|
||||
"5": 0
|
||||
},
|
||||
"learning_progression_quality": 4,
|
||||
"concepts_covered": [
|
||||
"Sparse connectivity",
|
||||
"Show the layer behavior",
|
||||
"Performance Considerations",
|
||||
"Network composition",
|
||||
"\ud83e\udde0 The Mathematical Foundation of Convolution",
|
||||
"ernel",
|
||||
"Returns",
|
||||
"Image",
|
||||
"Spatial hierarchy",
|
||||
"Composability",
|
||||
"Parameter efficiency",
|
||||
"\ud83e\uddea Quick Test: Flatten Function",
|
||||
"Autonomous driving",
|
||||
"Test Conv2D layer",
|
||||
"What You've Accomplished",
|
||||
"Sliding window",
|
||||
"Test different kernel sizes",
|
||||
"Run the comprehensive test suite",
|
||||
"Integration:",
|
||||
"Computer vision",
|
||||
"Feature maps",
|
||||
"Conv2D layer",
|
||||
"x",
|
||||
"Step 3: Flattening for Dense Layers",
|
||||
"Test case 2: 3x3 tensor",
|
||||
"Test case 1: 2x2 tensor",
|
||||
"EXAMPLE",
|
||||
"Production:",
|
||||
"Verify pipeline works",
|
||||
"\ud83e\uddea Quick Test: Conv2D Layer",
|
||||
"Test complete CNN pipeline",
|
||||
"pipelines",
|
||||
"Test case 3: Different shapes",
|
||||
"pipeline",
|
||||
"Show the convolution process",
|
||||
"Show the flattening behavior",
|
||||
"operations",
|
||||
"Use",
|
||||
"2",
|
||||
"Progress",
|
||||
"Build",
|
||||
"Learning Side:",
|
||||
"Final package structure:",
|
||||
"\ud83e\uddea Quick Test: Convolution Operation",
|
||||
"Module 5: CNN - Convolutional Neural Networks",
|
||||
"Test simple 3x3 input with 2x2 kernel",
|
||||
"Create a Conv2D layer",
|
||||
"3",
|
||||
"Test output shape",
|
||||
"Building Side:",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand"
|
||||
],
|
||||
"todo_count": 3,
|
||||
"hint_count": 3,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1173 lines) - students will be overwhelmed",
|
||||
"6 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Very long cell (55 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Long cell (32 lines)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Very long cell (81 lines)",
|
||||
"Cell 5: Complex implementation without error handling guidance",
|
||||
"Cell 6: Long cell (50 lines)",
|
||||
"Cell 6: High complexity without guidance",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 7: Very long cell (84 lines)",
|
||||
"Cell 7: Multiple functions in one cell (3)",
|
||||
"Cell 7: Complex implementation without error handling guidance",
|
||||
"Cell 8: Long cell (45 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 9: Long cell (47 lines)",
|
||||
"Cell 9: Complex implementation without error handling guidance",
|
||||
"Cell 10: Very long cell (51 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 11: Very long cell (316 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Multiple functions in one cell (5)",
|
||||
"Cell 12: Very long cell (85 lines)",
|
||||
"Cell 12: High complexity without guidance",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 13: Long cell (46 lines)",
|
||||
"Cell 13: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Split 11 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 55,
|
||||
"char_count": 2701,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Module 5: CNN - Convolutional Neural Networks",
|
||||
"Use",
|
||||
"Build",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (55 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 17,
|
||||
"char_count": 783,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Production:",
|
||||
"Integration:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 32,
|
||||
"char_count": 1539,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Sparse connectivity",
|
||||
"Performance Considerations",
|
||||
"\ud83e\udde0 The Mathematical Foundation of Convolution",
|
||||
"Spatial hierarchy",
|
||||
"Parameter efficiency"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (32 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 81,
|
||||
"char_count": 3841,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"ernel",
|
||||
"Image",
|
||||
"Spatial hierarchy",
|
||||
"Parameter efficiency",
|
||||
"Autonomous driving"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (81 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 50,
|
||||
"char_count": 2480,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Quick Test: Convolution Operation",
|
||||
"Test simple 3x3 input with 2x2 kernel",
|
||||
"Show the convolution process",
|
||||
"Progress",
|
||||
"Test output shape"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (50 lines)",
|
||||
"High complexity without guidance",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 84,
|
||||
"char_count": 3684,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Returns",
|
||||
"Composability",
|
||||
"Autonomous driving",
|
||||
"Conv2D layer",
|
||||
"x"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (84 lines)",
|
||||
"Multiple functions in one cell (3)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 45,
|
||||
"char_count": 2098,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Show the layer behavior",
|
||||
"Create a Conv2D layer",
|
||||
"\ud83e\uddea Quick Test: Conv2D Layer",
|
||||
"Test different kernel sizes",
|
||||
"Progress"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (45 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 47,
|
||||
"char_count": 1897,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Network composition",
|
||||
"Returns",
|
||||
"x",
|
||||
"Step 3: Flattening for Dense Layers",
|
||||
"EXAMPLE"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (47 lines)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 51,
|
||||
"char_count": 2384,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Test case 1: 2x2 tensor",
|
||||
"Test case 3: Different shapes",
|
||||
"\ud83e\uddea Quick Test: Flatten Function",
|
||||
"Show the flattening behavior",
|
||||
"3"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (51 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 316,
|
||||
"char_count": 15697,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"operations",
|
||||
"pipelines",
|
||||
"pipeline",
|
||||
"2",
|
||||
"Run the comprehensive test suite"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (316 lines)",
|
||||
"Too many concepts (5)",
|
||||
"Multiple functions in one cell (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 85,
|
||||
"char_count": 4087,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Test Conv2D layer",
|
||||
"Test case 2: 3x3 tensor",
|
||||
"Test case 1: 2x2 tensor",
|
||||
"Verify pipeline works",
|
||||
"Test complete CNN pipeline"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (85 lines)",
|
||||
"High complexity without guidance",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 46,
|
||||
"char_count": 2420,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Spatial hierarchy",
|
||||
"What You've Accomplished",
|
||||
"Sliding window",
|
||||
"Computer vision",
|
||||
"Feature maps"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (46 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "C",
|
||||
"Complexity": "B",
|
||||
"Cell_Length": "D"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1173 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (70.6 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (15.4% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 2: Too long (55 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 4: Too long (32 lines)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (81 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (50 lines)",
|
||||
"Cell 6: High complexity without guidance",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 7: Too long (84 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (45 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (47 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Too long (51 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (316 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (85 lines)",
|
||||
"Cell 12: High complexity without guidance",
|
||||
"Cell 13: Too many concepts (5)",
|
||||
"Cell 13: Too long (46 lines)"
|
||||
]
|
||||
}
|
||||
@@ -1,158 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 06_dataloader</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>06_dataloader</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-C">
|
||||
<h2>C</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-C"><h3>C</h3><p>Scaffolding</p></div><div class="grade-box grade-A"><h3>A</h3><p>Complexity</p></div><div class="grade-box grade-F"><h3>F</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1368</p>
|
||||
<p><strong>Total Cells:</strong> 12</p>
|
||||
<p><strong>Avg Cell Length:</strong> 88.5 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 3/5</p>
|
||||
<p><strong>Learning Progression:</strong> 4/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 50</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1368 lines, target: 200-400)</p><p>❌ Too long (88.5 avg, target: ≤30)</p><p>✅ Good (0.0% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1368 lines) - students will be overwhelmed</li><li>8 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Split 9 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 53 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Use, 6, Build</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (53 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 16 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Production:, Integration:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-2">
|
||||
<h4>Cell 4: Practical Connection</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 28 |
|
||||
<strong>Complexity:</strong> 2/5</p>
|
||||
<p><strong>Concepts:</strong> Performance Considerations, Production reality, Memory efficiency</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 113 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Returns, Memory efficiency, Visual Intuition</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (113 lines), Multiple functions in one cell (4), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 6: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 53 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Quick Test: Dataset Base Class, __len__, Show the dataset pattern</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (53 lines), Multiple functions in one cell (4)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 7: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 121 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Why DataLoaders Matter, DataLoader, Returns</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (121 lines), Multiple functions in one cell (3), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 75 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 🧪 Quick Test: DataLoader, Show the DataLoader behavior, Test DataLoader immediately after implementation</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (75 lines), Multiple functions in one cell (4)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 93 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> EXAMPLE, num_classes, Returns</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (93 lines), Multiple functions in one cell (4), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Concept Reinforcement</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 363 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> interface, scenarios, integration</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (363 lines), Too many concepts (5), Multiple functions in one cell (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 11: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 89 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Check that shapes are correct, Verify we processed all data, Test sample retrieval</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (89 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 12: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 49 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> DataLoader pattern, Build real datasets, What You've Accomplished</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (49 lines), Too many concepts (5)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,376 +0,0 @@
|
||||
{
|
||||
"module_name": "06_dataloader",
|
||||
"module_path": "modules/source/06_dataloader",
|
||||
"analysis_date": "2025-07-13T09:20:37.746666",
|
||||
"total_lines": 1368,
|
||||
"total_cells": 12,
|
||||
"avg_cell_length": 88.5,
|
||||
"scaffolding_quality": 3,
|
||||
"complexity_distribution": {
|
||||
"1": 2,
|
||||
"2": 1,
|
||||
"3": 9,
|
||||
"4": 0,
|
||||
"5": 0
|
||||
},
|
||||
"learning_progression_quality": 4,
|
||||
"concepts_covered": [
|
||||
"Performance Considerations",
|
||||
"Why DataLoaders Matter",
|
||||
"Production reality",
|
||||
"Returns",
|
||||
"DataLoader",
|
||||
"size",
|
||||
"What is a DataLoader?",
|
||||
"interface",
|
||||
"Check that shapes are correct",
|
||||
"Verify we processed all data",
|
||||
"DataLoader pattern",
|
||||
"Memory efficiency",
|
||||
"What You've Accomplished",
|
||||
"Visual Intuition",
|
||||
"Create a minimal test dataset",
|
||||
"Test sample retrieval",
|
||||
"class",
|
||||
"Integration:",
|
||||
"Run the comprehensive test suite",
|
||||
"Verify we processed all samples",
|
||||
"Computer vision",
|
||||
"Image datasets",
|
||||
"Test with shuffling",
|
||||
"Parallel processing",
|
||||
"Data is the fuel",
|
||||
"\ud83e\uddea Quick Test: DataLoader",
|
||||
"Production:",
|
||||
"EXAMPLE",
|
||||
"__getitem__",
|
||||
"6",
|
||||
"\ud83e\uddea Quick Test: Dataset Base Class",
|
||||
"Use",
|
||||
"2",
|
||||
"Test DataLoader immediately after implementation",
|
||||
"Progress",
|
||||
"Build",
|
||||
"Learning Side:",
|
||||
"Final package structure:",
|
||||
"num_classes",
|
||||
"__len__",
|
||||
"scenarios",
|
||||
"Why We Need Concrete Examples",
|
||||
"Show the dataset pattern",
|
||||
"integration",
|
||||
"Build real datasets",
|
||||
"Building Side:",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand",
|
||||
"Use the test dataset from before",
|
||||
"Show the DataLoader behavior"
|
||||
],
|
||||
"todo_count": 4,
|
||||
"hint_count": 3,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1368 lines) - students will be overwhelmed",
|
||||
"8 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Very long cell (53 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Very long cell (113 lines)",
|
||||
"Cell 5: Multiple functions in one cell (4)",
|
||||
"Cell 5: Complex implementation without error handling guidance",
|
||||
"Cell 6: Very long cell (53 lines)",
|
||||
"Cell 6: Multiple functions in one cell (4)",
|
||||
"Cell 7: Very long cell (121 lines)",
|
||||
"Cell 7: Multiple functions in one cell (3)",
|
||||
"Cell 7: Complex implementation without error handling guidance",
|
||||
"Cell 8: Very long cell (75 lines)",
|
||||
"Cell 8: Multiple functions in one cell (4)",
|
||||
"Cell 9: Very long cell (93 lines)",
|
||||
"Cell 9: Multiple functions in one cell (4)",
|
||||
"Cell 9: Complex implementation without error handling guidance",
|
||||
"Cell 10: Very long cell (363 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Multiple functions in one cell (5)",
|
||||
"Cell 11: Very long cell (89 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 12: Long cell (49 lines)",
|
||||
"Cell 12: Too many concepts (5)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Split 9 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 53,
|
||||
"char_count": 2463,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Use",
|
||||
"6",
|
||||
"Build",
|
||||
"Understand",
|
||||
"Build \u2192 Use \u2192 Understand"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (53 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 16,
|
||||
"char_count": 762,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Production:",
|
||||
"Integration:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 28,
|
||||
"char_count": 1425,
|
||||
"complexity_score": 2,
|
||||
"educational_type": "practical_connection",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Performance Considerations",
|
||||
"Production reality",
|
||||
"Memory efficiency",
|
||||
"Parallel processing",
|
||||
"Data is the fuel"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 113,
|
||||
"char_count": 4973,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Returns",
|
||||
"Memory efficiency",
|
||||
"Visual Intuition",
|
||||
"class",
|
||||
"Image datasets"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (113 lines)",
|
||||
"Multiple functions in one cell (4)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 53,
|
||||
"char_count": 2404,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Quick Test: Dataset Base Class",
|
||||
"__len__",
|
||||
"Show the dataset pattern",
|
||||
"__getitem__",
|
||||
"Create a minimal test dataset"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (53 lines)",
|
||||
"Multiple functions in one cell (4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 121,
|
||||
"char_count": 5258,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Why DataLoaders Matter",
|
||||
"DataLoader",
|
||||
"Returns",
|
||||
"What is a DataLoader?",
|
||||
"Memory efficiency"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (121 lines)",
|
||||
"Multiple functions in one cell (3)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 75,
|
||||
"char_count": 3725,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"\ud83e\uddea Quick Test: DataLoader",
|
||||
"Show the DataLoader behavior",
|
||||
"Test DataLoader immediately after implementation",
|
||||
"Progress",
|
||||
"Use the test dataset from before"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (75 lines)",
|
||||
"Multiple functions in one cell (4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 93,
|
||||
"char_count": 3756,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"EXAMPLE",
|
||||
"num_classes",
|
||||
"Returns",
|
||||
"size",
|
||||
"Why We Need Concrete Examples"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (93 lines)",
|
||||
"Multiple functions in one cell (4)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 363,
|
||||
"char_count": 20128,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_reinforcement",
|
||||
"has_todo": true,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"interface",
|
||||
"scenarios",
|
||||
"integration",
|
||||
"2",
|
||||
"Run the comprehensive test suite"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (363 lines)",
|
||||
"Too many concepts (5)",
|
||||
"Multiple functions in one cell (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 89,
|
||||
"char_count": 4928,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Check that shapes are correct",
|
||||
"Verify we processed all data",
|
||||
"Test sample retrieval",
|
||||
"Verify we processed all samples",
|
||||
"Test with shuffling"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (89 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 49,
|
||||
"char_count": 2900,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"DataLoader pattern",
|
||||
"Build real datasets",
|
||||
"What You've Accomplished",
|
||||
"Memory efficiency",
|
||||
"Computer vision"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (49 lines)",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "C",
|
||||
"category_grades": {
|
||||
"Scaffolding": "C",
|
||||
"Complexity": "A",
|
||||
"Cell_Length": "F"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1368 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (88.5 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (0.0% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 2: Too long (53 lines)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (113 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (53 lines)",
|
||||
"Cell 7: Too many concepts (5)",
|
||||
"Cell 7: Too long (121 lines)",
|
||||
"Cell 8: Too many concepts (5)",
|
||||
"Cell 8: Too long (75 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (93 lines)",
|
||||
"Cell 10: Too many concepts (5)",
|
||||
"Cell 10: Too long (363 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (89 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (49 lines)"
|
||||
]
|
||||
}
|
||||
@@ -1,182 +0,0 @@
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>TinyTorch Module Report Card: 07_autograd</title>
|
||||
<style>
|
||||
body { font-family: 'Segoe UI', sans-serif; margin: 20px; background: #f5f5f5; }
|
||||
.report-card { background: white; padding: 30px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); max-width: 1000px; margin: 0 auto; }
|
||||
.header { text-align: center; border-bottom: 3px solid #2196F3; padding-bottom: 20px; margin-bottom: 30px; }
|
||||
.grade-box { display: inline-block; margin: 10px; padding: 20px; border-radius: 8px; text-align: center; min-width: 100px; }
|
||||
.grade-A { background: #4CAF50; color: white; }
|
||||
.grade-B { background: #8BC34A; color: white; }
|
||||
.grade-C { background: #FF9800; color: white; }
|
||||
.grade-D { background: #FF5722; color: white; }
|
||||
.grade-F { background: #F44336; color: white; }
|
||||
.metrics { display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin: 20px 0; }
|
||||
.metric-box { padding: 15px; border: 1px solid #ddd; border-radius: 5px; }
|
||||
.critical { background: #ffebee; border-left: 4px solid #f44336; }
|
||||
.good { background: #e8f5e8; border-left: 4px solid #4caf50; }
|
||||
.warning { background: #fff3e0; border-left: 4px solid #ff9800; }
|
||||
.recommendations { background: #e3f2fd; padding: 20px; border-radius: 5px; margin: 20px 0; }
|
||||
.cell-analysis { margin: 10px 0; padding: 10px; border: 1px solid #eee; border-radius: 3px; }
|
||||
.complexity-1 { border-left: 4px solid #4CAF50; }
|
||||
.complexity-2 { border-left: 4px solid #8BC34A; }
|
||||
.complexity-3 { border-left: 4px solid #FF9800; }
|
||||
.complexity-4 { border-left: 4px solid #FF5722; }
|
||||
.complexity-5 { border-left: 4px solid #F44336; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="report-card">
|
||||
<div class="header">
|
||||
<h1>📊 TinyTorch Module Report Card</h1>
|
||||
<h2>07_autograd</h2>
|
||||
<p>Analysis Date: 2025-07-13</p>
|
||||
</div>
|
||||
|
||||
<div class="grades">
|
||||
<h3>📈 Overall Grade</h3>
|
||||
<div class="grade-box grade-D">
|
||||
<h2>D</h2>
|
||||
<p>Overall</p>
|
||||
</div>
|
||||
<div class="grade-box grade-D"><h3>D</h3><p>Scaffolding</p></div><div class="grade-box grade-B"><h3>B</h3><p>Complexity</p></div><div class="grade-box grade-F"><h3>F</h3><p>Cell Length</p></div>
|
||||
</div>
|
||||
|
||||
<div class="metrics">
|
||||
<div class="metric-box">
|
||||
<h4>📏 Size Metrics</h4>
|
||||
<p><strong>Total Lines:</strong> 1672</p>
|
||||
<p><strong>Total Cells:</strong> 15</p>
|
||||
<p><strong>Avg Cell Length:</strong> 86.9 lines</p>
|
||||
</div>
|
||||
|
||||
<div class="metric-box">
|
||||
<h4>🎯 Quality Metrics</h4>
|
||||
<p><strong>Scaffolding Quality:</strong> 2/5</p>
|
||||
<p><strong>Learning Progression:</strong> 1/5</p>
|
||||
<p><strong>Concepts Covered:</strong> 52</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="metric-box"><h4>🎯 vs Targets</h4><p>❌ Too long (1672 lines, target: 200-400)</p><p>❌ Too long (86.9 avg, target: ≤30)</p><p>✅ Good (26.7% high-complexity)</p></div><div class="critical"><h4>🚨 Critical Issues</h4><ul><li>Module too long (1672 lines) - students will be overwhelmed</li><li>Sudden complexity jumps will overwhelm students</li><li>9 cells are too long (>50 lines)</li></ul></div><div class="recommendations"><h4>💡 Recommendations</h4><ul><li>Break module into smaller sections or multiple modules</li><li>Add implementation ladders: break complex functions into 3 progressive steps</li><li>Add concept bridges: connect new ideas to familiar concepts</li><li>Include confidence builders: early wins to build momentum</li><li>Split 12 long cells into smaller, focused cells</li><li>Add immediate feedback tests after implementations</li></ul></div><div class="cell-analysis-section"><h3>🔍 Cell-by-Cell Analysis</h3>
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 1: Demonstration</h4>
|
||||
<p><strong>Type:</strong> code | <strong>Lines:</strong> 9 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> None</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 2: Explanation</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 28 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Analyze, Use, 7</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-1">
|
||||
<h4>Cell 3: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 16 |
|
||||
<strong>Complexity:</strong> 1/5</p>
|
||||
<p><strong>Concepts:</strong> Final package structure:, Foundation:, Production:</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 4: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 187 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Multivariable Chain Rule, Use case, Computational Efficiency</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (187 lines), High complexity without guidance, Multiple functions in one cell (3)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 5: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 127 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Variable, Data, grad_fn</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (127 lines), Too many concepts (5), Multiple functions in one cell (10), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 6: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 205 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Forward pass, Step 3: Basic Operations with Gradients, Addition</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (205 lines), Too many concepts (5), Multiple functions in one cell (8), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 7: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 52 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Run the test, Step 4: Testing Basic Operations, 4</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (52 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 8: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 42 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Run the test, Step 5: Chain Rule Testing, 5</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (42 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 9: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 82 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Returns, TODO, Step 6: Activation Function Gradients</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (82 lines), Multiple functions in one cell (4), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 10: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 46 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Run the test, Step 7: Integration Testing, 7</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (46 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 11: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 39 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Backward pass, Forward pass, What You've Accomplished</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Long cell (39 lines), High complexity without guidance, Too many concepts (5)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-4">
|
||||
<h4>Cell 12: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 204 |
|
||||
<strong>Complexity:</strong> 4/5</p>
|
||||
<p><strong>Concepts:</strong> Numerical Precision, Returns, Gradient Checkpointing</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (204 lines), Too many concepts (5), Multiple functions in one cell (10), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 13: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 105 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Computational Graph, 9, modules</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (105 lines), Multiple functions in one cell (3), Complex implementation without error handling guidance</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 14: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 98 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> Step 10: Advanced Testing, Run the test, 2 + y.grad.data.data.item()</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (98 lines)</p>
|
||||
</div>
|
||||
|
||||
<div class="cell-analysis complexity-3">
|
||||
<h4>Cell 15: Concept Introduction</h4>
|
||||
<p><strong>Type:</strong> markdown | <strong>Lines:</strong> 63 |
|
||||
<strong>Complexity:</strong> 3/5</p>
|
||||
<p><strong>Concepts:</strong> 11, Run the test, Step 11: Complete ML Pipeline Example</p>
|
||||
<p class="warning"><strong>⚠️ Issues:</strong> Very long cell (63 lines)</p>
|
||||
</div>
|
||||
</div></div></body></html>
|
||||
@@ -1,450 +0,0 @@
|
||||
{
|
||||
"module_name": "07_autograd",
|
||||
"module_path": "modules/source/07_autograd",
|
||||
"analysis_date": "2025-07-13T09:20:37.762821",
|
||||
"total_lines": 1672,
|
||||
"total_cells": 15,
|
||||
"avg_cell_length": 86.86666666666666,
|
||||
"scaffolding_quality": 2,
|
||||
"complexity_distribution": {
|
||||
"1": 2,
|
||||
"2": 0,
|
||||
"3": 9,
|
||||
"4": 4,
|
||||
"5": 0
|
||||
},
|
||||
"learning_progression_quality": 1,
|
||||
"concepts_covered": [
|
||||
"Numerical Precision",
|
||||
"Multivariable Chain Rule",
|
||||
"Step 4: Testing Basic Operations",
|
||||
"Step 7: Integration Testing",
|
||||
"Forward pass",
|
||||
"Returns",
|
||||
"Backward pass",
|
||||
"Subtraction",
|
||||
"Variable",
|
||||
"Use case",
|
||||
"Computational Efficiency",
|
||||
"Inefficient",
|
||||
"Process",
|
||||
"Step 5: Chain Rule Testing",
|
||||
"What You've Accomplished",
|
||||
"Data",
|
||||
"grad_fn",
|
||||
"Chain rule",
|
||||
"Computational efficiency",
|
||||
"Gradient Checkpointing",
|
||||
"Gradient Accumulation",
|
||||
"requires_grad",
|
||||
"x",
|
||||
"Flexibility",
|
||||
"Foundation:",
|
||||
"Run the test",
|
||||
"Production:",
|
||||
"Loss",
|
||||
"Step 11: Complete ML Pipeline Example",
|
||||
"7",
|
||||
"10",
|
||||
"6",
|
||||
"Import our existing components",
|
||||
"5",
|
||||
"Step 10: Advanced Testing",
|
||||
"2 + y.grad.data.data.item()",
|
||||
"Computational Graph",
|
||||
"modules",
|
||||
"Use",
|
||||
"TODO",
|
||||
"Build",
|
||||
"4",
|
||||
"Learning Side:",
|
||||
"Final package structure:",
|
||||
"Analyze",
|
||||
"11",
|
||||
"Step 3: Basic Operations with Gradients",
|
||||
"Addition",
|
||||
"Gradient Visualization",
|
||||
"Step 6: Activation Function Gradients",
|
||||
"Building Side:",
|
||||
"9"
|
||||
],
|
||||
"todo_count": 5,
|
||||
"hint_count": 5,
|
||||
"test_count": 1,
|
||||
"critical_issues": [
|
||||
"Module too long (1672 lines) - students will be overwhelmed",
|
||||
"Sudden complexity jumps will overwhelm students",
|
||||
"9 cells are too long (>50 lines)"
|
||||
],
|
||||
"overwhelm_points": [
|
||||
"Cell 1: Too many concepts (5)",
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Very long cell (187 lines)",
|
||||
"Cell 4: High complexity without guidance",
|
||||
"Cell 4: Multiple functions in one cell (3)",
|
||||
"Cell 5: Very long cell (127 lines)",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Multiple functions in one cell (10)",
|
||||
"Cell 5: Complex implementation without error handling guidance",
|
||||
"Cell 6: Very long cell (205 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Multiple functions in one cell (8)",
|
||||
"Cell 6: Complex implementation without error handling guidance",
|
||||
"Cell 7: Very long cell (52 lines)",
|
||||
"Cell 8: Long cell (42 lines)",
|
||||
"Cell 9: Very long cell (82 lines)",
|
||||
"Cell 9: Multiple functions in one cell (4)",
|
||||
"Cell 9: Complex implementation without error handling guidance",
|
||||
"Cell 10: Long cell (46 lines)",
|
||||
"Cell 11: Long cell (39 lines)",
|
||||
"Cell 11: High complexity without guidance",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 12: Very long cell (204 lines)",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Multiple functions in one cell (10)",
|
||||
"Cell 12: Complex implementation without error handling guidance",
|
||||
"Cell 13: Very long cell (105 lines)",
|
||||
"Cell 13: Multiple functions in one cell (3)",
|
||||
"Cell 13: Complex implementation without error handling guidance",
|
||||
"Cell 14: Very long cell (98 lines)",
|
||||
"Cell 15: Very long cell (63 lines)"
|
||||
],
|
||||
"recommendations": [
|
||||
"Break module into smaller sections or multiple modules",
|
||||
"Add implementation ladders: break complex functions into 3 progressive steps",
|
||||
"Add concept bridges: connect new ideas to familiar concepts",
|
||||
"Include confidence builders: early wins to build momentum",
|
||||
"Split 12 long cells into smaller, focused cells",
|
||||
"Add immediate feedback tests after implementations"
|
||||
],
|
||||
"cell_analyses": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"line_count": 9,
|
||||
"char_count": 180,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "demonstration",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 28,
|
||||
"char_count": 1536,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "explanation",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Analyze",
|
||||
"Use",
|
||||
"7",
|
||||
"Build",
|
||||
"Import our existing components"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 16,
|
||||
"char_count": 705,
|
||||
"complexity_score": 1,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Final package structure:",
|
||||
"Foundation:",
|
||||
"Production:",
|
||||
"Building Side:",
|
||||
"Learning Side:"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 187,
|
||||
"char_count": 8123,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Multivariable Chain Rule",
|
||||
"Use case",
|
||||
"Computational Efficiency",
|
||||
"Inefficient",
|
||||
"Process"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (187 lines)",
|
||||
"High complexity without guidance",
|
||||
"Multiple functions in one cell (3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 127,
|
||||
"char_count": 5680,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Variable",
|
||||
"Data",
|
||||
"grad_fn",
|
||||
"requires_grad",
|
||||
"Flexibility"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (127 lines)",
|
||||
"Too many concepts (5)",
|
||||
"Multiple functions in one cell (10)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 205,
|
||||
"char_count": 8423,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Forward pass",
|
||||
"Step 3: Basic Operations with Gradients",
|
||||
"Addition",
|
||||
"Subtraction",
|
||||
"Returns"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (205 lines)",
|
||||
"Too many concepts (5)",
|
||||
"Multiple functions in one cell (8)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 52,
|
||||
"char_count": 3001,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Run the test",
|
||||
"Step 4: Testing Basic Operations",
|
||||
"4"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (52 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 42,
|
||||
"char_count": 2199,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Run the test",
|
||||
"Step 5: Chain Rule Testing",
|
||||
"5"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (42 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 82,
|
||||
"char_count": 3253,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Returns",
|
||||
"TODO",
|
||||
"Step 6: Activation Function Gradients",
|
||||
"6",
|
||||
"x"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (82 lines)",
|
||||
"Multiple functions in one cell (4)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 46,
|
||||
"char_count": 2612,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Run the test",
|
||||
"Step 7: Integration Testing",
|
||||
"7"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (46 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 39,
|
||||
"char_count": 2096,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Backward pass",
|
||||
"Forward pass",
|
||||
"What You've Accomplished",
|
||||
"Chain rule",
|
||||
"Computational efficiency"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Long cell (39 lines)",
|
||||
"High complexity without guidance",
|
||||
"Too many concepts (5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 204,
|
||||
"char_count": 8229,
|
||||
"complexity_score": 4,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Numerical Precision",
|
||||
"Returns",
|
||||
"Gradient Checkpointing",
|
||||
"Gradient Accumulation",
|
||||
"x"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (204 lines)",
|
||||
"Too many concepts (5)",
|
||||
"Multiple functions in one cell (10)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 105,
|
||||
"char_count": 4075,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": true,
|
||||
"has_hints": true,
|
||||
"concepts_introduced": [
|
||||
"Computational Graph",
|
||||
"9",
|
||||
"modules",
|
||||
"Gradient Visualization",
|
||||
"Returns"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (105 lines)",
|
||||
"Multiple functions in one cell (3)",
|
||||
"Complex implementation without error handling guidance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 98,
|
||||
"char_count": 4826,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"Step 10: Advanced Testing",
|
||||
"Run the test",
|
||||
"2 + y.grad.data.data.item()",
|
||||
"10"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (98 lines)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"line_count": 63,
|
||||
"char_count": 3128,
|
||||
"complexity_score": 3,
|
||||
"educational_type": "concept_introduction",
|
||||
"has_todo": false,
|
||||
"has_hints": false,
|
||||
"concepts_introduced": [
|
||||
"11",
|
||||
"Run the test",
|
||||
"Step 11: Complete ML Pipeline Example",
|
||||
"Loss"
|
||||
],
|
||||
"overwhelm_factors": [
|
||||
"Very long cell (63 lines)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"overall_grade": "D",
|
||||
"category_grades": {
|
||||
"Scaffolding": "D",
|
||||
"Complexity": "B",
|
||||
"Cell_Length": "F"
|
||||
},
|
||||
"vs_targets": {
|
||||
"Length": "\u274c Too long (1672 lines, target: 200-400)",
|
||||
"Cell_Length": "\u274c Too long (86.9 avg, target: \u226430)",
|
||||
"Complexity": "\u2705 Good (26.7% high-complexity)"
|
||||
},
|
||||
"vs_best_practices": [
|
||||
"Cell 2: Too many concepts (5)",
|
||||
"Cell 3: Too many concepts (5)",
|
||||
"Cell 4: Too many concepts (5)",
|
||||
"Cell 4: Too long (187 lines)",
|
||||
"Cell 4: High complexity without guidance",
|
||||
"Cell 5: Too many concepts (5)",
|
||||
"Cell 5: Too long (127 lines)",
|
||||
"Cell 6: Too many concepts (5)",
|
||||
"Cell 6: Too long (205 lines)",
|
||||
"Cell 7: Too long (52 lines)",
|
||||
"Cell 8: Too long (42 lines)",
|
||||
"Cell 9: Too many concepts (5)",
|
||||
"Cell 9: Too long (82 lines)",
|
||||
"Cell 10: Too long (46 lines)",
|
||||
"Cell 11: Too many concepts (5)",
|
||||
"Cell 11: Too long (39 lines)",
|
||||
"Cell 11: High complexity without guidance",
|
||||
"Cell 12: Too many concepts (5)",
|
||||
"Cell 12: Too long (204 lines)",
|
||||
"Cell 13: Too many concepts (5)",
|
||||
"Cell 13: Too long (105 lines)",
|
||||
"Cell 14: Too many concepts (4)",
|
||||
"Cell 14: Too long (98 lines)",
|
||||
"Cell 15: Too many concepts (4)",
|
||||
"Cell 15: Too long (63 lines)",
|
||||
"Cells 3-4: Complexity cliff (1\u21924)"
|
||||
]
|
||||
}
|
||||
@@ -1,42 +0,0 @@
|
||||
[DEFAULT]
|
||||
# All sections below are required unless otherwise specified.
|
||||
# See https://github.com/fastai/nbdev/blob/master/settings.ini for examples.
|
||||
|
||||
### Python library ###
|
||||
repo = TinyTorch
|
||||
lib_name = tinytorch
|
||||
version = 0.1.0
|
||||
min_python = 3.8
|
||||
license = apache2
|
||||
black_formatting = False
|
||||
|
||||
### nbdev ###
|
||||
doc_path = _docs
|
||||
lib_path = tinytorch
|
||||
nbs_path = assignments/source
|
||||
recursive = True
|
||||
tst_flags = notest
|
||||
put_version_in_init = True
|
||||
|
||||
### Docs ###
|
||||
branch = main
|
||||
custom_sidebar = False
|
||||
doc_host = https://tinytorch.github.io
|
||||
doc_baseurl = /TinyTorch/
|
||||
git_url = https://github.com/tinytorch/TinyTorch/
|
||||
title = TinyTorch
|
||||
|
||||
### PyPI ###
|
||||
audience = Developers
|
||||
author = TinyTorch Team
|
||||
author_email = team@tinytorch.org
|
||||
copyright = 2024 onwards, TinyTorch Team
|
||||
description = Build ML Systems from Scratch - A hands-on systems course
|
||||
keywords = machine learning, deep learning, systems, education
|
||||
language = English
|
||||
status = 3
|
||||
user = tinytorch
|
||||
|
||||
### Optional ###
|
||||
requirements = numpy>=1.20.0 matplotlib>=3.3.0 rich>=10.0.0 jupyter>=1.0.0 pytest>=6.0.0
|
||||
dev_requirements = nbdev>=2.3.0 black>=22.0.0
|
||||
@@ -1,79 +0,0 @@
|
||||
# My Project Model Performance Report
|
||||
|
||||
## Executive Summary
|
||||
|
||||
This report presents comprehensive performance benchmarking results for My Project Model using MLPerf-inspired methodology. The evaluation covers three standard scenarios: single-stream (latency), server (throughput), and offline (batch processing).
|
||||
|
||||
### Key Findings
|
||||
- **Single Stream**: 95.00 samples/sec, 10.19ms mean latency, 8.61ms 90th percentile
|
||||
- **Server**: 87.00 samples/sec, 12.14ms mean latency, 8.06ms 90th percentile
|
||||
- **Offline**: 120.00 samples/sec, 7.84ms mean latency, 7.29ms 90th percentile
|
||||
|
||||
## Methodology
|
||||
|
||||
### Benchmark Framework
|
||||
- **Architecture**: MLPerf-inspired four-component system
|
||||
- **Scenarios**: Single-stream, server, and offline evaluation
|
||||
- **Statistical Validation**: Multiple runs with confidence intervals
|
||||
- **Metrics**: Latency distribution, throughput, accuracy
|
||||
|
||||
### Test Environment
|
||||
- **Hardware**: Standard development machine
|
||||
- **Software**: TinyTorch framework
|
||||
- **Dataset**: Standardized evaluation dataset
|
||||
- **Validation**: Statistical significance testing
|
||||
|
||||
## Detailed Results
|
||||
|
||||
### Single Stream Scenario
|
||||
|
||||
- **Sample Count**: 100
|
||||
- **Mean Latency**: 10.19 ms
|
||||
- **Median Latency**: 10.05 ms
|
||||
- **90th Percentile**: 8.61 ms
|
||||
- **95th Percentile**: 10.85 ms
|
||||
- **Standard Deviation**: 2.05 ms
|
||||
- **Throughput**: 95.00 samples/second
|
||||
- **Accuracy**: 0.9420
|
||||
|
||||
### Server Scenario
|
||||
|
||||
- **Sample Count**: 150
|
||||
- **Mean Latency**: 12.14 ms
|
||||
- **Median Latency**: 11.91 ms
|
||||
- **90th Percentile**: 8.06 ms
|
||||
- **95th Percentile**: 9.67 ms
|
||||
- **Standard Deviation**: 3.03 ms
|
||||
- **Throughput**: 87.00 samples/second
|
||||
- **Accuracy**: 0.9380
|
||||
|
||||
### Offline Scenario
|
||||
|
||||
- **Sample Count**: 50
|
||||
- **Mean Latency**: 7.84 ms
|
||||
- **Median Latency**: 7.86 ms
|
||||
- **90th Percentile**: 7.29 ms
|
||||
- **95th Percentile**: 8.55 ms
|
||||
- **Standard Deviation**: 1.14 ms
|
||||
- **Throughput**: 120.00 samples/second
|
||||
- **Accuracy**: 0.9450
|
||||
|
||||
## Statistical Validation
|
||||
|
||||
All results include proper statistical validation:
|
||||
- Multiple independent runs for reliability
|
||||
- Confidence intervals for key metrics
|
||||
- Outlier detection and handling
|
||||
- Significance testing for comparisons
|
||||
|
||||
## Recommendations
|
||||
|
||||
Based on the benchmark results:
|
||||
1. **Performance Characteristics**: Model shows consistent performance across scenarios
|
||||
2. **Optimization Opportunities**: Focus on reducing tail latency for production deployment
|
||||
3. **Scalability**: Server scenario results indicate good potential for production scaling
|
||||
4. **Further Testing**: Consider testing with larger datasets and different hardware configurations
|
||||
|
||||
## Conclusion
|
||||
|
||||
This comprehensive benchmarking demonstrates {model_name}'s performance characteristics using industry-standard methodology. The results provide a solid foundation for production deployment decisions and further optimization efforts.
|
||||
Reference in New Issue
Block a user