- Tests activation function integration with Tensor class operations
- Validates that activations preserve Tensor types in neural network contexts
- Tests matrix operations for multi-dimensional neural network layers
- Verifies softmax probability distributions for classification scenarios
- Tests chaining tensor operations with activations for complete workflows
- Positioned before MODULE SUMMARY as per educational structure
- Tests tensor integration with NumPy arrays and operations
- Validates tensor-NumPy compatibility for scientific computing
- Ensures broadcasting works correctly between tensors and scalars
- Verifies integration with NumPy functions on tensor data
- Positioned before MODULE SUMMARY as per educational structure
Updates the name of the unit test function for training
integration to improve clarity and consistency.
This change ensures the test function name accurately
reflects its purpose.
Stops the automatic execution of the integration test.
This change prevents the test from running every time the module is loaded,
allowing for more focused and controlled testing.
Removes redundant "DEVELOPMENT" headers from several notebook files.
These headers are no longer necessary and declutter the notebook content, improving readability and focus on the core content and testing sections.
✅ Standardized test explanations with consistent format
📝 Added markdown cells before all test functions
🎯 Improved educational clarity for student understanding
Changes:
- 01_setup: Added 2 test explanations (personal_info, system_info)
- 02_tensor: Added 3 test explanations (creation, properties, arithmetic)
- 12_compression: Added 8 test explanations (metrics, pruning, quantization, distillation, etc.)
All 15 modules now follow standardized test documentation pattern:
### 🧪 Unit Test: [Component Name]
[Brief explanation of validation purpose]
Ensures every test has clear educational context for students.
✅ COMPLETED: Systematic review and fixes for ALL 15 modules with _dev.py files
📊 SUMMARY OF WORK:
• 01_setup: Added missing test function calls
• 02_tensor: Added missing test function calls
• 03_activations: Already had all calls ✓
• 04_layers: Fixed function name mismatches
• 05_dense: Added missing test function calls
• 06_spatial: Added missing test function calls
• 07_attention: Added missing test function calls
• 08_dataloader: Already had all calls ✓
• 09_autograd: Added missing calls + fixed name mismatches
• 10_optimizers: Added missing test function call
• 11_training: Added missing calls + fixed name mismatches
• 12_compression: Added missing calls + fixed name mismatches
• 13_kernels: Fixed function name mismatches
• 14_benchmarking: Added missing calls + fixed name mismatches
• 15_mlops: Fixed function name mismatches
🔧 CHANGES MADE:
• Added test function calls after function definitions where missing
• Fixed function name mismatches (e.g., test_xxx() → test_unit_xxx())
• Ensured every test function is actually executed when cells run
🎯 RESULT: Students now get immediate feedback from all test functions!
Every test function defined in the modules is now properly called,
providing real-time validation and educational feedback to students.
- Added test_unit_benchmark_scenarios() call after function definition
- Fixed test_statistical_validation() → test_unit_statistical_validation()
- Added test_unit_tinytorch_perf() call after function definition
- Fixed test_performance_reporter() → test_unit_performance_reporter()
- Fixed test_comprehensive_benchmarking() → test_module_comprehensive_benchmarking()
Ensures all test functions are executed when cells run, providing immediate feedback to students.
- Added test_unit_compression_metrics() call and fixed name mismatch
- Added test_unit_magnitude_pruning() call and fixed name mismatch
- Added test_unit_quantization() call and fixed name mismatch
- Added test_unit_distillation() call and fixed name mismatch
- Added test_unit_structured_pruning() call and fixed name mismatch
- Added test_unit_comprehensive_comparison() call and fixed name mismatch
Ensures all test functions are executed when cells run, providing immediate feedback to students.
- Added test_unit_mse_loss() call after function definition
- Added test_unit_crossentropy_loss() call after function definition
- Added test_unit_binary_crossentropy_loss() call after function definition
- Fixed test_accuracy_metric() → test_unit_accuracy_metric()
- Fixed test_trainer() → test_unit_trainer()
- Fixed test_training() → test_module_training()
Ensures all test functions are executed when cells run, providing immediate feedback to students.
- Added test_unit_gradient_descent_step() call after function definition
Ensures the gradient descent step test function is executed when cells run, providing immediate feedback to students.
- Added test_unit_variable_class() call after function definition
- Added test_unit_add_operation() call after function definition
- Added test_unit_multiply_operation() call after function definition
- Added test_unit_subtract_operation() call after function definition
- Added test_unit_chain_rule() call after function definition
- Added test_module_neural_network_training() call after function definition
- Fixed function name mismatches in existing if __name__ == '__main__' calls
Ensures all test functions are executed when cells run, providing immediate feedback to students.
- Added test_unit_attention_mechanism() call after function definition
- Added test_unit_self_attention_wrapper() call after function definition
- Added test_unit_masking_utilities() call after function definition
Ensures all test functions are executed when cells run, providing immediate feedback to students.
- Added test_unit_convolution_operation() call after function definition
- Added test_unit_conv2d_layer() call after function definition
- Added test_unit_flatten_function() call after function definition
Ensures all test functions are executed when cells run, providing immediate feedback to students.
