mirror of
https://github.com/MLSysBook/TinyTorch.git
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9361cbf987
- Create professional examples directory showcasing TinyTorch as real ML framework - Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison - Fix import paths in exported modules (training.py, dense.py) - Update training module with autograd integration for loss functions - Add progressive integration tests for all 16 modules - Document framework capabilities and usage patterns This commit establishes the examples gallery that demonstrates TinyTorch works like PyTorch/TensorFlow, validating the complete framework.
47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
"""
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Core Functionality Tests for Module XX: [Module Name]
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Template for testing core module functionality
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"""
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import numpy as np
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import sys
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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class TestCoreFeatures:
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"""Test core functionality of the module."""
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def test_basic_creation(self):
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"""Test basic object creation."""
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# Example:
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# from tinytorch.core.module import MainClass
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# obj = MainClass()
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# assert obj is not None
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pass
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def test_core_operation(self):
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"""Test main operation of the module."""
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# Example:
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# obj = MainClass()
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# result = obj.process(input_data)
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# assert result.shape == expected_shape
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pass
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def test_edge_cases(self):
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"""Test edge cases and boundary conditions."""
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# Example:
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# obj = MainClass()
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# # Test with empty input
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# result = obj.process([])
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# assert result is not None
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pass
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def test_error_handling(self):
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"""Test proper error handling."""
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# Example:
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# with pytest.raises(ValueError):
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# obj = MainClass(invalid_param=-1)
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pass |