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.
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
"""
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Capability Tests for Module XX: [Module Name]
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Template for testing that the module enables its intended capabilities
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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 TestModuleCapabilities:
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"""Test that the module enables its promised capabilities."""
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def test_solves_intended_problem(self):
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"""Test the module can solve its primary use case."""
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# Example for Conv2D:
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# from tinytorch.core.spatial import Conv2D
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#
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# conv = Conv2D(3, 32, kernel_size=3)
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# image = Tensor(np.random.randn(1, 28, 28, 3))
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# features = conv(image)
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#
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# # Should extract features
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# assert features.shape[-1] == 32 # 32 feature maps
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pass
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def test_real_world_scenario(self):
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"""Test a real-world usage scenario."""
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# Example for Optimizer:
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# from tinytorch.core.optimizers import Adam
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# from tinytorch.core.layers import Dense
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#
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# layer = Dense(10, 5)
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# optimizer = Adam(learning_rate=0.001)
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#
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# # Simulate training step
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# loss = compute_loss()
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# gradients = compute_gradients()
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# optimizer.step(layer.parameters(), gradients)
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#
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# # Parameters should be updated
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# assert parameters_changed
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pass
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def test_performance_acceptable(self):
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"""Test that performance is acceptable."""
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# Example:
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# import time
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#
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# start = time.time()
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# result = expensive_operation()
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# duration = time.time() - start
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#
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# assert duration < 1.0 # Should complete in under 1 second
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pass |