mirror of
https://github.com/MLSysBook/TinyTorch.git
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a9fed98b66
- Removed temporary test files and audit reports - Deleted backup and temp_holding directories - Reorganized module structure (07->09 spatial, 09->07 dataloader) - Added new modules: 11-14 (tokenization, embeddings, attention, transformers) - Updated examples with historical ML milestones - Cleaned up documentation structure
108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
"""
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Integration Tests for Module 04: Layers
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========================================
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These tests run automatically when you complete Module 04 with:
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`tito module complete 04_layers`
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They verify that layers work correctly with other completed modules.
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"""
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import numpy as np
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
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def test_layers_integration():
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"""Test that layers integrate with tensors and activations."""
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print("Running Module 04 Integration Tests...")
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print("-" * 40)
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# Test 1: Layers work with Tensors
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print("Test 1: Layer + Tensor integration")
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Dense
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layer = Dense(3, 2)
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x = Tensor(np.random.randn(5, 3))
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output = layer(x)
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assert output.shape == (5, 2), f"Expected shape (5, 2), got {output.shape}"
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print("✅ Layers work with Tensors")
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except Exception as e:
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print(f"❌ Layer-Tensor integration failed: {e}")
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return False
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# Test 2: Layers work with Activations
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print("Test 2: Layer + Activation integration")
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try:
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from tinytorch.core.activations import ReLU, Sigmoid
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layer1 = Dense(4, 8)
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relu = ReLU()
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layer2 = Dense(8, 4)
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sigmoid = Sigmoid()
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x = Tensor(np.random.randn(2, 4))
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h = relu(layer1(x))
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y = sigmoid(layer2(h))
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assert y.shape == (2, 4), f"Expected shape (2, 4), got {y.shape}"
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print("✅ Layers work with Activations")
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except Exception as e:
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print(f"❌ Layer-Activation integration failed: {e}")
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return False
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# Test 3: Multi-layer stacking
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print("Test 3: Multi-layer network construction")
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try:
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layers = [
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Dense(10, 20),
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ReLU(),
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Dense(20, 15),
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ReLU(),
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Dense(15, 5)
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]
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x = Tensor(np.random.randn(3, 10))
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for layer in layers:
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x = layer(x)
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assert x.shape == (3, 5), f"Expected final shape (3, 5), got {x.shape}"
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print("✅ Multi-layer networks work")
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except Exception as e:
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print(f"❌ Multi-layer stacking failed: {e}")
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return False
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# Test 4: Parameter access
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print("Test 4: Parameter management")
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try:
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layer = Dense(5, 3)
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assert hasattr(layer, 'weights'), "Layer missing weights"
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assert hasattr(layer, 'bias'), "Layer missing bias"
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assert layer.weights.shape == (5, 3), f"Wrong weight shape: {layer.weights.shape}"
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assert layer.bias.shape == (3,), f"Wrong bias shape: {layer.bias.shape}"
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total_params = layer.weights.size + layer.bias.size
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assert total_params == 18, f"Expected 18 parameters, got {total_params}"
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print("✅ Parameter management works")
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except Exception as e:
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print(f"❌ Parameter management failed: {e}")
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return False
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print("-" * 40)
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print("✅ All Module 04 integration tests passed!")
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print()
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print("🎯 CAPABILITY UNLOCKED: Network Architecture Construction")
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print("📚 You can now run: python examples/perceptron_1957/rosenblatt_perceptron.py")
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print()
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return True
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if __name__ == "__main__":
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success = test_layers_integration()
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exit(0 if success else 1) |