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f842d0c774
Milestone 02 Structure (matches milestone 01): - README.md: Comprehensive guide with historical context - xor_crisis.py: Part 1 - demonstrates single-layer failure (executable) - xor_solved.py: Part 2 - demonstrates multi-layer success (executable) Cleanup: - ✅ Removed old perceptron_xor_fails.py - ✅ Moved test files to tests/integration/ - test_xor_simple.py - test_xor_thorough.py - test_xor_original_1986.py (verifies 2-2-1 architecture works!) - ✅ Updated README with clear instructions - ✅ Made scripts executable Milestone 02 now has the same polish and structure as milestone 01: - Clear file naming (crisis vs solved) - Beautiful rich output - Historical context - Pedagogically structured
59 lines
1.4 KiB
Python
59 lines
1.4 KiB
Python
#!/usr/bin/env python3
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"""
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Simple XOR test to verify multi-layer networks work
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"""
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import sys
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sys.path.insert(0, '.')
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import numpy as np
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from tinytorch import Tensor, Linear, ReLU, Sigmoid, BinaryCrossEntropyLoss, SGD
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print("=" * 70)
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print("🧪 Testing Multi-Layer Network on XOR Problem")
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print("=" * 70)
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# XOR dataset
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X_data = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])
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y_data = np.array([[0.0], [1.0], [1.0], [0.0]])
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X = Tensor(X_data)
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y = Tensor(y_data)
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# Build network
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hidden = Linear(2, 4)
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relu = ReLU()
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output = Linear(4, 1)
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sigmoid = Sigmoid()
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loss_fn = BinaryCrossEntropyLoss()
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optimizer = SGD([p for p in hidden.parameters()] + [p for p in output.parameters()], lr=0.5)
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print("\n🔥 Training on XOR...")
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for epoch in range(500):
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# Forward
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h = hidden(X)
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h_act = relu(h)
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out = output(h_act)
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pred = sigmoid(out)
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loss = loss_fn(pred, y)
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# Backward
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loss.backward()
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# Update
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optimizer.step()
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optimizer.zero_grad()
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if (epoch + 1) % 100 == 0:
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accuracy = ((pred.data > 0.5).astype(float) == y.data).mean()
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print(f"Epoch {epoch+1:3d}/500 Loss: {loss.data:.4f} Accuracy: {accuracy:.1%}")
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print("\n✅ Final predictions:")
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for i in range(4):
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pred_val = (pred.data[i, 0] > 0.5).astype(int)
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print(f" Input: {X_data[i]} → Predicted: {pred_val} (Expected: {int(y_data[i, 0])})")
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print("\n" + "=" * 70)
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print("🎉 Multi-layer network working!")
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print("=" * 70)
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