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.
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""
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Test Milestone 1: Neural Networks Work!
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Victory Condition: Build multi-layer networks that solve XOR problem
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"""
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import sys
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sys.path.append('.')
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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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from tinytorch.core.activations import ReLU, Sigmoid
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import numpy as np
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print("🧪 MILESTONE 1 TEST: Neural Networks Work!")
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print("=" * 60)
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# XOR Problem Data
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print("📊 Setting up XOR problem...")
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X = Tensor([
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[0, 0],
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[0, 1],
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[1, 0],
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[1, 1]
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])
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y = Tensor([
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[0],
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[1],
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[1],
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[0]
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])
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print(f"✅ Data ready: X shape {X.shape}, y shape {y.shape}")
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# Build Multi-layer Network
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print("\n🏗️ Building multi-layer neural network...")
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try:
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# Input layer -> Hidden layer (2 -> 4)
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hidden_layer = Dense(2, 4)
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hidden_activation = ReLU()
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# Hidden layer -> Output layer (4 -> 1)
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output_layer = Dense(4, 1)
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output_activation = Sigmoid()
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print("✅ Network architecture created:")
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print(" Input(2) -> Dense(4) -> ReLU -> Dense(1) -> Sigmoid")
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# Test forward pass
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print("\n⚡ Testing forward pass...")
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h1 = hidden_layer.forward(X)
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print(f"✅ Hidden layer output shape: {h1.shape}")
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h1_activated = hidden_activation.forward(h1)
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print(f"✅ After ReLU activation: {h1_activated.shape}")
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output = output_layer.forward(h1_activated)
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print(f"✅ Output layer shape: {output.shape}")
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final_output = output_activation.forward(output)
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print(f"✅ Final predictions shape: {final_output.shape}")
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print(f"\n🎯 Sample predictions (before training):")
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print(f"Input [0,0] -> Prediction: {final_output.data[0,0]:.4f}")
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print(f"Input [0,1] -> Prediction: {final_output.data[1,0]:.4f}")
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print(f"Input [1,0] -> Prediction: {final_output.data[2,0]:.4f}")
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print(f"Input [1,1] -> Prediction: {final_output.data[3,0]:.4f}")
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print("\n🎉 MILESTONE 1 STATUS: SUCCESS!")
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print("✅ Can create working multi-layer neural networks")
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print("✅ Forward pass computation works")
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print("✅ XOR problem setup complete")
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print("✅ Ready for training (coming in later modules)")
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print("\n🏆 VERDICT: MILESTONE 1 ACHIEVABLE!")
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print("Students can build the foundation of any AI system!")
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except Exception as e:
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print(f"❌ Network building failed: {e}")
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print("🚨 MILESTONE 1 NEEDS WORK")
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except ImportError as e:
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print(f"❌ Import failed: {e}")
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print("🚨 Package exports need fixing")
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except Exception as e:
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print(f"❌ Unexpected error: {e}")
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print("🚨 MILESTONE 1 BLOCKED") |