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https://github.com/MLSysBook/TinyTorch.git
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Features: - 16 checkpoint test suite validating ML systems capabilities - Integration tests covering complete learning progression - Rich CLI progress tracking with visual timelines - Capability-driven assessment from environment to production Checkpoints: - Environment setup through full ML system deployment - Each checkpoint validates integrated functionality - Progressive capability building with clear success criteria - Professional CLI interface with status/timeline/test commands
93 lines
4.2 KiB
Python
93 lines
4.2 KiB
Python
"""
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Checkpoint 2: Intelligence (After Module 3 - Activations)
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Question: "Can I add nonlinearity - the key to neural network intelligence?"
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"""
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import numpy as np
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import pytest
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def test_checkpoint_02_intelligence():
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"""
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Checkpoint 2: Intelligence
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Validates that students can apply activation functions to create nonlinear
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transformations - the key breakthrough that enables neural networks to
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learn complex patterns and exhibit intelligence.
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"""
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print("\n🧠 Checkpoint 2: Intelligence")
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print("=" * 50)
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.activations import ReLU, Sigmoid, Tanh, Softmax
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except ImportError as e:
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pytest.fail(f"❌ Cannot import required classes - complete Modules 2-3 first: {e}")
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# Test 1: ReLU - Sparsity and efficiency
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print("⚡ Testing ReLU activation...")
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data = Tensor([-2, -1, 0, 1, 2])
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relu = ReLU()
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relu_output = relu(data)
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expected_relu = np.array([0, 0, 0, 1, 2])
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assert np.array_equal(relu_output.data, expected_relu), f"ReLU failed: expected {expected_relu}, got {relu_output.data}"
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print(f"✅ ReLU creates sparsity: {data.data} → {relu_output.data}")
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# Test 2: Sigmoid - Probability outputs
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print("📊 Testing Sigmoid activation...")
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sigmoid = Sigmoid()
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sigmoid_output = sigmoid(data)
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# Sigmoid should output values between 0 and 1
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assert np.all(sigmoid_output.data >= 0) and np.all(sigmoid_output.data <= 1), "Sigmoid outputs should be in [0,1]"
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print(f"✅ Sigmoid creates probabilities: {data.data} → {sigmoid_output.data}")
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# Test 3: Tanh - Zero-centered outputs
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print("🎯 Testing Tanh activation...")
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tanh = Tanh()
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tanh_output = tanh(data)
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# Tanh should output values between -1 and 1
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assert np.all(tanh_output.data >= -1) and np.all(tanh_output.data <= 1), "Tanh outputs should be in [-1,1]"
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assert abs(tanh_output.data[2]) < 1e-6, "Tanh(0) should be approximately 0"
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print(f"✅ Tanh is zero-centered: {data.data} → {tanh_output.data}")
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# Test 4: Softmax - Probability distributions
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print("🎲 Testing Softmax activation...")
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logits = Tensor([1.0, 2.0, 3.0])
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softmax = Softmax()
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softmax_output = softmax(logits)
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# Softmax should sum to 1 and all values should be positive
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assert abs(np.sum(softmax_output.data) - 1.0) < 1e-6, f"Softmax should sum to 1, got {np.sum(softmax_output.data)}"
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assert np.all(softmax_output.data > 0), "All softmax outputs should be positive"
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print(f"✅ Softmax creates distribution: {logits.data} → {softmax_output.data} (sum={np.sum(softmax_output.data):.3f})")
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# Test 5: Chaining activations (nonlinear intelligence)
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print("🔗 Testing chained nonlinear transformations...")
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complex_data = Tensor([-3, -1, 0, 1, 3])
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# Apply multiple transformations: data → ReLU → Sigmoid
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step1 = relu(complex_data) # Remove negative values
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intelligent_output = sigmoid(step1) # Convert to probabilities
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assert np.all(intelligent_output.data >= 0) and np.all(intelligent_output.data <= 1), "Chained output should be valid probabilities"
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print(f"✅ Chained intelligence: {complex_data.data} → ReLU → Sigmoid → {intelligent_output.data}")
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# Test 6: Batch processing (multiple samples)
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print("📦 Testing batch processing...")
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batch_data = Tensor([[-1, 0, 1], [2, -2, 0]]) # 2 samples, 3 features each
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batch_output = relu(batch_data)
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expected_batch = np.array([[0, 0, 1], [2, 0, 0]])
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assert np.array_equal(batch_output.data, expected_batch), f"Batch ReLU failed: expected {expected_batch}, got {batch_output.data}"
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print(f"✅ Batch processing: {batch_data.shape} → {batch_output.shape}")
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print("\n🎉 Intelligence Complete!")
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print("📝 You can now add nonlinearity - the key to neural network intelligence")
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print("🔧 Built capabilities: ReLU, Sigmoid, Tanh, Softmax, chained activations, batch processing")
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print("🧠 Breakthrough: Networks can now learn complex, nonlinear patterns!")
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print("🎯 Next: Build learnable neural network components")
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if __name__ == "__main__":
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test_checkpoint_02_intelligence() |