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🎯 NORTH STAR VISION DOCUMENTED: 'Don't Just Import It, Build It' - Training AI Engineers, not just ML users AI Engineering emerges as a foundational discipline like Computer Engineering, bridging algorithms and systems to build the AI infrastructure of the future. 🧪 ROBUST TESTING FRAMEWORK ESTABLISHED: - Created tests/regression/ for sandbox integrity tests - Implemented test-driven bug prevention workflow - Clear separation: student tests (pedagogical) vs system tests (robustness) - Every bug becomes a test to prevent recurrence ✅ KEY IMPLEMENTATIONS: - NORTH_STAR.md: Vision for AI Engineering discipline - Testing best practices: Focus on robust student sandbox - Git workflow standards: Professional development practices - Regression test suite: Prevent infrastructure issues - Conv->Linear dimension tests (found CNN bug) - Transformer reshaping tests (found GPT bug) 🏗️ SANDBOX INTEGRITY: Students need a solid, predictable environment where they focus on ML concepts, not debugging framework issues. The framework must be invisible. 📚 EDUCATIONAL PHILOSOPHY: TinyTorch isn't just teaching a framework - it's founding the AI Engineering discipline by training engineers who understand how to BUILD ML systems. This establishes the foundation for training the first generation of true AI Engineers who will define this emerging discipline.
297 lines
9.2 KiB
Python
297 lines
9.2 KiB
Python
#!/usr/bin/env python3
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"""
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CNN Integration Test - After Module 11
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======================================
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This test validates that modules 1-11 work together for CNN image classification.
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Required modules:
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- Module 01-08: Core MLP functionality (from MNIST test)
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- Module 09: Spatial operations (Conv2d, MaxPool2d)
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- Module 10: DataLoader for efficient batch processing
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- Module 11: CNN training capabilities
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This demonstrates the milestone: "Can train CNNs on CIFAR-10"
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import numpy as np
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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
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from tinytorch.core.training import CrossEntropyLoss
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# Try to import spatial operations
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try:
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from tinytorch.core.spatial import Conv2d, MaxPool2d, Flatten
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SPATIAL_AVAILABLE = True
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except ImportError:
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print("⚠️ Spatial operations not available - using placeholder tests")
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SPATIAL_AVAILABLE = False
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class SimpleCNN:
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"""Simple CNN for CIFAR-10 style classification."""
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def __init__(self, num_classes=10):
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if SPATIAL_AVAILABLE:
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# Convolutional layers
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self.conv1 = Conv2d(3, 32, kernel_size=3) # 3 channels -> 32 filters
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self.conv2 = Conv2d(32, 64, kernel_size=3) # 32 -> 64 filters
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self.pool = MaxPool2d(kernel_size=2)
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self.flatten = Flatten()
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# Dense layers
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self.fc1 = Dense(64 * 5 * 5, 256) # Assuming 32x32 input -> 5x5 after conv+pool
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self.fc2 = Dense(256, num_classes)
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else:
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# Fallback: treat as flattened MLP
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self.fc1 = Dense(32*32*3, 256)
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self.fc2 = Dense(256, num_classes)
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self.relu = ReLU()
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def forward(self, x):
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"""Forward pass."""
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if SPATIAL_AVAILABLE:
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# CNN path
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x = self.conv1(x)
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x = self.relu(x)
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x = self.pool(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.pool(x)
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x = self.flatten(x)
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else:
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# MLP path - flatten input
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if len(x.shape) == 4: # (batch, channels, height, width)
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batch_size = x.shape[0]
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x = Tensor(x.data.reshape(batch_size, -1))
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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return x
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def __call__(self, x):
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return self.forward(x)
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def parameters(self):
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"""Get all trainable parameters."""
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params = []
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if SPATIAL_AVAILABLE:
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if hasattr(self.conv1, 'parameters'):
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params.extend(self.conv1.parameters())
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if hasattr(self.conv2, 'parameters'):
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params.extend(self.conv2.parameters())
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params.extend([
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self.fc1.weights, self.fc1.bias,
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self.fc2.weights, self.fc2.bias
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])
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return params
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def generate_fake_cifar(num_samples=32, num_classes=10):
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"""Generate fake CIFAR-10 like data for testing."""
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np.random.seed(42)
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# Generate random 32x32x3 images
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X = np.random.randn(num_samples, 3, 32, 32).astype(np.float32)
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# Generate random labels
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y = np.random.randint(0, num_classes, size=(num_samples,)).astype(np.int64)
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return X, y
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def test_cnn_architecture():
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"""Test CNN architecture can handle image data."""
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print("🏗️ Testing CNN Architecture...")
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try:
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model = SimpleCNN(num_classes=10)
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# Create fake image batch: (batch_size, channels, height, width)
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batch_size = 8
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x = Tensor(np.random.randn(batch_size, 3, 32, 32).astype(np.float32))
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print(f" ✓ Created model and image batch")
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print(f" Input shape: {x.shape} (batch, channels, height, width)")
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# Forward pass
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output = model(x)
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print(f" ✓ Forward pass successful")
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print(f" Output shape: {output.shape}")
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expected_shape = (batch_size, 10)
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assert output.shape == expected_shape, f"Expected {expected_shape}, got {output.shape}"
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print("✅ CNN architecture working!")
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return True
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except Exception as e:
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print(f"❌ CNN architecture test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_spatial_operations():
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"""Test spatial operations if available."""
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print("🔍 Testing Spatial Operations...")
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if not SPATIAL_AVAILABLE:
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print(" ⚠️ Spatial operations not available - skipping")
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return True
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try:
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# Test Conv2d
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conv = Conv2d(3, 16, kernel_size=3)
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x = Tensor(np.random.randn(1, 3, 8, 8).astype(np.float32))
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conv_out = conv(x)
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print(f" ✓ Conv2d: {x.shape} -> {conv_out.shape}")
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# Test MaxPool2d
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pool = MaxPool2d(kernel_size=2)
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pool_out = pool(conv_out)
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print(f" ✓ MaxPool2d: {conv_out.shape} -> {pool_out.shape}")
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# Test Flatten
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flatten = Flatten()
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flat_out = flatten(pool_out)
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print(f" ✓ Flatten: {pool_out.shape} -> {flat_out.shape}")
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print("✅ Spatial operations working!")
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return True
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except Exception as e:
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print(f"❌ Spatial operations test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_cnn_training_step():
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"""Test CNN training step."""
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print("🏋️ Testing CNN Training Step...")
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try:
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# Create small CNN and fake CIFAR data
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model = SimpleCNN(num_classes=5)
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# Small batch
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x = Tensor(np.random.randn(4, 3, 16, 16).astype(np.float32)) # Smaller images
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y = Tensor(np.array([0, 1, 2, 3]))
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print(f" ✓ Created CNN model and data")
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print(f" Image batch shape: {x.shape}")
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print(f" Labels shape: {y.shape}")
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# Forward pass
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outputs = model(x)
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print(f" ✓ CNN forward pass: {x.shape} -> {outputs.shape}")
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# Loss computation
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criterion = CrossEntropyLoss()
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loss = criterion(outputs, y)
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print(f" ✓ Loss computation successful")
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print("✅ CNN training step working!")
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return True
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except Exception as e:
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print(f"❌ CNN training step failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_image_data_pipeline():
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"""Test image data processing pipeline."""
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print("📸 Testing Image Data Pipeline...")
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try:
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# Generate batch of fake CIFAR images
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X, y = generate_fake_cifar(num_samples=16)
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print(f" ✓ Generated fake image data")
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print(f" Images shape: {X.shape}")
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print(f" Labels shape: {y.shape}")
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# Convert to tensors
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X_tensor = Tensor(X)
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y_tensor = Tensor(y)
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print(f" ✓ Converted to tensors")
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# Test CNN can process this data
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model = SimpleCNN(num_classes=10)
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outputs = model(X_tensor)
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print(f" ✓ CNN processed image batch: {X_tensor.shape} -> {outputs.shape}")
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# Test loss computation
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criterion = CrossEntropyLoss()
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loss = criterion(outputs, y_tensor)
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print(f" ✓ Loss computation on image batch successful")
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print("✅ Image data pipeline working!")
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return True
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except Exception as e:
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print(f"❌ Image data pipeline failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def run_cnn_integration_test():
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"""Run complete CNN integration test."""
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print("=" * 60)
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print("🔥 CNN INTEGRATION TEST - Modules 1-11")
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print("=" * 60)
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print()
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success = True
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tests = [
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test_cnn_architecture,
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test_spatial_operations,
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test_cnn_training_step,
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test_image_data_pipeline
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]
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for test in tests:
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try:
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if not test():
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success = False
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print()
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except Exception as e:
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print(f"❌ Test failed with error: {e}")
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import traceback
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traceback.print_exc()
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success = False
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print()
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if success:
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print("🎉 CNN INTEGRATION TEST PASSED!")
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print()
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print("✅ Milestone Achieved: Can build CNNs for image classification")
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print(" • CNN architecture handles 4D image tensors")
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if SPATIAL_AVAILABLE:
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print(" • Spatial operations (Conv2d, MaxPool2d) work")
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else:
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print(" • Fallback MLP architecture works for images")
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print(" • Training pipeline supports image data")
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print(" • End-to-end image classification pipeline functional")
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print()
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print("🚀 Ready for Module 12+: Attention and Transformers!")
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else:
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print("❌ CNN INTEGRATION TEST FAILED!")
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print(" Check spatial and training modules before proceeding")
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print("=" * 60)
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return success
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if __name__ == "__main__":
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run_cnn_integration_test() |