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Phase 1 Complete: Training Infrastructure - TrainingMonitor class with loss tracking, validation splits, early stopping - Fixed gradient flow by maintaining computational graph - Updated XOR and MNIST to use new infrastructure - Added progress visualization with status indicators Results: - Perceptron: 100% accuracy achieved - XOR: Learning with validation monitoring - MNIST: Gradient flow verified on all 6 parameters - Validation splits prevent overfitting - Early stopping triggers correctly Next: Ensure all examples learn properly before optimization
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""Test MNIST training to debug loss computation."""
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import sys
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import os
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import numpy as np
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project_root = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(project_root)
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from tinytorch.core.tensor import Tensor
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from examples.mnist_mlp_1986.train_mlp import MNISTMLP
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from examples.utils import cross_entropy_loss
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print("Testing MNIST training with small batch...")
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# Create simple model (check actual signature)
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model = MNISTMLP() # Uses default sizes
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# Create small batch of synthetic data
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batch_size = 4
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X = np.random.randn(batch_size, 784).astype(np.float32) * 0.1
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y = np.array([0, 1, 2, 3]) # Different classes
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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"Input shape: {X.shape}")
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print(f"Labels: {y}")
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# Forward pass
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outputs = model.forward(X_tensor)
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print(f"Output shape: {outputs.data.shape}")
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# Check output values
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outputs_np = np.array(outputs.data.data if hasattr(outputs.data, 'data') else outputs.data)
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print(f"Output sample (first row): {outputs_np[0][:5]}...")
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print(f"Output range: [{outputs_np.min():.4f}, {outputs_np.max():.4f}]")
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# Test MSE loss (simpler)
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print("\n=== Testing MSE Loss ===")
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# Create one-hot targets for MSE
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one_hot = np.zeros((batch_size, 10))
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for i in range(batch_size):
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one_hot[i, y[i]] = 1.0
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targets_tensor = Tensor(one_hot)
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# Compute MSE
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diff = outputs - targets_tensor
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squared_diff = diff * diff
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print(f"Diff shape: {diff.data.shape}")
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print(f"Squared diff shape: {squared_diff.data.shape}")
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# Extract mean manually
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squared_np = np.array(squared_diff.data.data if hasattr(squared_diff.data, 'data') else squared_diff.data)
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mse_value = np.mean(squared_np)
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print(f"MSE loss value: {mse_value:.4f}")
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# Test backward
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n_elements = np.prod(squared_diff.data.shape)
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grad_output = Tensor(np.ones_like(squared_diff.data) / n_elements)
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squared_diff.backward(grad_output)
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# Check for gradients
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params_with_grad = 0
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for param in model.parameters():
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if param.grad is not None:
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params_with_grad += 1
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print(f"\nGradient check: {params_with_grad}/{len(model.parameters())} parameters have gradients")
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if params_with_grad > 0:
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print("✅ Gradients are flowing!")
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else:
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print("❌ No gradients detected")
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