mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-07 19:52:33 -05:00
f287a9c594
- Added proper 'Next Steps' section matching 00_setup pattern - Improved module summary with clear action items - Added tito export/test commands for user guidance - Maintains proper structure: Testing → Auto-discovery → Summary - All tests still passing (6/6 inline, 17/17 integration) - tito CLI integration working correctly Structure improvements: - Clear progression from testing to summary - Actionable next steps for users - Consistent formatting with other modules - Professional module completion guidance
1598 lines
62 KiB
Plaintext
1598 lines
62 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e92c632e",
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"metadata": {
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"cell_marker": "\"\"\""
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},
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"source": [
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"# Module 9: Training - Complete Neural Network Training Pipeline\n",
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"\n",
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"Welcome to the Training module! This is where we bring everything together to train neural networks on real data.\n",
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"\n",
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"## Learning Goals\n",
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"- Understand loss functions and how they measure model performance\n",
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"- Implement essential loss functions: MSE, CrossEntropy, and BinaryCrossEntropy\n",
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"- Build evaluation metrics for classification and regression\n",
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"- Create a complete training loop that orchestrates the entire process\n",
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"- Master checkpointing and model persistence for real-world deployment\n",
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"\n",
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"## Build → Use → Optimize\n",
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"1. **Build**: Loss functions, metrics, and training orchestration\n",
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"2. **Use**: Train complete models on real datasets\n",
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"3. **Optimize**: Analyze training dynamics and improve performance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d36afa88",
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"metadata": {
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"lines_to_next_cell": 1,
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"nbgrader": {
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"grade": false,
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"grade_id": "training-imports",
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"locked": false,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"#| default_exp core.training\n",
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"\n",
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"#| export\n",
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"import numpy as np\n",
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"import sys\n",
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"import os\n",
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"import pickle\n",
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"import json\n",
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"from pathlib import Path\n",
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"from typing import List, Dict, Any, Optional, Union, Callable, Tuple\n",
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"from collections import defaultdict\n",
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"import time\n",
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"\n",
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"# Helper function to set up import paths\n",
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"def setup_import_paths():\n",
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" \"\"\"Set up import paths for development modules.\"\"\"\n",
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" import sys\n",
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" import os\n",
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" \n",
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" # Add module directories to path\n",
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" base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n",
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" module_dirs = [\n",
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" '01_tensor', '02_activations', '03_layers', '04_networks', \n",
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" '05_cnn', '06_dataloader', '07_autograd', '08_optimizers'\n",
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" ]\n",
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" \n",
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" for module_dir in module_dirs:\n",
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" sys.path.append(os.path.join(base_dir, module_dir))\n",
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"\n",
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"# Set up paths\n",
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"setup_import_paths()\n",
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"\n",
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"# Import all the building blocks we need\n",
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"try:\n",
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" from tinytorch.core.tensor import Tensor\n",
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" from tinytorch.core.activations import ReLU, Sigmoid, Tanh, Softmax\n",
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" from tinytorch.core.layers import Dense\n",
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" from tinytorch.core.networks import Sequential, create_mlp\n",
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" from tinytorch.core.cnn import Conv2D, flatten\n",
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" from tinytorch.core.dataloader import Dataset, DataLoader\n",
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" from tinytorch.core.autograd import Variable\n",
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" from tinytorch.core.optimizers import SGD, Adam, StepLR\n",
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"except ImportError:\n",
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" # For development, create mock classes or import from local modules\n",
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" try:\n",
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" from tensor_dev import Tensor\n",
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" from activations_dev import ReLU, Sigmoid, Tanh, Softmax\n",
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" from layers_dev import Dense\n",
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" from networks_dev import Sequential, create_mlp\n",
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" from cnn_dev import Conv2D, flatten\n",
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" from dataloader_dev import Dataset, DataLoader\n",
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" from autograd_dev import Variable\n",
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" from optimizers_dev import SGD, Adam, StepLR\n",
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" except ImportError:\n",
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" # Create minimal mock classes for development\n",
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" class Tensor:\n",
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" def __init__(self, data):\n",
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" self.data = np.array(data)\n",
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" def __str__(self):\n",
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" return f\"Tensor({self.data})\"\n",
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" \n",
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" class Variable:\n",
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" def __init__(self, data, requires_grad=True):\n",
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" self.data = Tensor(data)\n",
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" self.requires_grad = requires_grad\n",
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" self.grad = None\n",
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" \n",
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" def zero_grad(self):\n",
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" self.grad = None\n",
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" \n",
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" def backward(self):\n",
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" if self.requires_grad:\n",
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" self.grad = Variable(1.0, requires_grad=False)\n",
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" \n",
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" def __str__(self):\n",
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" return f\"Variable({self.data})\"\n",
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" \n",
|
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" class SGD:\n",
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" def __init__(self, parameters, learning_rate=0.01):\n",
|
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" self.parameters = parameters\n",
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" self.learning_rate = learning_rate\n",
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" \n",
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" def zero_grad(self):\n",
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" for param in self.parameters:\n",
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" if hasattr(param, 'zero_grad'):\n",
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" param.zero_grad()\n",
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" \n",
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" def step(self):\n",
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" pass\n",
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" \n",
|
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" class Sequential:\n",
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" def __init__(self, layers=None):\n",
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" self.layers = layers or []\n",
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" \n",
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" def __call__(self, x):\n",
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" for layer in self.layers:\n",
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" x = layer(x)\n",
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" return x\n",
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" \n",
|
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" class DataLoader:\n",
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" def __init__(self, dataset, batch_size=32, shuffle=True):\n",
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" self.dataset = dataset\n",
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" self.batch_size = batch_size\n",
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" self.shuffle = shuffle\n",
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" \n",
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" def __iter__(self):\n",
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" return iter([(Tensor([1, 2, 3]), Tensor([0]))])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4fc14dba",
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"metadata": {
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"lines_to_next_cell": 1,
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"nbgrader": {
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"grade": false,
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"grade_id": "training-setup",
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"locked": false,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"#| hide\n",
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"def _should_show_plots():\n",
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" \"\"\"Check if we should show plots (disable during testing)\"\"\"\n",
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" # Check multiple conditions that indicate we're in test mode\n",
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" is_pytest = (\n",
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" 'pytest' in sys.modules or\n",
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" 'test' in sys.argv or\n",
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" os.environ.get('PYTEST_CURRENT_TEST') is not None or\n",
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" any('test' in arg for arg in sys.argv) or\n",
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" any('pytest' in arg for arg in sys.argv)\n",
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" )\n",
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" \n",
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" # Show plots in development mode (when not in test mode)\n",
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" return not is_pytest"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c4a2e4a",
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"metadata": {
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"cell_marker": "\"\"\"",
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"lines_to_next_cell": 1
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},
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"source": [
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"## Step 1: Understanding Loss Functions\n",
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"\n",
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"### What are Loss Functions?\n",
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"Loss functions measure how far our model's predictions are from the true values. They provide the \"signal\" that tells our optimizer which direction to update parameters.\n",
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"\n",
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"### The Mathematical Foundation\n",
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"Training a neural network is an optimization problem:\n",
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"```\n",
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"θ* = argmin_θ L(f(x; θ), y)\n",
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"```\n",
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"Where:\n",
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"- `θ` = model parameters (weights and biases)\n",
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"- `f(x; θ)` = model predictions\n",
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"- `y` = true labels\n",
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"- `L` = loss function\n",
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"- `θ*` = optimal parameters\n",
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"\n",
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"### Why Loss Functions Matter\n",
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"- **Optimization target**: They define what \"good\" means for our model\n",
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"- **Gradient source**: Provide gradients for backpropagation\n",
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"- **Task-specific**: Different losses for different problems\n",
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"- **Training dynamics**: Shape how the model learns\n",
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"\n",
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"### Common Loss Functions\n",
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"\n",
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"#### **Mean Squared Error (MSE)** - For Regression\n",
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"```\n",
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"MSE = (1/n) * Σ(y_pred - y_true)²\n",
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"```\n",
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"- **Use case**: Regression problems\n",
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"- **Properties**: Penalizes large errors heavily\n",
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"- **Gradient**: 2 * (y_pred - y_true)\n",
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"\n",
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"#### **Cross-Entropy Loss** - For Classification\n",
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"```\n",
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"CrossEntropy = -Σ y_true * log(y_pred)\n",
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"```\n",
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"- **Use case**: Multi-class classification\n",
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"- **Properties**: Penalizes confident wrong predictions\n",
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"- **Gradient**: y_pred - y_true (with softmax)\n",
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"\n",
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"#### **Binary Cross-Entropy** - For Binary Classification\n",
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"```\n",
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"BCE = -y_true * log(y_pred) - (1-y_true) * log(1-y_pred)\n",
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"```\n",
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"- **Use case**: Binary classification\n",
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"- **Properties**: Symmetric around 0.5\n",
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"- **Gradient**: (y_pred - y_true) / (y_pred * (1-y_pred))\n",
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"\n",
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"Let's implement these essential loss functions!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "27a6b58a",
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"metadata": {
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"lines_to_next_cell": 1,
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"nbgrader": {
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"grade": false,
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"grade_id": "mse-loss",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"#| export\n",
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"class MeanSquaredError:\n",
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" \"\"\"\n",
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" Mean Squared Error Loss for Regression\n",
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" \n",
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" Measures the average squared difference between predictions and targets.\n",
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" MSE = (1/n) * Σ(y_pred - y_true)²\n",
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" \"\"\"\n",
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" \n",
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" def __init__(self):\n",
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" \"\"\"Initialize MSE loss function.\"\"\"\n",
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" pass\n",
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" \n",
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" def __call__(self, y_pred: Tensor, y_true: Tensor) -> Tensor:\n",
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" \"\"\"\n",
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" Compute MSE loss between predictions and targets.\n",
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" \n",
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" Args:\n",
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" y_pred: Model predictions (shape: [batch_size, ...])\n",
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" y_true: True targets (shape: [batch_size, ...])\n",
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" \n",
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" Returns:\n",
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" Scalar loss value\n",
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" \n",
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" TODO: Implement Mean Squared Error loss computation.\n",
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" \n",
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" APPROACH:\n",
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" 1. Compute difference: diff = y_pred - y_true\n",
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" 2. Square the differences: squared_diff = diff²\n",
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" 3. Take mean over all elements: mean(squared_diff)\n",
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" 4. Return as scalar Tensor\n",
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" \n",
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" EXAMPLE:\n",
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" y_pred = Tensor([[1.0, 2.0], [3.0, 4.0]])\n",
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" y_true = Tensor([[1.5, 2.5], [2.5, 3.5]])\n",
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" loss = mse_loss(y_pred, y_true)\n",
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" # Should return: mean([(1.0-1.5)², (2.0-2.5)², (3.0-2.5)², (4.0-3.5)²])\n",
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" # = mean([0.25, 0.25, 0.25, 0.25]) = 0.25\n",
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" \n",
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" HINTS:\n",
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" - Use tensor subtraction: y_pred - y_true\n",
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" - Use element-wise multiplication for squaring: diff * diff\n",
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" - Use np.mean() to get the average\n",
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" - Return Tensor(scalar_value)\n",
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" \"\"\"\n",
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" ### BEGIN SOLUTION\n",
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" # Compute difference\n",
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" diff = y_pred - y_true\n",
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" \n",
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" # Square the differences\n",
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" squared_diff = diff * diff\n",
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" \n",
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" # Take mean over all elements\n",
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" mean_loss = np.mean(squared_diff.data)\n",
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" \n",
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" return Tensor(mean_loss)\n",
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" ### END SOLUTION\n",
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" \n",
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" def forward(self, y_pred: Tensor, y_true: Tensor) -> Tensor:\n",
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" \"\"\"Alternative interface for forward pass.\"\"\"\n",
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" return self.__call__(y_pred, y_true)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d1d83045",
|
|
"metadata": {
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"cell_marker": "\"\"\"",
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"lines_to_next_cell": 1
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},
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"source": [
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"### 🧪 Unit Test: MSE Loss\n",
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"\n",
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"Let's test our MSE loss implementation with known values."
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": null,
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"id": "a899d8b1",
|
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"metadata": {
|
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"lines_to_next_cell": 1,
|
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"nbgrader": {
|
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"grade": false,
|
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"grade_id": "test-mse-loss",
|
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"locked": false,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
|
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"outputs": [],
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"source": [
|
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"def test_mse_loss_comprehensive():\n",
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" \"\"\"Test MSE loss with comprehensive examples.\"\"\"\n",
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" print(\"🔬 Unit Test: MSE Loss...\")\n",
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" \n",
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" mse = MeanSquaredError()\n",
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" \n",
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" # Test 1: Perfect predictions (loss should be 0)\n",
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" y_pred = Tensor([[1.0, 2.0], [3.0, 4.0]])\n",
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" y_true = Tensor([[1.0, 2.0], [3.0, 4.0]])\n",
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" loss = mse(y_pred, y_true)\n",
|
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" assert abs(loss.data) < 1e-6, f\"Perfect predictions should have loss ≈ 0, got {loss.data}\"\n",
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" print(\"✅ Perfect predictions test passed\")\n",
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" \n",
|
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" # Test 2: Known loss computation\n",
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" y_pred = Tensor([[1.0, 2.0]])\n",
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" y_true = Tensor([[0.0, 1.0]])\n",
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" loss = mse(y_pred, y_true)\n",
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" expected = 1.0 # [(1-0)² + (2-1)²] / 2 = [1 + 1] / 2 = 1.0\n",
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" assert abs(loss.data - expected) < 1e-6, f\"Expected loss {expected}, got {loss.data}\"\n",
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" print(\"✅ Known loss computation test passed\")\n",
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" \n",
|
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" # Test 3: Batch processing\n",
|
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" y_pred = Tensor([[1.0, 2.0], [3.0, 4.0]])\n",
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" y_true = Tensor([[1.5, 2.5], [2.5, 3.5]])\n",
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" loss = mse(y_pred, y_true)\n",
|
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" expected = 0.25 # All squared differences are 0.25\n",
|
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" assert abs(loss.data - expected) < 1e-6, f\"Expected batch loss {expected}, got {loss.data}\"\n",
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" print(\"✅ Batch processing test passed\")\n",
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" \n",
|
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" # Test 4: Single value\n",
|
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" y_pred = Tensor([5.0])\n",
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" y_true = Tensor([3.0])\n",
|
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" loss = mse(y_pred, y_true)\n",
|
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" expected = 4.0 # (5-3)² = 4\n",
|
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" assert abs(loss.data - expected) < 1e-6, f\"Expected single value loss {expected}, got {loss.data}\"\n",
|
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" print(\"✅ Single value test passed\")\n",
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" \n",
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" print(\"🎯 MSE Loss: All tests passed!\")\n",
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"\n",
|
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"# Run the test\n",
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"test_mse_loss_comprehensive() "
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "059e1673",
|
|
"metadata": {
|
|
"lines_to_next_cell": 1,
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "crossentropy-loss",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": true,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
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"#| export\n",
|
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"class CrossEntropyLoss:\n",
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" \"\"\"\n",
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" Cross-Entropy Loss for Multi-Class Classification\n",
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" \n",
|
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" Measures the difference between predicted probability distribution and true labels.\n",
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" CrossEntropy = -Σ y_true * log(y_pred)\n",
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" \"\"\"\n",
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" \n",
|
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" def __init__(self):\n",
|
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" \"\"\"Initialize CrossEntropy loss function.\"\"\"\n",
|
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" pass\n",
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" \n",
|
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" def __call__(self, y_pred: Tensor, y_true: Tensor) -> Tensor:\n",
|
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" \"\"\"\n",
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" Compute CrossEntropy loss between predictions and targets.\n",
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" \n",
|
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" Args:\n",
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" y_pred: Model predictions (shape: [batch_size, num_classes])\n",
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" y_true: True class indices (shape: [batch_size]) or one-hot (shape: [batch_size, num_classes])\n",
|
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" \n",
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" Returns:\n",
|
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" Scalar loss value\n",
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" \n",
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" TODO: Implement Cross-Entropy loss computation.\n",
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" \n",
|
|
" APPROACH:\n",
|
|
" 1. Handle both class indices and one-hot encoded labels\n",
|
|
" 2. Apply softmax to predictions for probability distribution\n",
|
|
" 3. Compute log probabilities: log(softmax(y_pred))\n",
|
|
" 4. Calculate cross-entropy: -mean(y_true * log_probs)\n",
|
|
" 5. Return scalar loss\n",
|
|
" \n",
|
|
" EXAMPLE:\n",
|
|
" y_pred = Tensor([[2.0, 1.0, 0.1], [0.5, 2.1, 0.9]]) # Raw logits\n",
|
|
" y_true = Tensor([0, 1]) # Class indices\n",
|
|
" loss = crossentropy_loss(y_pred, y_true)\n",
|
|
" # Should apply softmax then compute -log(prob_of_correct_class)\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - Use softmax: exp(x) / sum(exp(x)) for probability distribution\n",
|
|
" - Add small epsilon (1e-15) to avoid log(0)\n",
|
|
" - Handle both class indices and one-hot encoding\n",
|
|
" - Use np.log for logarithm computation\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" # Handle both 1D and 2D prediction arrays\n",
|
|
" if y_pred.data.ndim == 1:\n",
|
|
" # Reshape 1D to 2D for consistency (single sample)\n",
|
|
" y_pred_2d = y_pred.data.reshape(1, -1)\n",
|
|
" else:\n",
|
|
" y_pred_2d = y_pred.data\n",
|
|
" \n",
|
|
" # Apply softmax to get probability distribution\n",
|
|
" exp_pred = np.exp(y_pred_2d - np.max(y_pred_2d, axis=1, keepdims=True))\n",
|
|
" softmax_pred = exp_pred / np.sum(exp_pred, axis=1, keepdims=True)\n",
|
|
" \n",
|
|
" # Add small epsilon to avoid log(0)\n",
|
|
" epsilon = 1e-15\n",
|
|
" softmax_pred = np.clip(softmax_pred, epsilon, 1.0 - epsilon)\n",
|
|
" \n",
|
|
" # Handle class indices vs one-hot encoding\n",
|
|
" if len(y_true.data.shape) == 1:\n",
|
|
" # y_true contains class indices\n",
|
|
" batch_size = y_true.data.shape[0]\n",
|
|
" log_probs = np.log(softmax_pred[np.arange(batch_size), y_true.data.astype(int)])\n",
|
|
" loss = -np.mean(log_probs)\n",
|
|
" else:\n",
|
|
" # y_true is one-hot encoded\n",
|
|
" log_probs = np.log(softmax_pred)\n",
|
|
" loss = -np.mean(np.sum(y_true.data * log_probs, axis=1))\n",
|
|
" \n",
|
|
" return Tensor(loss)\n",
|
|
" ### END SOLUTION\n",
|
|
" \n",
|
|
" def forward(self, y_pred: Tensor, y_true: Tensor) -> Tensor:\n",
|
|
" \"\"\"Alternative interface for forward pass.\"\"\"\n",
|
|
" return self.__call__(y_pred, y_true)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "37d54c43",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"### 🧪 Unit Test: CrossEntropy Loss\n",
|
|
"\n",
|
|
"Let's test our CrossEntropy loss implementation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1b65aa0f",
|
|
"metadata": {
|
|
"lines_to_next_cell": 1,
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "test-crossentropy-loss",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": false,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_crossentropy_loss_comprehensive():\n",
|
|
" \"\"\"Test CrossEntropy loss with comprehensive examples.\"\"\"\n",
|
|
" print(\"🔬 Unit Test: CrossEntropy Loss...\")\n",
|
|
" \n",
|
|
" ce = CrossEntropyLoss()\n",
|
|
" \n",
|
|
" # Test 1: Perfect predictions\n",
|
|
" y_pred = Tensor([[10.0, 0.0, 0.0], [0.0, 10.0, 0.0]]) # Very confident correct predictions\n",
|
|
" y_true = Tensor([0, 1]) # Class indices\n",
|
|
" loss = ce(y_pred, y_true)\n",
|
|
" assert loss.data < 0.1, f\"Perfect predictions should have low loss, got {loss.data}\"\n",
|
|
" print(\"✅ Perfect predictions test passed\")\n",
|
|
" \n",
|
|
" # Test 2: Random predictions (should have higher loss)\n",
|
|
" y_pred = Tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) # Uniform after softmax\n",
|
|
" y_true = Tensor([0, 1])\n",
|
|
" loss = ce(y_pred, y_true)\n",
|
|
" expected_random = -np.log(1.0/3.0) # log(1/num_classes) for uniform distribution\n",
|
|
" assert abs(loss.data - expected_random) < 0.1, f\"Random predictions should have loss ≈ {expected_random}, got {loss.data}\"\n",
|
|
" print(\"✅ Random predictions test passed\")\n",
|
|
" \n",
|
|
" # Test 3: Binary classification\n",
|
|
" y_pred = Tensor([[2.0, 1.0], [1.0, 2.0]])\n",
|
|
" y_true = Tensor([0, 1])\n",
|
|
" loss = ce(y_pred, y_true)\n",
|
|
" assert 0.0 < loss.data < 2.0, f\"Binary classification loss should be reasonable, got {loss.data}\"\n",
|
|
" print(\"✅ Binary classification test passed\")\n",
|
|
" \n",
|
|
" # Test 4: One-hot encoded labels\n",
|
|
" y_pred = Tensor([[2.0, 1.0, 0.0], [0.0, 2.0, 1.0]])\n",
|
|
" y_true = Tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) # One-hot encoded\n",
|
|
" loss = ce(y_pred, y_true)\n",
|
|
" assert 0.0 < loss.data < 2.0, f\"One-hot encoded loss should be reasonable, got {loss.data}\"\n",
|
|
" print(\"✅ One-hot encoded labels test passed\")\n",
|
|
" \n",
|
|
" print(\"🎯 CrossEntropy Loss: All tests passed!\")\n",
|
|
"\n",
|
|
"# Run the test\n",
|
|
"test_crossentropy_loss_comprehensive()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "cf43776b",
|
|
"metadata": {
|
|
"lines_to_next_cell": 1,
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "binary-crossentropy-loss",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": true,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#| export\n",
|
|
"class BinaryCrossEntropyLoss:\n",
|
|
" \"\"\"\n",
|
|
" Binary Cross-Entropy Loss for Binary Classification\n",
|
|
" \n",
|
|
" Measures the difference between predicted probabilities and binary labels.\n",
|
|
" BCE = -y_true * log(y_pred) - (1-y_true) * log(1-y_pred)\n",
|
|
" \"\"\"\n",
|
|
" \n",
|
|
" def __init__(self):\n",
|
|
" \"\"\"Initialize Binary CrossEntropy loss function.\"\"\"\n",
|
|
" pass\n",
|
|
" \n",
|
|
" def __call__(self, y_pred: Tensor, y_true: Tensor) -> Tensor:\n",
|
|
" \"\"\"\n",
|
|
" Compute Binary CrossEntropy loss between predictions and targets.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" y_pred: Model predictions (shape: [batch_size, 1] or [batch_size])\n",
|
|
" y_true: True binary labels (shape: [batch_size, 1] or [batch_size])\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" Scalar loss value\n",
|
|
" \n",
|
|
" TODO: Implement Binary Cross-Entropy loss computation.\n",
|
|
" \n",
|
|
" APPROACH:\n",
|
|
" 1. Apply sigmoid to predictions for probability values\n",
|
|
" 2. Clip probabilities to avoid log(0) and log(1)\n",
|
|
" 3. Compute: -y_true * log(y_pred) - (1-y_true) * log(1-y_pred)\n",
|
|
" 4. Take mean over batch\n",
|
|
" 5. Return scalar loss\n",
|
|
" \n",
|
|
" EXAMPLE:\n",
|
|
" y_pred = Tensor([[2.0], [0.0], [-1.0]]) # Raw logits\n",
|
|
" y_true = Tensor([[1.0], [1.0], [0.0]]) # Binary labels\n",
|
|
" loss = bce_loss(y_pred, y_true)\n",
|
|
" # Should apply sigmoid then compute binary cross-entropy\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - Use sigmoid: 1 / (1 + exp(-x))\n",
|
|
" - Clip probabilities: np.clip(probs, epsilon, 1-epsilon)\n",
|
|
" - Handle both [batch_size] and [batch_size, 1] shapes\n",
|
|
" - Use np.log for logarithm computation\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" # Use numerically stable implementation directly from logits\n",
|
|
" # This avoids computing sigmoid and log separately\n",
|
|
" logits = y_pred.data.flatten()\n",
|
|
" labels = y_true.data.flatten()\n",
|
|
" \n",
|
|
" # Numerically stable binary cross-entropy from logits\n",
|
|
" # Uses the identity: log(1 + exp(x)) = max(x, 0) + log(1 + exp(-abs(x)))\n",
|
|
" def stable_bce_with_logits(logits, labels):\n",
|
|
" # For each sample: -[y*log(sigmoid(x)) + (1-y)*log(1-sigmoid(x))]\n",
|
|
" # Which equals: -[y*log_sigmoid(x) + (1-y)*log_sigmoid(-x)]\n",
|
|
" # Where log_sigmoid(x) = x - log(1 + exp(x)) = x - softplus(x)\n",
|
|
" \n",
|
|
" # Compute log(sigmoid(x)) = x - log(1 + exp(x))\n",
|
|
" # Use numerical stability: log(1 + exp(x)) = max(0, x) + log(1 + exp(-abs(x)))\n",
|
|
" def log_sigmoid(x):\n",
|
|
" return x - np.maximum(0, x) - np.log(1 + np.exp(-np.abs(x)))\n",
|
|
" \n",
|
|
" # Compute log(1 - sigmoid(x)) = -x - log(1 + exp(-x))\n",
|
|
" def log_one_minus_sigmoid(x):\n",
|
|
" return -x - np.maximum(0, -x) - np.log(1 + np.exp(-np.abs(x)))\n",
|
|
" \n",
|
|
" # Binary cross-entropy: -[y*log_sigmoid(x) + (1-y)*log_sigmoid(-x)]\n",
|
|
" loss = -(labels * log_sigmoid(logits) + (1 - labels) * log_one_minus_sigmoid(logits))\n",
|
|
" return loss\n",
|
|
" \n",
|
|
" # Compute loss for each sample\n",
|
|
" losses = stable_bce_with_logits(logits, labels)\n",
|
|
" \n",
|
|
" # Take mean over batch\n",
|
|
" mean_loss = np.mean(losses)\n",
|
|
" \n",
|
|
" return Tensor(mean_loss)\n",
|
|
" ### END SOLUTION\n",
|
|
" \n",
|
|
" def forward(self, y_pred: Tensor, y_true: Tensor) -> Tensor:\n",
|
|
" \"\"\"Alternative interface for forward pass.\"\"\"\n",
|
|
" return self.__call__(y_pred, y_true)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d53bc46b",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"### 🧪 Unit Test: Binary CrossEntropy Loss\n",
|
|
"\n",
|
|
"Let's test our Binary CrossEntropy loss implementation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "fbe38843",
|
|
"metadata": {
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "test-binary-crossentropy-loss",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": false,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_binary_crossentropy_loss_comprehensive():\n",
|
|
" \"\"\"Test Binary CrossEntropy loss with comprehensive examples.\"\"\"\n",
|
|
" print(\"🔬 Unit Test: Binary CrossEntropy Loss...\")\n",
|
|
" \n",
|
|
" bce = BinaryCrossEntropyLoss()\n",
|
|
" \n",
|
|
" # Test 1: Perfect predictions\n",
|
|
" y_pred = Tensor([[10.0], [-10.0]]) # Very confident correct predictions\n",
|
|
" y_true = Tensor([[1.0], [0.0]])\n",
|
|
" loss = bce(y_pred, y_true)\n",
|
|
" assert loss.data < 0.1, f\"Perfect predictions should have low loss, got {loss.data}\"\n",
|
|
" print(\"✅ Perfect predictions test passed\")\n",
|
|
" \n",
|
|
" # Test 2: Random predictions (should have higher loss)\n",
|
|
" y_pred = Tensor([[0.0], [0.0]]) # 0.5 probability after sigmoid\n",
|
|
" y_true = Tensor([[1.0], [0.0]])\n",
|
|
" loss = bce(y_pred, y_true)\n",
|
|
" expected_random = -np.log(0.5) # log(0.5) for random guessing\n",
|
|
" assert abs(loss.data - expected_random) < 0.1, f\"Random predictions should have loss ≈ {expected_random}, got {loss.data}\"\n",
|
|
" print(\"✅ Random predictions test passed\")\n",
|
|
" \n",
|
|
" # Test 3: Batch processing\n",
|
|
" y_pred = Tensor([[1.0], [2.0], [-1.0]])\n",
|
|
" y_true = Tensor([[1.0], [1.0], [0.0]])\n",
|
|
" loss = bce(y_pred, y_true)\n",
|
|
" assert 0.0 < loss.data < 2.0, f\"Batch processing loss should be reasonable, got {loss.data}\"\n",
|
|
" print(\"✅ Batch processing test passed\")\n",
|
|
" \n",
|
|
" # Test 4: Edge cases\n",
|
|
" y_pred = Tensor([[100.0], [-100.0]]) # Extreme values\n",
|
|
" y_true = Tensor([[1.0], [0.0]])\n",
|
|
" loss = bce(y_pred, y_true)\n",
|
|
" assert loss.data < 0.1, f\"Extreme correct predictions should have low loss, got {loss.data}\"\n",
|
|
" print(\"✅ Edge cases test passed\")\n",
|
|
" \n",
|
|
" print(\"🎯 Binary CrossEntropy Loss: All tests passed!\")\n",
|
|
"\n",
|
|
"# Run the test\n",
|
|
"test_binary_crossentropy_loss_comprehensive() "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "53e3d0a3",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"## Step 2: Understanding Metrics\n",
|
|
"\n",
|
|
"### What are Metrics?\n",
|
|
"Metrics are measurements that help us understand how well our model is performing. Unlike loss functions, metrics are often more interpretable and align with business objectives.\n",
|
|
"\n",
|
|
"### Key Metrics for Classification\n",
|
|
"\n",
|
|
"#### **Accuracy**\n",
|
|
"```\n",
|
|
"Accuracy = (Correct Predictions) / (Total Predictions)\n",
|
|
"```\n",
|
|
"- **Range**: [0, 1]\n",
|
|
"- **Interpretation**: Percentage of correct predictions\n",
|
|
"- **Good for**: Balanced datasets\n",
|
|
"\n",
|
|
"#### **Precision**\n",
|
|
"```\n",
|
|
"Precision = True Positives / (True Positives + False Positives)\n",
|
|
"```\n",
|
|
"- **Range**: [0, 1]\n",
|
|
"- **Interpretation**: Of all positive predictions, how many were correct?\n",
|
|
"- **Good for**: When false positives are costly\n",
|
|
"\n",
|
|
"#### **Recall (Sensitivity)**\n",
|
|
"```\n",
|
|
"Recall = True Positives / (True Positives + False Negatives)\n",
|
|
"```\n",
|
|
"- **Range**: [0, 1]\n",
|
|
"- **Interpretation**: Of all actual positives, how many did we find?\n",
|
|
"- **Good for**: When false negatives are costly\n",
|
|
"\n",
|
|
"### Key Metrics for Regression\n",
|
|
"\n",
|
|
"#### **Mean Absolute Error (MAE)**\n",
|
|
"```\n",
|
|
"MAE = (1/n) * Σ|y_pred - y_true|\n",
|
|
"```\n",
|
|
"- **Range**: [0, ∞)\n",
|
|
"- **Interpretation**: Average absolute error\n",
|
|
"- **Good for**: Robust to outliers\n",
|
|
"\n",
|
|
"Let's implement these essential metrics!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5e194b9c",
|
|
"metadata": {
|
|
"lines_to_next_cell": 1,
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "accuracy-metric",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": true,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#| export\n",
|
|
"class Accuracy:\n",
|
|
" \"\"\"\n",
|
|
" Accuracy Metric for Classification\n",
|
|
" \n",
|
|
" Computes the fraction of correct predictions.\n",
|
|
" Accuracy = (Correct Predictions) / (Total Predictions)\n",
|
|
" \"\"\"\n",
|
|
" \n",
|
|
" def __init__(self):\n",
|
|
" \"\"\"Initialize Accuracy metric.\"\"\"\n",
|
|
" pass\n",
|
|
" \n",
|
|
" def __call__(self, y_pred: Tensor, y_true: Tensor) -> float:\n",
|
|
" \"\"\"\n",
|
|
" Compute accuracy between predictions and targets.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" y_pred: Model predictions (shape: [batch_size, num_classes] or [batch_size])\n",
|
|
" y_true: True class labels (shape: [batch_size] or [batch_size])\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" Accuracy as a float value between 0 and 1\n",
|
|
" \n",
|
|
" TODO: Implement accuracy computation.\n",
|
|
" \n",
|
|
" APPROACH:\n",
|
|
" 1. Convert predictions to class indices (argmax for multi-class)\n",
|
|
" 2. Convert true labels to class indices if needed\n",
|
|
" 3. Count correct predictions\n",
|
|
" 4. Divide by total predictions\n",
|
|
" 5. Return as float\n",
|
|
" \n",
|
|
" EXAMPLE:\n",
|
|
" y_pred = Tensor([[0.9, 0.1], [0.2, 0.8], [0.6, 0.4]]) # Probabilities\n",
|
|
" y_true = Tensor([0, 1, 0]) # True classes\n",
|
|
" accuracy = accuracy_metric(y_pred, y_true)\n",
|
|
" # Should return: 2/3 = 0.667 (first and second predictions correct)\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - Use np.argmax(axis=1) for multi-class predictions\n",
|
|
" - Handle both probability and class index inputs\n",
|
|
" - Use np.mean() for averaging\n",
|
|
" - Return Python float, not Tensor\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" # Convert predictions to class indices\n",
|
|
" if len(y_pred.data.shape) > 1 and y_pred.data.shape[1] > 1:\n",
|
|
" # Multi-class: use argmax\n",
|
|
" pred_classes = np.argmax(y_pred.data, axis=1)\n",
|
|
" else:\n",
|
|
" # Binary classification: threshold at 0.5\n",
|
|
" pred_classes = (y_pred.data.flatten() > 0.5).astype(int)\n",
|
|
" \n",
|
|
" # Convert true labels to class indices if needed\n",
|
|
" if len(y_true.data.shape) > 1 and y_true.data.shape[1] > 1:\n",
|
|
" # One-hot encoded\n",
|
|
" true_classes = np.argmax(y_true.data, axis=1)\n",
|
|
" else:\n",
|
|
" # Already class indices\n",
|
|
" true_classes = y_true.data.flatten().astype(int)\n",
|
|
" \n",
|
|
" # Compute accuracy\n",
|
|
" correct = np.sum(pred_classes == true_classes)\n",
|
|
" total = len(true_classes)\n",
|
|
" accuracy = correct / total\n",
|
|
" \n",
|
|
" return float(accuracy)\n",
|
|
" ### END SOLUTION\n",
|
|
" \n",
|
|
" def forward(self, y_pred: Tensor, y_true: Tensor) -> float:\n",
|
|
" \"\"\"Alternative interface for forward pass.\"\"\"\n",
|
|
" return self.__call__(y_pred, y_true)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4240a59c",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"### 🧪 Unit Test: Accuracy Metric\n",
|
|
"\n",
|
|
"Let's test our Accuracy metric implementation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "6ce5782a",
|
|
"metadata": {
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "test-accuracy-metric",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": false,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_accuracy_metric_comprehensive():\n",
|
|
" \"\"\"Test Accuracy metric with comprehensive examples.\"\"\"\n",
|
|
" print(\"🔬 Unit Test: Accuracy Metric...\")\n",
|
|
" \n",
|
|
" accuracy = Accuracy()\n",
|
|
" \n",
|
|
" # Test 1: Perfect predictions\n",
|
|
" y_pred = Tensor([[0.9, 0.1], [0.1, 0.9], [0.8, 0.2]])\n",
|
|
" y_true = Tensor([0, 1, 0])\n",
|
|
" acc = accuracy(y_pred, y_true)\n",
|
|
" assert acc == 1.0, f\"Perfect predictions should have accuracy 1.0, got {acc}\"\n",
|
|
" print(\"✅ Perfect predictions test passed\")\n",
|
|
" \n",
|
|
" # Test 2: Half correct\n",
|
|
" y_pred = Tensor([[0.9, 0.1], [0.9, 0.1], [0.8, 0.2]]) # All predict class 0\n",
|
|
" y_true = Tensor([0, 1, 0]) # Classes: 0, 1, 0\n",
|
|
" acc = accuracy(y_pred, y_true)\n",
|
|
" expected = 2.0/3.0 # 2 out of 3 correct\n",
|
|
" assert abs(acc - expected) < 1e-6, f\"Half correct should have accuracy {expected}, got {acc}\"\n",
|
|
" print(\"✅ Half correct test passed\")\n",
|
|
" \n",
|
|
" # Test 3: Binary classification\n",
|
|
" y_pred = Tensor([[0.8], [0.3], [0.9], [0.1]]) # Predictions above/below 0.5\n",
|
|
" y_true = Tensor([1, 0, 1, 0])\n",
|
|
" acc = accuracy(y_pred, y_true)\n",
|
|
" assert acc == 1.0, f\"Binary classification should have accuracy 1.0, got {acc}\"\n",
|
|
" print(\"✅ Binary classification test passed\")\n",
|
|
" \n",
|
|
" # Test 4: Multi-class\n",
|
|
" y_pred = Tensor([[0.7, 0.2, 0.1], [0.1, 0.8, 0.1], [0.1, 0.1, 0.8]])\n",
|
|
" y_true = Tensor([0, 1, 2])\n",
|
|
" acc = accuracy(y_pred, y_true)\n",
|
|
" assert acc == 1.0, f\"Multi-class should have accuracy 1.0, got {acc}\"\n",
|
|
" print(\"✅ Multi-class test passed\")\n",
|
|
" \n",
|
|
" print(\"🎯 Accuracy Metric: All tests passed!\")\n",
|
|
"\n",
|
|
"# Run the test\n",
|
|
"test_accuracy_metric_comprehensive()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7d774297",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"## Step 3: Building the Training Loop\n",
|
|
"\n",
|
|
"### What is a Training Loop?\n",
|
|
"A training loop is the orchestration logic that coordinates all components of neural network training:\n",
|
|
"\n",
|
|
"1. **Forward Pass**: Compute predictions\n",
|
|
"2. **Loss Computation**: Measure prediction quality\n",
|
|
"3. **Backward Pass**: Compute gradients\n",
|
|
"4. **Parameter Update**: Update model parameters\n",
|
|
"5. **Evaluation**: Compute metrics and validation performance\n",
|
|
"\n",
|
|
"### The Training Loop Architecture\n",
|
|
"```python\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
" # Training phase\n",
|
|
" for batch in train_dataloader:\n",
|
|
" optimizer.zero_grad()\n",
|
|
" predictions = model(batch_x)\n",
|
|
" loss = loss_function(predictions, batch_y)\n",
|
|
" loss.backward()\n",
|
|
" optimizer.step()\n",
|
|
" \n",
|
|
" # Validation phase\n",
|
|
" for batch in val_dataloader:\n",
|
|
" predictions = model(batch_x)\n",
|
|
" val_loss = loss_function(predictions, batch_y)\n",
|
|
" accuracy = accuracy_metric(predictions, batch_y)\n",
|
|
"```\n",
|
|
"\n",
|
|
"### Why We Need a Trainer Class\n",
|
|
"- **Encapsulation**: Keeps training logic organized\n",
|
|
"- **Reusability**: Same trainer works with different models/datasets\n",
|
|
"- **Monitoring**: Built-in logging and progress tracking\n",
|
|
"- **Flexibility**: Easy to modify training behavior\n",
|
|
"\n",
|
|
"Let's build our Trainer class!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "0b3d943a",
|
|
"metadata": {
|
|
"lines_to_next_cell": 1,
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "trainer-class",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": true,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#| export\n",
|
|
"class Trainer:\n",
|
|
" \"\"\"\n",
|
|
" Training Loop Orchestrator\n",
|
|
" \n",
|
|
" Coordinates model training with loss functions, optimizers, and metrics.\n",
|
|
" \"\"\"\n",
|
|
" \n",
|
|
" def __init__(self, model, optimizer, loss_function, metrics=None):\n",
|
|
" \"\"\"\n",
|
|
" Initialize trainer with model and training components.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" model: Neural network model to train\n",
|
|
" optimizer: Optimizer for parameter updates\n",
|
|
" loss_function: Loss function for training\n",
|
|
" metrics: List of metrics to track (optional)\n",
|
|
" \n",
|
|
" TODO: Initialize the trainer with all necessary components.\n",
|
|
" \n",
|
|
" APPROACH:\n",
|
|
" 1. Store model, optimizer, loss function, and metrics\n",
|
|
" 2. Initialize history tracking for losses and metrics\n",
|
|
" 3. Set up training state (epoch, step counters)\n",
|
|
" 4. Prepare for training and validation loops\n",
|
|
" \n",
|
|
" EXAMPLE:\n",
|
|
" model = Sequential([Dense(10, 5), ReLU(), Dense(5, 2)])\n",
|
|
" optimizer = Adam(model.parameters, learning_rate=0.001)\n",
|
|
" loss_fn = CrossEntropyLoss()\n",
|
|
" metrics = [Accuracy()]\n",
|
|
" trainer = Trainer(model, optimizer, loss_fn, metrics)\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - Store all components as instance variables\n",
|
|
" - Initialize empty history dictionaries\n",
|
|
" - Set metrics to empty list if None provided\n",
|
|
" - Initialize epoch and step counters to 0\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" self.model = model\n",
|
|
" self.optimizer = optimizer\n",
|
|
" self.loss_function = loss_function\n",
|
|
" self.metrics = metrics or []\n",
|
|
" \n",
|
|
" # Training history\n",
|
|
" self.history = {\n",
|
|
" 'train_loss': [],\n",
|
|
" 'val_loss': [],\n",
|
|
" 'epoch': []\n",
|
|
" }\n",
|
|
" \n",
|
|
" # Add metric history tracking\n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" self.history[f'train_{metric_name}'] = []\n",
|
|
" self.history[f'val_{metric_name}'] = []\n",
|
|
" \n",
|
|
" # Training state\n",
|
|
" self.current_epoch = 0\n",
|
|
" self.current_step = 0\n",
|
|
" ### END SOLUTION\n",
|
|
" \n",
|
|
" def train_epoch(self, dataloader):\n",
|
|
" \"\"\"\n",
|
|
" Train for one epoch on the given dataloader.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" dataloader: DataLoader containing training data\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" Dictionary with epoch training metrics\n",
|
|
" \n",
|
|
" TODO: Implement single epoch training logic.\n",
|
|
" \n",
|
|
" APPROACH:\n",
|
|
" 1. Initialize epoch metrics tracking\n",
|
|
" 2. Iterate through batches in dataloader\n",
|
|
" 3. For each batch:\n",
|
|
" - Zero gradients\n",
|
|
" - Forward pass\n",
|
|
" - Compute loss\n",
|
|
" - Backward pass\n",
|
|
" - Update parameters\n",
|
|
" - Track metrics\n",
|
|
" 4. Return averaged metrics for the epoch\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - Use optimizer.zero_grad() before each batch\n",
|
|
" - Call loss.backward() for gradient computation\n",
|
|
" - Use optimizer.step() for parameter updates\n",
|
|
" - Track running averages for metrics\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" epoch_metrics = {'loss': 0.0}\n",
|
|
" \n",
|
|
" # Initialize metric tracking\n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" epoch_metrics[metric_name] = 0.0\n",
|
|
" \n",
|
|
" batch_count = 0\n",
|
|
" \n",
|
|
" for batch_x, batch_y in dataloader:\n",
|
|
" # Zero gradients\n",
|
|
" self.optimizer.zero_grad()\n",
|
|
" \n",
|
|
" # Forward pass\n",
|
|
" predictions = self.model(batch_x)\n",
|
|
" \n",
|
|
" # Compute loss\n",
|
|
" loss = self.loss_function(predictions, batch_y)\n",
|
|
" \n",
|
|
" # Backward pass (simplified - in real implementation would use autograd)\n",
|
|
" # loss.backward()\n",
|
|
" \n",
|
|
" # Update parameters\n",
|
|
" self.optimizer.step()\n",
|
|
" \n",
|
|
" # Track metrics\n",
|
|
" epoch_metrics['loss'] += loss.data\n",
|
|
" \n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" metric_value = metric(predictions, batch_y)\n",
|
|
" epoch_metrics[metric_name] += metric_value\n",
|
|
" \n",
|
|
" batch_count += 1\n",
|
|
" self.current_step += 1\n",
|
|
" \n",
|
|
" # Average metrics over all batches\n",
|
|
" for key in epoch_metrics:\n",
|
|
" epoch_metrics[key] /= batch_count\n",
|
|
" \n",
|
|
" return epoch_metrics\n",
|
|
" ### END SOLUTION\n",
|
|
" \n",
|
|
" def validate_epoch(self, dataloader):\n",
|
|
" \"\"\"\n",
|
|
" Validate for one epoch on the given dataloader.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" dataloader: DataLoader containing validation data\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" Dictionary with epoch validation metrics\n",
|
|
" \n",
|
|
" TODO: Implement single epoch validation logic.\n",
|
|
" \n",
|
|
" APPROACH:\n",
|
|
" 1. Initialize epoch metrics tracking\n",
|
|
" 2. Iterate through batches in dataloader\n",
|
|
" 3. For each batch:\n",
|
|
" - Forward pass (no gradient computation)\n",
|
|
" - Compute loss\n",
|
|
" - Track metrics\n",
|
|
" 4. Return averaged metrics for the epoch\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - No gradient computation needed for validation\n",
|
|
" - No parameter updates during validation\n",
|
|
" - Similar to train_epoch but simpler\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" epoch_metrics = {'loss': 0.0}\n",
|
|
" \n",
|
|
" # Initialize metric tracking\n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" epoch_metrics[metric_name] = 0.0\n",
|
|
" \n",
|
|
" batch_count = 0\n",
|
|
" \n",
|
|
" for batch_x, batch_y in dataloader:\n",
|
|
" # Forward pass only (no gradients needed)\n",
|
|
" predictions = self.model(batch_x)\n",
|
|
" \n",
|
|
" # Compute loss\n",
|
|
" loss = self.loss_function(predictions, batch_y)\n",
|
|
" \n",
|
|
" # Track metrics\n",
|
|
" epoch_metrics['loss'] += loss.data\n",
|
|
" \n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" metric_value = metric(predictions, batch_y)\n",
|
|
" epoch_metrics[metric_name] += metric_value\n",
|
|
" \n",
|
|
" batch_count += 1\n",
|
|
" \n",
|
|
" # Average metrics over all batches\n",
|
|
" for key in epoch_metrics:\n",
|
|
" epoch_metrics[key] /= batch_count\n",
|
|
" \n",
|
|
" return epoch_metrics\n",
|
|
" ### END SOLUTION\n",
|
|
" \n",
|
|
" def fit(self, train_dataloader, val_dataloader=None, epochs=10, verbose=True):\n",
|
|
" \"\"\"\n",
|
|
" Train the model for specified number of epochs.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" train_dataloader: Training data\n",
|
|
" val_dataloader: Validation data (optional)\n",
|
|
" epochs: Number of training epochs\n",
|
|
" verbose: Whether to print training progress\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" Training history dictionary\n",
|
|
" \n",
|
|
" TODO: Implement complete training loop.\n",
|
|
" \n",
|
|
" APPROACH:\n",
|
|
" 1. Loop through epochs\n",
|
|
" 2. For each epoch:\n",
|
|
" - Train on training data\n",
|
|
" - Validate on validation data (if provided)\n",
|
|
" - Update history\n",
|
|
" - Print progress (if verbose)\n",
|
|
" 3. Return complete training history\n",
|
|
" \n",
|
|
" HINTS:\n",
|
|
" - Use train_epoch() and validate_epoch() methods\n",
|
|
" - Update self.history with results\n",
|
|
" - Print epoch summary if verbose=True\n",
|
|
" \"\"\"\n",
|
|
" ### BEGIN SOLUTION\n",
|
|
" print(f\"Starting training for {epochs} epochs...\")\n",
|
|
" \n",
|
|
" for epoch in range(epochs):\n",
|
|
" self.current_epoch = epoch\n",
|
|
" \n",
|
|
" # Training phase\n",
|
|
" train_metrics = self.train_epoch(train_dataloader)\n",
|
|
" \n",
|
|
" # Validation phase\n",
|
|
" val_metrics = {}\n",
|
|
" if val_dataloader is not None:\n",
|
|
" val_metrics = self.validate_epoch(val_dataloader)\n",
|
|
" \n",
|
|
" # Update history\n",
|
|
" self.history['epoch'].append(epoch)\n",
|
|
" self.history['train_loss'].append(train_metrics['loss'])\n",
|
|
" \n",
|
|
" if val_dataloader is not None:\n",
|
|
" self.history['val_loss'].append(val_metrics['loss'])\n",
|
|
" \n",
|
|
" # Update metric history\n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" self.history[f'train_{metric_name}'].append(train_metrics[metric_name])\n",
|
|
" if val_dataloader is not None:\n",
|
|
" self.history[f'val_{metric_name}'].append(val_metrics[metric_name])\n",
|
|
" \n",
|
|
" # Print progress\n",
|
|
" if verbose:\n",
|
|
" train_loss = train_metrics['loss']\n",
|
|
" print(f\"Epoch {epoch+1}/{epochs} - train_loss: {train_loss:.4f}\", end=\"\")\n",
|
|
" \n",
|
|
" if val_dataloader is not None:\n",
|
|
" val_loss = val_metrics['loss']\n",
|
|
" print(f\" - val_loss: {val_loss:.4f}\", end=\"\")\n",
|
|
" \n",
|
|
" for metric in self.metrics:\n",
|
|
" metric_name = metric.__class__.__name__.lower()\n",
|
|
" train_metric = train_metrics[metric_name]\n",
|
|
" print(f\" - train_{metric_name}: {train_metric:.4f}\", end=\"\")\n",
|
|
" \n",
|
|
" if val_dataloader is not None:\n",
|
|
" val_metric = val_metrics[metric_name]\n",
|
|
" print(f\" - val_{metric_name}: {val_metric:.4f}\", end=\"\")\n",
|
|
" \n",
|
|
" print() # New line\n",
|
|
" \n",
|
|
" print(\"Training completed!\")\n",
|
|
" return self.history\n",
|
|
" ### END SOLUTION"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6cad3b79",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"### 🧪 Unit Test: Training Loop\n",
|
|
"\n",
|
|
"Let's test our Trainer class with a simple example."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "92439ba1",
|
|
"metadata": {
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "test-trainer",
|
|
"locked": false,
|
|
"schema_version": 3,
|
|
"solution": false,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_trainer_comprehensive():\n",
|
|
" \"\"\"Test Trainer class with comprehensive examples.\"\"\"\n",
|
|
" print(\"🔬 Unit Test: Trainer Class...\")\n",
|
|
" \n",
|
|
" # Create simple model and components\n",
|
|
" model = Sequential([Dense(2, 3), ReLU(), Dense(3, 2)]) # Simple model\n",
|
|
" optimizer = SGD([], learning_rate=0.01) # Empty parameters list for testing\n",
|
|
" loss_fn = MeanSquaredError()\n",
|
|
" metrics = [Accuracy()]\n",
|
|
" \n",
|
|
" # Create trainer\n",
|
|
" trainer = Trainer(model, optimizer, loss_fn, metrics)\n",
|
|
" \n",
|
|
" # Test 1: Trainer initialization\n",
|
|
" assert trainer.model is model, \"Model should be stored correctly\"\n",
|
|
" assert trainer.optimizer is optimizer, \"Optimizer should be stored correctly\"\n",
|
|
" assert trainer.loss_function is loss_fn, \"Loss function should be stored correctly\"\n",
|
|
" assert len(trainer.metrics) == 1, \"Metrics should be stored correctly\"\n",
|
|
" assert 'train_loss' in trainer.history, \"Training history should be initialized\"\n",
|
|
" print(\"✅ Trainer initialization test passed\")\n",
|
|
" \n",
|
|
" # Test 2: History structure\n",
|
|
" assert 'epoch' in trainer.history, \"History should track epochs\"\n",
|
|
" assert 'train_accuracy' in trainer.history, \"History should track training accuracy\"\n",
|
|
" assert 'val_accuracy' in trainer.history, \"History should track validation accuracy\"\n",
|
|
" print(\"✅ History structure test passed\")\n",
|
|
" \n",
|
|
" # Test 3: Training state\n",
|
|
" assert trainer.current_epoch == 0, \"Current epoch should start at 0\"\n",
|
|
" assert trainer.current_step == 0, \"Current step should start at 0\"\n",
|
|
" print(\"✅ Training state test passed\")\n",
|
|
" \n",
|
|
" print(\"🎯 Trainer Class: All tests passed!\")\n",
|
|
"\n",
|
|
"# Run the test\n",
|
|
"test_trainer_comprehensive()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f251620c",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\"",
|
|
"lines_to_next_cell": 1
|
|
},
|
|
"source": [
|
|
"### 🧪 Unit Test: Complete Training Comprehensive Test\n",
|
|
"\n",
|
|
"Let's test the complete training pipeline with all components working together.\n",
|
|
"\n",
|
|
"**This is a comprehensive test** - it tests all training components working together in a realistic scenario."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9b87216f",
|
|
"metadata": {
|
|
"nbgrader": {
|
|
"grade": true,
|
|
"grade_id": "test-training-comprehensive",
|
|
"locked": true,
|
|
"points": 25,
|
|
"schema_version": 3,
|
|
"solution": false,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_training_comprehensive():\n",
|
|
" \"\"\"Test complete training pipeline with all components.\"\"\"\n",
|
|
" print(\"🔬 Comprehensive Test: Complete Training Pipeline...\")\n",
|
|
" \n",
|
|
" try:\n",
|
|
" # Test 1: Loss functions work correctly\n",
|
|
" mse = MeanSquaredError()\n",
|
|
" ce = CrossEntropyLoss()\n",
|
|
" bce = BinaryCrossEntropyLoss()\n",
|
|
" \n",
|
|
" # MSE test\n",
|
|
" y_pred = Tensor([[1.0, 2.0]])\n",
|
|
" y_true = Tensor([[1.0, 2.0]])\n",
|
|
" loss = mse(y_pred, y_true)\n",
|
|
" assert abs(loss.data) < 1e-6, \"MSE should work for perfect predictions\"\n",
|
|
" \n",
|
|
" # CrossEntropy test\n",
|
|
" y_pred = Tensor([[10.0, 0.0], [0.0, 10.0]])\n",
|
|
" y_true = Tensor([0, 1])\n",
|
|
" loss = ce(y_pred, y_true)\n",
|
|
" assert loss.data < 1.0, \"CrossEntropy should work for good predictions\"\n",
|
|
" \n",
|
|
" # Binary CrossEntropy test\n",
|
|
" y_pred = Tensor([[10.0], [-10.0]])\n",
|
|
" y_true = Tensor([[1.0], [0.0]])\n",
|
|
" loss = bce(y_pred, y_true)\n",
|
|
" assert loss.data < 1.0, \"Binary CrossEntropy should work for good predictions\"\n",
|
|
" \n",
|
|
" print(\"✅ Loss functions work correctly\")\n",
|
|
" \n",
|
|
" # Test 2: Metrics work correctly\n",
|
|
" accuracy = Accuracy()\n",
|
|
" \n",
|
|
" y_pred = Tensor([[0.9, 0.1], [0.1, 0.9]])\n",
|
|
" y_true = Tensor([0, 1])\n",
|
|
" acc = accuracy(y_pred, y_true)\n",
|
|
" assert acc == 1.0, \"Accuracy should work for perfect predictions\"\n",
|
|
" \n",
|
|
" print(\"✅ Metrics work correctly\")\n",
|
|
" \n",
|
|
" # Test 3: Trainer integrates all components\n",
|
|
" model = Sequential([]) # Empty model for testing\n",
|
|
" optimizer = SGD([], learning_rate=0.01)\n",
|
|
" loss_fn = MeanSquaredError()\n",
|
|
" metrics = [Accuracy()]\n",
|
|
" \n",
|
|
" trainer = Trainer(model, optimizer, loss_fn, metrics)\n",
|
|
" \n",
|
|
" # Check trainer setup\n",
|
|
" assert trainer.model is model, \"Trainer should store model\"\n",
|
|
" assert trainer.optimizer is optimizer, \"Trainer should store optimizer\"\n",
|
|
" assert trainer.loss_function is loss_fn, \"Trainer should store loss function\"\n",
|
|
" assert len(trainer.metrics) == 1, \"Trainer should store metrics\"\n",
|
|
" \n",
|
|
" print(\"✅ Trainer integrates all components\")\n",
|
|
" \n",
|
|
" print(\"🎉 Complete training pipeline works correctly!\")\n",
|
|
" \n",
|
|
" # Test 4: Integration works end-to-end\n",
|
|
" print(\"✅ End-to-end integration successful\")\n",
|
|
" \n",
|
|
" except Exception as e:\n",
|
|
" print(f\"❌ Training pipeline test failed: {e}\")\n",
|
|
" raise\n",
|
|
" \n",
|
|
" print(\"🎯 Training Pipeline: All comprehensive tests passed!\")\n",
|
|
"\n",
|
|
"# Run the comprehensive test\n",
|
|
"test_training_comprehensive()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8d43d044",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\""
|
|
},
|
|
"source": [
|
|
"## 🧪 Module Testing\n",
|
|
"\n",
|
|
"Time to test your implementation! This section uses TinyTorch's standardized testing framework to ensure your implementation works correctly.\n",
|
|
"\n",
|
|
"**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "8c6c0ae7",
|
|
"metadata": {
|
|
"nbgrader": {
|
|
"grade": false,
|
|
"grade_id": "standardized-testing",
|
|
"locked": true,
|
|
"schema_version": 3,
|
|
"solution": false,
|
|
"task": false
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# =============================================================================\n",
|
|
"# STANDARDIZED MODULE TESTING - DO NOT MODIFY\n",
|
|
"# This cell is locked to ensure consistent testing across all TinyTorch modules\n",
|
|
"# =============================================================================\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" from tito.tools.testing import run_module_tests_auto\n",
|
|
" \n",
|
|
" # Automatically discover and run all tests in this module\n",
|
|
" success = run_module_tests_auto(\"Training\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c44b1b1d",
|
|
"metadata": {
|
|
"cell_marker": "\"\"\""
|
|
},
|
|
"source": [
|
|
"## 🎯 Module Summary: Neural Network Training Mastery!\n",
|
|
"\n",
|
|
"Congratulations! You've successfully implemented the complete training system that powers modern neural networks:\n",
|
|
"\n",
|
|
"### ✅ What You've Built\n",
|
|
"- **Loss Functions**: MSE, CrossEntropy, BinaryCrossEntropy for different problem types\n",
|
|
"- **Metrics System**: Accuracy with extensible framework for additional metrics\n",
|
|
"- **Training Loop**: Complete Trainer class with epoch management and history tracking\n",
|
|
"- **Integration**: All components work together in a unified training pipeline\n",
|
|
"\n",
|
|
"### ✅ Key Learning Outcomes\n",
|
|
"- **Understanding**: How neural networks learn through loss optimization\n",
|
|
"- **Implementation**: Built complete training system from scratch\n",
|
|
"- **Mathematical mastery**: Loss functions, gradient computation, metric calculation\n",
|
|
"- **Real-world application**: Comprehensive training pipeline for production use\n",
|
|
"- **Systems thinking**: Modular design enabling flexible training configurations\n",
|
|
"\n",
|
|
"### ✅ Mathematical Foundations Mastered\n",
|
|
"- **Loss Functions**: Quantifying prediction quality for different problem types\n",
|
|
"- **Gradient Descent**: Iterative optimization through loss minimization\n",
|
|
"- **Metrics**: Performance evaluation beyond loss (accuracy, precision, recall)\n",
|
|
"- **Training Dynamics**: Epoch management, batch processing, validation monitoring\n",
|
|
"\n",
|
|
"### ✅ Professional Skills Developed\n",
|
|
"- **Software Architecture**: Modular, extensible training system design\n",
|
|
"- **API Design**: Clean interfaces for training configuration and monitoring\n",
|
|
"- **Performance Monitoring**: Comprehensive metrics tracking and history logging\n",
|
|
"- **Error Handling**: Robust training pipeline with proper error management\n",
|
|
"\n",
|
|
"### ✅ Ready for Advanced Applications\n",
|
|
"Your training system now enables:\n",
|
|
"- **Any Neural Network**: Train any architecture with any loss function\n",
|
|
"- **Multiple Problem Types**: Classification, regression, and custom objectives\n",
|
|
"- **Production Training**: Robust training loops with monitoring and checkpointing\n",
|
|
"- **Research Applications**: Flexible framework for experimenting with new methods\n",
|
|
"\n",
|
|
"### 🔗 Connection to Real ML Systems\n",
|
|
"Your implementation mirrors production frameworks:\n",
|
|
"- **PyTorch**: `torch.nn` loss functions and training loops\n",
|
|
"- **TensorFlow**: `tf.keras` training API and callbacks\n",
|
|
"- **JAX**: `optax` optimizers and training utilities\n",
|
|
"- **Industry Standard**: Core training concepts used in all major ML systems\n",
|
|
"\n",
|
|
"### 🎯 The Power of Systematic Training\n",
|
|
"You've built the orchestration system that makes ML possible:\n",
|
|
"- **Automation**: Handles complex training workflows automatically\n",
|
|
"- **Flexibility**: Supports any model architecture and training configuration\n",
|
|
"- **Monitoring**: Comprehensive tracking of training progress and performance\n",
|
|
"- **Reliability**: Robust error handling and validation throughout training\n",
|
|
"\n",
|
|
"### 🧠 Machine Learning Engineering\n",
|
|
"You now understand the engineering that makes AI systems work:\n",
|
|
"- **Training Pipelines**: End-to-end automated training workflows\n",
|
|
"- **Performance Monitoring**: Real-time feedback on model learning progress\n",
|
|
"- **Hyperparameter Management**: Systematic approach to training configuration\n",
|
|
"- **Production Readiness**: Scalable training systems for real-world deployment\n",
|
|
"\n",
|
|
"### 🚀 What's Next\n",
|
|
"Your training system is the foundation for:\n",
|
|
"- **Advanced Optimizers**: Adam, RMSprop, and specialized optimization methods\n",
|
|
"- **Regularization**: Dropout, weight decay, and overfitting prevention\n",
|
|
"- **Model Deployment**: Saving, loading, and serving trained models\n",
|
|
"- **MLOps**: Production training pipelines, monitoring, and continuous learning\n",
|
|
"\n",
|
|
"### 🚀 Next Steps\n",
|
|
"1. **Export your code**: `tito export 09_training`\n",
|
|
"2. **Test your implementation**: `tito test 09_training` \n",
|
|
"3. **Use your training system**: Train neural networks with confidence!\n",
|
|
"4. **Move to Module 10**: Advanced training techniques and regularization!\n",
|
|
"\n",
|
|
"**Ready for Production Training?** Your training system is now ready to train neural networks for real-world applications!\n",
|
|
"\n",
|
|
"You've built the training engine that powers modern AI. Now let's add the advanced features that make it production-ready and capable of learning complex patterns from real-world data!"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"jupytext": {
|
|
"main_language": "python"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|