TinyTorch/tinytorch/core/training.py

# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/09_training/training_dev.ipynb.

# %% auto 0
__all__ = ['setup_import_paths', 'MeanSquaredError', 'CrossEntropyLoss', 'BinaryCrossEntropyLoss', 'Accuracy', 'Trainer']

# %% ../../modules/source/09_training/training_dev.ipynb 1
import numpy as np
import sys
import os
import pickle
import json
from pathlib import Path
from typing import List, Dict, Any, Optional, Union, Callable, Tuple
from collections import defaultdict
import time

# Helper function to set up import paths
def setup_import_paths():
    """Set up import paths for development modules."""
    import sys
    import os
    
    # Add module directories to path
    base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    module_dirs = [
        '01_tensor', '02_activations', '03_layers', '04_networks', 
        '05_cnn', '06_dataloader', '07_autograd', '08_optimizers'
    ]
    
    for module_dir in module_dirs:
        sys.path.append(os.path.join(base_dir, module_dir))

# Set up paths
setup_import_paths()

# Import all the building blocks we need
try:
    from tinytorch.core.tensor import Tensor
    from tinytorch.core.activations import ReLU, Sigmoid, Tanh, Softmax
    from tinytorch.core.layers import Dense
    from tinytorch.core.networks import Sequential, create_mlp
    from tinytorch.core.cnn import Conv2D, flatten
    from tinytorch.core.dataloader import Dataset, DataLoader
    from tinytorch.core.autograd import Variable
    from tinytorch.core.optimizers import SGD, Adam, StepLR
except ImportError:
    # For development, create mock classes or import from local modules
    try:
        from tensor_dev import Tensor
        from activations_dev import ReLU, Sigmoid, Tanh, Softmax
        from layers_dev import Dense
        from networks_dev import Sequential, create_mlp
        from cnn_dev import Conv2D, flatten
        from dataloader_dev import Dataset, DataLoader
        from autograd_dev import Variable
        from optimizers_dev import SGD, Adam, StepLR
    except ImportError:
        # Create minimal mock classes for development
        class Tensor:
            def __init__(self, data):
                self.data = np.array(data)
            def __str__(self):
                return f"Tensor({self.data})"
        
        class Variable:
            def __init__(self, data, requires_grad=True):
                self.data = Tensor(data)
                self.requires_grad = requires_grad
                self.grad = None
            
            def zero_grad(self):
                self.grad = None
            
            def backward(self):
                if self.requires_grad:
                    self.grad = Variable(1.0, requires_grad=False)
            
            def __str__(self):
                return f"Variable({self.data})"
        
        class SGD:
            def __init__(self, parameters, learning_rate=0.01):
                self.parameters = parameters
                self.learning_rate = learning_rate
            
            def zero_grad(self):
                for param in self.parameters:
                    if hasattr(param, 'zero_grad'):
                        param.zero_grad()
            
            def step(self):
                pass
        
        class Sequential:
            def __init__(self, layers=None):
                self.layers = layers or []
            
            def __call__(self, x):
                for layer in self.layers:
                    x = layer(x)
                return x
        
        class DataLoader:
            def __init__(self, dataset, batch_size=32, shuffle=True):
                self.dataset = dataset
                self.batch_size = batch_size
                self.shuffle = shuffle
            
            def __iter__(self):
                return iter([(Tensor([1, 2, 3]), Tensor([0]))])

# %% ../../modules/source/09_training/training_dev.ipynb 4
class MeanSquaredError:
    """
    Mean Squared Error Loss for Regression
    
    Measures the average squared difference between predictions and targets.
    MSE = (1/n) * Σ(y_pred - y_true)²
    """
    
    def __init__(self):
        """Initialize MSE loss function."""
        pass
    
    def __call__(self, y_pred: Tensor, y_true: Tensor) -> Tensor:
        """
        Compute MSE loss between predictions and targets.
        
        Args:
            y_pred: Model predictions (shape: [batch_size, ...])
            y_true: True targets (shape: [batch_size, ...])
            
        Returns:
            Scalar loss value
            
        TODO: Implement Mean Squared Error loss computation.
        
        APPROACH:
        1. Compute difference: diff = y_pred - y_true
        2. Square the differences: squared_diff = diff²
        3. Take mean over all elements: mean(squared_diff)
        4. Return as scalar Tensor
        
        EXAMPLE:
        y_pred = Tensor([[1.0, 2.0], [3.0, 4.0]])
        y_true = Tensor([[1.5, 2.5], [2.5, 3.5]])
        loss = mse_loss(y_pred, y_true)
        # Should return: mean([(1.0-1.5)², (2.0-2.5)², (3.0-2.5)², (4.0-3.5)²])
        #                = mean([0.25, 0.25, 0.25, 0.25]) = 0.25
        
        HINTS:
        - Use tensor subtraction: y_pred - y_true
        - Use element-wise multiplication for squaring: diff * diff
        - Use np.mean() to get the average
        - Return Tensor(scalar_value)
        """
        ### BEGIN SOLUTION
        # Compute difference
        diff = y_pred - y_true
        
        # Square the differences
        squared_diff = diff * diff
        
        # Take mean over all elements
        mean_loss = np.mean(squared_diff.data)
        
        return Tensor(mean_loss)
        ### END SOLUTION
    
    def forward(self, y_pred: Tensor, y_true: Tensor) -> Tensor:
        """Alternative interface for forward pass."""
        return self.__call__(y_pred, y_true)

# %% ../../modules/source/09_training/training_dev.ipynb 7
class CrossEntropyLoss:
    """
    Cross-Entropy Loss for Multi-Class Classification
    
    Measures the difference between predicted probability distribution and true labels.
    CrossEntropy = -Σ y_true * log(y_pred)
    """
    
    def __init__(self):
        """Initialize CrossEntropy loss function."""
        pass
    
    def __call__(self, y_pred: Tensor, y_true: Tensor) -> Tensor:
        """
        Compute CrossEntropy loss between predictions and targets.
        
        Args:
            y_pred: Model predictions (shape: [batch_size, num_classes])
            y_true: True class indices (shape: [batch_size]) or one-hot (shape: [batch_size, num_classes])
            
        Returns:
            Scalar loss value
            
        TODO: Implement Cross-Entropy loss computation.
        
        APPROACH:
        1. Handle both class indices and one-hot encoded labels
        2. Apply softmax to predictions for probability distribution
        3. Compute log probabilities: log(softmax(y_pred))
        4. Calculate cross-entropy: -mean(y_true * log_probs)
        5. Return scalar loss
        
        EXAMPLE:
        y_pred = Tensor([[2.0, 1.0, 0.1], [0.5, 2.1, 0.9]])  # Raw logits
        y_true = Tensor([0, 1])  # Class indices
        loss = crossentropy_loss(y_pred, y_true)
        # Should apply softmax then compute -log(prob_of_correct_class)
        
        HINTS:
        - Use softmax: exp(x) / sum(exp(x)) for probability distribution
        - Add small epsilon (1e-15) to avoid log(0)
        - Handle both class indices and one-hot encoding
        - Use np.log for logarithm computation
        """
        ### BEGIN SOLUTION
        # Handle both 1D and 2D prediction arrays
        if y_pred.data.ndim == 1:
            # Reshape 1D to 2D for consistency (single sample)
            y_pred_2d = y_pred.data.reshape(1, -1)
        else:
            y_pred_2d = y_pred.data
            
        # Apply softmax to get probability distribution
        exp_pred = np.exp(y_pred_2d - np.max(y_pred_2d, axis=1, keepdims=True))
        softmax_pred = exp_pred / np.sum(exp_pred, axis=1, keepdims=True)
        
        # Add small epsilon to avoid log(0)
        epsilon = 1e-15
        softmax_pred = np.clip(softmax_pred, epsilon, 1.0 - epsilon)
        
        # Handle class indices vs one-hot encoding
        if len(y_true.data.shape) == 1:
            # y_true contains class indices
            batch_size = y_true.data.shape[0]
            log_probs = np.log(softmax_pred[np.arange(batch_size), y_true.data.astype(int)])
            loss = -np.mean(log_probs)
        else:
            # y_true is one-hot encoded
            log_probs = np.log(softmax_pred)
            loss = -np.mean(np.sum(y_true.data * log_probs, axis=1))
        
        return Tensor(loss)
        ### END SOLUTION
    
    def forward(self, y_pred: Tensor, y_true: Tensor) -> Tensor:
        """Alternative interface for forward pass."""
        return self.__call__(y_pred, y_true)

# %% ../../modules/source/09_training/training_dev.ipynb 10
class BinaryCrossEntropyLoss:
    """
    Binary Cross-Entropy Loss for Binary Classification
    
    Measures the difference between predicted probabilities and binary labels.
    BCE = -y_true * log(y_pred) - (1-y_true) * log(1-y_pred)
    """
    
    def __init__(self):
        """Initialize Binary CrossEntropy loss function."""
        pass
    
    def __call__(self, y_pred: Tensor, y_true: Tensor) -> Tensor:
        """
        Compute Binary CrossEntropy loss between predictions and targets.
        
        Args:
            y_pred: Model predictions (shape: [batch_size, 1] or [batch_size])
            y_true: True binary labels (shape: [batch_size, 1] or [batch_size])
            
        Returns:
            Scalar loss value
            
        TODO: Implement Binary Cross-Entropy loss computation.
        
        APPROACH:
        1. Apply sigmoid to predictions for probability values
        2. Clip probabilities to avoid log(0) and log(1)
        3. Compute: -y_true * log(y_pred) - (1-y_true) * log(1-y_pred)
        4. Take mean over batch
        5. Return scalar loss
        
        EXAMPLE:
        y_pred = Tensor([[2.0], [0.0], [-1.0]])  # Raw logits
        y_true = Tensor([[1.0], [1.0], [0.0]])   # Binary labels
        loss = bce_loss(y_pred, y_true)
        # Should apply sigmoid then compute binary cross-entropy
        
        HINTS:
        - Use sigmoid: 1 / (1 + exp(-x))
        - Clip probabilities: np.clip(probs, epsilon, 1-epsilon)
        - Handle both [batch_size] and [batch_size, 1] shapes
        - Use np.log for logarithm computation
        """
        ### BEGIN SOLUTION
        # Use numerically stable implementation directly from logits
        # This avoids computing sigmoid and log separately
        logits = y_pred.data.flatten()
        labels = y_true.data.flatten()
        
        # Numerically stable binary cross-entropy from logits
        # Uses the identity: log(1 + exp(x)) = max(x, 0) + log(1 + exp(-abs(x)))
        def stable_bce_with_logits(logits, labels):
            # For each sample: -[y*log(sigmoid(x)) + (1-y)*log(1-sigmoid(x))]
            # Which equals: -[y*log_sigmoid(x) + (1-y)*log_sigmoid(-x)]
            # Where log_sigmoid(x) = x - log(1 + exp(x)) = x - softplus(x)
            
            # Compute log(sigmoid(x)) = x - log(1 + exp(x))
            # Use numerical stability: log(1 + exp(x)) = max(0, x) + log(1 + exp(-abs(x)))
            def log_sigmoid(x):
                return x - np.maximum(0, x) - np.log(1 + np.exp(-np.abs(x)))
            
            # Compute log(1 - sigmoid(x)) = -x - log(1 + exp(-x))
            def log_one_minus_sigmoid(x):
                return -x - np.maximum(0, -x) - np.log(1 + np.exp(-np.abs(x)))
            
            # Binary cross-entropy: -[y*log_sigmoid(x) + (1-y)*log_sigmoid(-x)]
            loss = -(labels * log_sigmoid(logits) + (1 - labels) * log_one_minus_sigmoid(logits))
            return loss
        
        # Compute loss for each sample
        losses = stable_bce_with_logits(logits, labels)
        
        # Take mean over batch
        mean_loss = np.mean(losses)
        
        return Tensor(mean_loss)
        ### END SOLUTION
    
    def forward(self, y_pred: Tensor, y_true: Tensor) -> Tensor:
        """Alternative interface for forward pass."""
        return self.__call__(y_pred, y_true)

# %% ../../modules/source/09_training/training_dev.ipynb 14
class Accuracy:
    """
    Accuracy Metric for Classification
    
    Computes the fraction of correct predictions.
    Accuracy = (Correct Predictions) / (Total Predictions)
    """
    
    def __init__(self):
        """Initialize Accuracy metric."""
        pass
    
    def __call__(self, y_pred: Tensor, y_true: Tensor) -> float:
        """
        Compute accuracy between predictions and targets.
        
        Args:
            y_pred: Model predictions (shape: [batch_size, num_classes] or [batch_size])
            y_true: True class labels (shape: [batch_size] or [batch_size])
            
        Returns:
            Accuracy as a float value between 0 and 1
            
        TODO: Implement accuracy computation.
        
        APPROACH:
        1. Convert predictions to class indices (argmax for multi-class)
        2. Convert true labels to class indices if needed
        3. Count correct predictions
        4. Divide by total predictions
        5. Return as float
        
        EXAMPLE:
        y_pred = Tensor([[0.9, 0.1], [0.2, 0.8], [0.6, 0.4]])  # Probabilities
        y_true = Tensor([0, 1, 0])  # True classes
        accuracy = accuracy_metric(y_pred, y_true)
        # Should return: 2/3 = 0.667 (first and second predictions correct)
        
        HINTS:
        - Use np.argmax(axis=1) for multi-class predictions
        - Handle both probability and class index inputs
        - Use np.mean() for averaging
        - Return Python float, not Tensor
        """
        ### BEGIN SOLUTION
        # Convert predictions to class indices
        if len(y_pred.data.shape) > 1 and y_pred.data.shape[1] > 1:
            # Multi-class: use argmax
            pred_classes = np.argmax(y_pred.data, axis=1)
        else:
            # Binary classification: threshold at 0.5
            pred_classes = (y_pred.data.flatten() > 0.5).astype(int)
        
        # Convert true labels to class indices if needed
        if len(y_true.data.shape) > 1 and y_true.data.shape[1] > 1:
            # One-hot encoded
            true_classes = np.argmax(y_true.data, axis=1)
        else:
            # Already class indices
            true_classes = y_true.data.flatten().astype(int)
        
        # Compute accuracy
        correct = np.sum(pred_classes == true_classes)
        total = len(true_classes)
        accuracy = correct / total
        
        return float(accuracy)
        ### END SOLUTION
    
    def forward(self, y_pred: Tensor, y_true: Tensor) -> float:
        """Alternative interface for forward pass."""
        return self.__call__(y_pred, y_true)

# %% ../../modules/source/09_training/training_dev.ipynb 18
class Trainer:
    """
    Training Loop Orchestrator
    
    Coordinates model training with loss functions, optimizers, and metrics.
    """
    
    def __init__(self, model, optimizer, loss_function, metrics=None):
        """
        Initialize trainer with model and training components.
        
        Args:
            model: Neural network model to train
            optimizer: Optimizer for parameter updates
            loss_function: Loss function for training
            metrics: List of metrics to track (optional)
            
        TODO: Initialize the trainer with all necessary components.
        
        APPROACH:
        1. Store model, optimizer, loss function, and metrics
        2. Initialize history tracking for losses and metrics
        3. Set up training state (epoch, step counters)
        4. Prepare for training and validation loops
        
        EXAMPLE:
        model = Sequential([Dense(10, 5), ReLU(), Dense(5, 2)])
        optimizer = Adam(model.parameters, learning_rate=0.001)
        loss_fn = CrossEntropyLoss()
        metrics = [Accuracy()]
        trainer = Trainer(model, optimizer, loss_fn, metrics)
        
        HINTS:
        - Store all components as instance variables
        - Initialize empty history dictionaries
        - Set metrics to empty list if None provided
        - Initialize epoch and step counters to 0
        """
        ### BEGIN SOLUTION
        self.model = model
        self.optimizer = optimizer
        self.loss_function = loss_function
        self.metrics = metrics or []
        
        # Training history
        self.history = {
            'train_loss': [],
            'val_loss': [],
            'epoch': []
        }
        
        # Add metric history tracking
        for metric in self.metrics:
            metric_name = metric.__class__.__name__.lower()
            self.history[f'train_{metric_name}'] = []
            self.history[f'val_{metric_name}'] = []
        
        # Training state
        self.current_epoch = 0
        self.current_step = 0
        ### END SOLUTION
    
    def train_epoch(self, dataloader):
        """
        Train for one epoch on the given dataloader.
        
        Args:
            dataloader: DataLoader containing training data
            
        Returns:
            Dictionary with epoch training metrics
            
        TODO: Implement single epoch training logic.
        
        APPROACH:
        1. Initialize epoch metrics tracking
        2. Iterate through batches in dataloader
        3. For each batch:
           - Zero gradients
           - Forward pass
           - Compute loss
           - Backward pass
           - Update parameters
           - Track metrics
        4. Return averaged metrics for the epoch
        
        HINTS:
        - Use optimizer.zero_grad() before each batch
        - Call loss.backward() for gradient computation
        - Use optimizer.step() for parameter updates
        - Track running averages for metrics
        """
        ### BEGIN SOLUTION
        epoch_metrics = {'loss': 0.0}
        
        # Initialize metric tracking
        for metric in self.metrics:
            metric_name = metric.__class__.__name__.lower()
            epoch_metrics[metric_name] = 0.0
        
        batch_count = 0
        
        for batch_x, batch_y in dataloader:
            # Zero gradients
            self.optimizer.zero_grad()
            
            # Forward pass
            predictions = self.model(batch_x)
            
            # Compute loss
            loss = self.loss_function(predictions, batch_y)
            
            # Backward pass (simplified - in real implementation would use autograd)
            # loss.backward()
            
            # Update parameters
            self.optimizer.step()
            
            # Track metrics
            epoch_metrics['loss'] += loss.data
            
            for metric in self.metrics:
                metric_name = metric.__class__.__name__.lower()
                metric_value = metric(predictions, batch_y)
                epoch_metrics[metric_name] += metric_value
            
            batch_count += 1
            self.current_step += 1
        
        # Average metrics over all batches
        for key in epoch_metrics:
            epoch_metrics[key] /= batch_count
        
        return epoch_metrics
        ### END SOLUTION
    
    def validate_epoch(self, dataloader):
        """
        Validate for one epoch on the given dataloader.
        
        Args:
            dataloader: DataLoader containing validation data
            
        Returns:
            Dictionary with epoch validation metrics
            
        TODO: Implement single epoch validation logic.
        
        APPROACH:
        1. Initialize epoch metrics tracking
        2. Iterate through batches in dataloader
        3. For each batch:
           - Forward pass (no gradient computation)
           - Compute loss
           - Track metrics
        4. Return averaged metrics for the epoch
        
        HINTS:
        - No gradient computation needed for validation
        - No parameter updates during validation
        - Similar to train_epoch but simpler
        """
        ### BEGIN SOLUTION
        epoch_metrics = {'loss': 0.0}
        
        # Initialize metric tracking
        for metric in self.metrics:
            metric_name = metric.__class__.__name__.lower()
            epoch_metrics[metric_name] = 0.0
        
        batch_count = 0
        
        for batch_x, batch_y in dataloader:
            # Forward pass only (no gradients needed)
            predictions = self.model(batch_x)
            
            # Compute loss
            loss = self.loss_function(predictions, batch_y)
            
            # Track metrics
            epoch_metrics['loss'] += loss.data
            
            for metric in self.metrics:
                metric_name = metric.__class__.__name__.lower()
                metric_value = metric(predictions, batch_y)
                epoch_metrics[metric_name] += metric_value
            
            batch_count += 1
        
        # Average metrics over all batches
        for key in epoch_metrics:
            epoch_metrics[key] /= batch_count
        
        return epoch_metrics
        ### END SOLUTION
    
    def fit(self, train_dataloader, val_dataloader=None, epochs=10, verbose=True):
        """
        Train the model for specified number of epochs.
        
        Args:
            train_dataloader: Training data
            val_dataloader: Validation data (optional)
            epochs: Number of training epochs
            verbose: Whether to print training progress
            
        Returns:
            Training history dictionary
            
        TODO: Implement complete training loop.
        
        APPROACH:
        1. Loop through epochs
        2. For each epoch:
           - Train on training data
           - Validate on validation data (if provided)
           - Update history
           - Print progress (if verbose)
        3. Return complete training history
        
        HINTS:
        - Use train_epoch() and validate_epoch() methods
        - Update self.history with results
        - Print epoch summary if verbose=True
        """
        ### BEGIN SOLUTION
        print(f"Starting training for {epochs} epochs...")
        
        for epoch in range(epochs):
            self.current_epoch = epoch
            
            # Training phase
            train_metrics = self.train_epoch(train_dataloader)
            
            # Validation phase
            val_metrics = {}
            if val_dataloader is not None:
                val_metrics = self.validate_epoch(val_dataloader)
            
            # Update history
            self.history['epoch'].append(epoch)
            self.history['train_loss'].append(train_metrics['loss'])
            
            if val_dataloader is not None:
                self.history['val_loss'].append(val_metrics['loss'])
            
            # Update metric history
            for metric in self.metrics:
                metric_name = metric.__class__.__name__.lower()
                self.history[f'train_{metric_name}'].append(train_metrics[metric_name])
                if val_dataloader is not None:
                    self.history[f'val_{metric_name}'].append(val_metrics[metric_name])
            
            # Print progress
            if verbose:
                train_loss = train_metrics['loss']
                print(f"Epoch {epoch+1}/{epochs} - train_loss: {train_loss:.4f}", end="")
                
                if val_dataloader is not None:
                    val_loss = val_metrics['loss']
                    print(f" - val_loss: {val_loss:.4f}", end="")
                
                for metric in self.metrics:
                    metric_name = metric.__class__.__name__.lower()
                    train_metric = train_metrics[metric_name]
                    print(f" - train_{metric_name}: {train_metric:.4f}", end="")
                    
                    if val_dataloader is not None:
                        val_metric = val_metrics[metric_name]
                        print(f" - val_{metric_name}: {val_metric:.4f}", end="")
                
                print()  # New line
        
        print("Training completed!")
        return self.history
        ### END SOLUTION