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Export: Training and Optimizers modules to TinyTorch package
- Exported 09_training module using nbdev directly from Python file - Exported 08_optimizers module to resolve import dependencies - All training components now available in tinytorch.core.training: * MeanSquaredError, CrossEntropyLoss, BinaryCrossEntropyLoss * Accuracy metric * Trainer class with complete training orchestration - All optimizers now available in tinytorch.core.optimizers: * SGD, Adam optimizers * StepLR learning rate scheduler - All components properly exported and functional - Integration tests passing (17/17) - Inline tests passing (6/6) - tito CLI integration working correctly Package exports: - tinytorch.core.training: 688 lines, 5 main classes - tinytorch.core.optimizers: 17,396 bytes, complete optimizer suite - Clean separation of development vs package code - Ready for production use and further development
This commit is contained in:
Vijay Janapa Reddi
1754
modules/source/08_optimizers/optimizers_dev.ipynb
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1754
modules/source/08_optimizers/optimizers_dev.ipynb
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tinytorch/core/optimizers.py
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tinytorch/core/optimizers.py
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/08_optimizers/optimizers_dev.ipynb.
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# %% auto 0
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__all__ = ['setup_import_paths', 'gradient_descent_step', 'SGD', 'Adam', 'StepLR']
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# %% ../../modules/source/08_optimizers/optimizers_dev.ipynb 1
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import math
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import numpy as np
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import sys
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import os
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from typing import List, Dict, Any, Optional, Union
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from collections import defaultdict
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# Helper function to set up import paths
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def setup_import_paths():
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"""Set up import paths for development modules."""
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import sys
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import os
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# Add module directories to path
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base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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tensor_dir = os.path.join(base_dir, '01_tensor')
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autograd_dir = os.path.join(base_dir, '07_autograd')
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if tensor_dir not in sys.path:
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sys.path.append(tensor_dir)
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if autograd_dir not in sys.path:
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sys.path.append(autograd_dir)
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# Import our existing components
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.autograd import Variable
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except ImportError:
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# For development, try local imports
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try:
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setup_import_paths()
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from tensor_dev import Tensor
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from autograd_dev import Variable
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except ImportError:
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# Create minimal fallback classes for testing
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print("Warning: Using fallback classes for testing")
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class Tensor:
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def __init__(self, data):
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self.data = np.array(data)
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self.shape = self.data.shape
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def __str__(self):
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return f"Tensor({self.data})"
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class Variable:
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def __init__(self, data, requires_grad=True):
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if isinstance(data, (int, float)):
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self.data = Tensor([data])
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else:
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self.data = Tensor(data)
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self.requires_grad = requires_grad
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self.grad = None
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def zero_grad(self):
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self.grad = None
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def __str__(self):
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return f"Variable({self.data.data})"
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# %% ../../modules/source/08_optimizers/optimizers_dev.ipynb 6
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def gradient_descent_step(parameter: Variable, learning_rate: float) -> None:
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"""
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Perform one step of gradient descent on a parameter.
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Args:
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parameter: Variable with gradient information
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learning_rate: How much to update parameter
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TODO: Implement basic gradient descent parameter update.
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STEP-BY-STEP IMPLEMENTATION:
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1. Check if parameter has a gradient
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2. Get current parameter value and gradient
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3. Update parameter: new_value = old_value - learning_rate * gradient
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4. Update parameter data with new value
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5. Handle edge cases (no gradient, invalid values)
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EXAMPLE USAGE:
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```python
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# Parameter with gradient
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w = Variable(2.0, requires_grad=True)
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w.grad = Variable(0.5) # Gradient from loss
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# Update parameter
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gradient_descent_step(w, learning_rate=0.1)
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# w.data now contains: 2.0 - 0.1 * 0.5 = 1.95
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```
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IMPLEMENTATION HINTS:
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- Check if parameter.grad is not None
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- Use parameter.grad.data.data to get gradient value
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- Update parameter.data with new Tensor
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- Don't modify gradient (it's used for logging)
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LEARNING CONNECTIONS:
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- This is the foundation of all neural network training
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- PyTorch's optimizer.step() does exactly this
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- The learning rate determines convergence speed
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"""
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### BEGIN SOLUTION
|
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if parameter.grad is not None:
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# Get current parameter value and gradient
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current_value = parameter.data.data
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gradient_value = parameter.grad.data.data
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# Update parameter: new_value = old_value - learning_rate * gradient
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new_value = current_value - learning_rate * gradient_value
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# Update parameter data
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parameter.data = Tensor(new_value)
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### END SOLUTION
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# %% ../../modules/source/08_optimizers/optimizers_dev.ipynb 10
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class SGD:
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"""
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SGD Optimizer with Momentum
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Implements stochastic gradient descent with momentum:
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v_t = momentum * v_{t-1} + gradient
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parameter = parameter - learning_rate * v_t
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"""
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def __init__(self, parameters: List[Variable], learning_rate: float = 0.01,
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momentum: float = 0.0, weight_decay: float = 0.0):
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"""
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Initialize SGD optimizer.
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Args:
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parameters: List of Variables to optimize
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learning_rate: Learning rate (default: 0.01)
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momentum: Momentum coefficient (default: 0.0)
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weight_decay: L2 regularization coefficient (default: 0.0)
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TODO: Implement SGD optimizer initialization.
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APPROACH:
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1. Store parameters and hyperparameters
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2. Initialize momentum buffers for each parameter
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3. Set up state tracking for optimization
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4. Prepare for step() and zero_grad() methods
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EXAMPLE:
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```python
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# Create optimizer
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optimizer = SGD([w1, w2, b1, b2], learning_rate=0.01, momentum=0.9)
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|
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# In training loop:
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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```
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|
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HINTS:
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- Store parameters as a list
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- Initialize momentum buffers as empty dict
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- Use parameter id() as key for momentum tracking
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- Momentum buffers will be created lazily in step()
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"""
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### BEGIN SOLUTION
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self.parameters = parameters
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.weight_decay = weight_decay
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# Initialize momentum buffers (created lazily)
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self.momentum_buffers = {}
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# Track optimization steps
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self.step_count = 0
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### END SOLUTION
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def step(self) -> None:
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"""
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Perform one optimization step.
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TODO: Implement SGD parameter update with momentum.
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APPROACH:
|
||||
1. Iterate through all parameters
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2. For each parameter with gradient:
|
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a. Get current gradient
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b. Apply weight decay if specified
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c. Update momentum buffer (or create if first time)
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||||
d. Update parameter using momentum
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3. Increment step count
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|
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MATHEMATICAL FORMULATION:
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- If weight_decay > 0: gradient = gradient + weight_decay * parameter
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- momentum_buffer = momentum * momentum_buffer + gradient
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- parameter = parameter - learning_rate * momentum_buffer
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IMPLEMENTATION HINTS:
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- Use id(param) as key for momentum buffers
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- Initialize buffer with zeros if not exists
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- Handle case where momentum = 0 (no momentum)
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- Update parameter.data with new Tensor
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"""
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### BEGIN SOLUTION
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for param in self.parameters:
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if param.grad is not None:
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# Get gradient
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gradient = param.grad.data.data
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# Apply weight decay (L2 regularization)
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if self.weight_decay > 0:
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gradient = gradient + self.weight_decay * param.data.data
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# Get or create momentum buffer
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param_id = id(param)
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if param_id not in self.momentum_buffers:
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self.momentum_buffers[param_id] = np.zeros_like(param.data.data)
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# Update momentum buffer
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self.momentum_buffers[param_id] = (
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self.momentum * self.momentum_buffers[param_id] + gradient
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)
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# Update parameter
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param.data = Tensor(
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param.data.data - self.learning_rate * self.momentum_buffers[param_id]
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)
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self.step_count += 1
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### END SOLUTION
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def zero_grad(self) -> None:
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"""
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Zero out gradients for all parameters.
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TODO: Implement gradient zeroing.
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APPROACH:
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1. Iterate through all parameters
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2. Set gradient to None for each parameter
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3. This prepares for next backward pass
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IMPLEMENTATION HINTS:
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- Simply set param.grad = None
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- This is called before loss.backward()
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- Essential for proper gradient accumulation
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"""
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### BEGIN SOLUTION
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for param in self.parameters:
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param.grad = None
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### END SOLUTION
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# %% ../../modules/source/08_optimizers/optimizers_dev.ipynb 14
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class Adam:
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"""
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Adam Optimizer
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Implements Adam algorithm with adaptive learning rates:
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- First moment: exponential moving average of gradients
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- Second moment: exponential moving average of squared gradients
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- Bias correction: accounts for initialization bias
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- Adaptive updates: different learning rate per parameter
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"""
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def __init__(self, parameters: List[Variable], learning_rate: float = 0.001,
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beta1: float = 0.9, beta2: float = 0.999, epsilon: float = 1e-8,
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weight_decay: float = 0.0):
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"""
|
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Initialize Adam optimizer.
|
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Args:
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parameters: List of Variables to optimize
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learning_rate: Learning rate (default: 0.001)
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beta1: Exponential decay rate for first moment (default: 0.9)
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beta2: Exponential decay rate for second moment (default: 0.999)
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epsilon: Small constant for numerical stability (default: 1e-8)
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weight_decay: L2 regularization coefficient (default: 0.0)
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TODO: Implement Adam optimizer initialization.
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APPROACH:
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1. Store parameters and hyperparameters
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2. Initialize first moment buffers (m_t)
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3. Initialize second moment buffers (v_t)
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4. Set up step counter for bias correction
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EXAMPLE:
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```python
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# Create Adam optimizer
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optimizer = Adam([w1, w2, b1, b2], learning_rate=0.001)
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# In training loop:
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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```
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HINTS:
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- Store all hyperparameters
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- Initialize moment buffers as empty dicts
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- Use parameter id() as key for tracking
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- Buffers will be created lazily in step()
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"""
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### BEGIN SOLUTION
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self.parameters = parameters
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
|
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self.epsilon = epsilon
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self.weight_decay = weight_decay
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# Initialize moment buffers (created lazily)
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self.first_moment = {} # m_t
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self.second_moment = {} # v_t
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# Track optimization steps for bias correction
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self.step_count = 0
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### END SOLUTION
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|
||||
def step(self) -> None:
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"""
|
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Perform one optimization step using Adam algorithm.
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|
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TODO: Implement Adam parameter update.
|
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|
||||
APPROACH:
|
||||
1. Increment step count
|
||||
2. For each parameter with gradient:
|
||||
a. Get current gradient
|
||||
b. Apply weight decay if specified
|
||||
c. Update first moment (momentum)
|
||||
d. Update second moment (variance)
|
||||
e. Apply bias correction
|
||||
f. Update parameter with adaptive learning rate
|
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|
||||
MATHEMATICAL FORMULATION:
|
||||
- m_t = beta1 * m_{t-1} + (1 - beta1) * gradient
|
||||
- v_t = beta2 * v_{t-1} + (1 - beta2) * gradient^2
|
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- m_hat = m_t / (1 - beta1^t)
|
||||
- v_hat = v_t / (1 - beta2^t)
|
||||
- parameter = parameter - learning_rate * m_hat / (sqrt(v_hat) + epsilon)
|
||||
|
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IMPLEMENTATION HINTS:
|
||||
- Use id(param) as key for moment buffers
|
||||
- Initialize buffers with zeros if not exists
|
||||
- Use np.sqrt() for square root
|
||||
- Handle numerical stability with epsilon
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
self.step_count += 1
|
||||
|
||||
for param in self.parameters:
|
||||
if param.grad is not None:
|
||||
# Get gradient
|
||||
gradient = param.grad.data.data
|
||||
|
||||
# Apply weight decay (L2 regularization)
|
||||
if self.weight_decay > 0:
|
||||
gradient = gradient + self.weight_decay * param.data.data
|
||||
|
||||
# Get or create moment buffers
|
||||
param_id = id(param)
|
||||
if param_id not in self.first_moment:
|
||||
self.first_moment[param_id] = np.zeros_like(param.data.data)
|
||||
self.second_moment[param_id] = np.zeros_like(param.data.data)
|
||||
|
||||
# Update first moment (momentum)
|
||||
self.first_moment[param_id] = (
|
||||
self.beta1 * self.first_moment[param_id] +
|
||||
(1 - self.beta1) * gradient
|
||||
)
|
||||
|
||||
# Update second moment (variance)
|
||||
self.second_moment[param_id] = (
|
||||
self.beta2 * self.second_moment[param_id] +
|
||||
(1 - self.beta2) * gradient * gradient
|
||||
)
|
||||
|
||||
# Bias correction
|
||||
first_moment_corrected = (
|
||||
self.first_moment[param_id] / (1 - self.beta1 ** self.step_count)
|
||||
)
|
||||
second_moment_corrected = (
|
||||
self.second_moment[param_id] / (1 - self.beta2 ** self.step_count)
|
||||
)
|
||||
|
||||
# Update parameter with adaptive learning rate
|
||||
param.data = Tensor(
|
||||
param.data.data - self.learning_rate * first_moment_corrected /
|
||||
(np.sqrt(second_moment_corrected) + self.epsilon)
|
||||
)
|
||||
### END SOLUTION
|
||||
|
||||
def zero_grad(self) -> None:
|
||||
"""
|
||||
Zero out gradients for all parameters.
|
||||
|
||||
TODO: Implement gradient zeroing (same as SGD).
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Set param.grad = None for all parameters
|
||||
- This is identical to SGD implementation
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
for param in self.parameters:
|
||||
param.grad = None
|
||||
### END SOLUTION
|
||||
|
||||
# %% ../../modules/source/08_optimizers/optimizers_dev.ipynb 19
|
||||
class StepLR:
|
||||
"""
|
||||
Step Learning Rate Scheduler
|
||||
|
||||
Decays learning rate by gamma every step_size epochs:
|
||||
learning_rate = initial_lr * (gamma ^ (epoch // step_size))
|
||||
"""
|
||||
|
||||
def __init__(self, optimizer: Union[SGD, Adam], step_size: int, gamma: float = 0.1):
|
||||
"""
|
||||
Initialize step learning rate scheduler.
|
||||
|
||||
Args:
|
||||
optimizer: Optimizer to schedule
|
||||
step_size: Number of epochs between decreases
|
||||
gamma: Multiplicative factor for learning rate decay
|
||||
|
||||
TODO: Implement learning rate scheduler initialization.
|
||||
|
||||
APPROACH:
|
||||
1. Store optimizer reference
|
||||
2. Store scheduling parameters
|
||||
3. Save initial learning rate
|
||||
4. Initialize step counter
|
||||
|
||||
EXAMPLE:
|
||||
```python
|
||||
optimizer = SGD([w1, w2], learning_rate=0.1)
|
||||
scheduler = StepLR(optimizer, step_size=10, gamma=0.1)
|
||||
|
||||
# In training loop:
|
||||
for epoch in range(100):
|
||||
train_one_epoch()
|
||||
scheduler.step() # Update learning rate
|
||||
```
|
||||
|
||||
HINTS:
|
||||
- Store optimizer reference
|
||||
- Save initial learning rate from optimizer
|
||||
- Initialize step counter to 0
|
||||
- gamma is the decay factor (0.1 = 10x reduction)
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
self.optimizer = optimizer
|
||||
self.step_size = step_size
|
||||
self.gamma = gamma
|
||||
self.initial_lr = optimizer.learning_rate
|
||||
self.step_count = 0
|
||||
### END SOLUTION
|
||||
|
||||
def step(self) -> None:
|
||||
"""
|
||||
Update learning rate based on current step.
|
||||
|
||||
TODO: Implement learning rate update.
|
||||
|
||||
APPROACH:
|
||||
1. Increment step counter
|
||||
2. Calculate new learning rate using step decay formula
|
||||
3. Update optimizer's learning rate
|
||||
|
||||
MATHEMATICAL FORMULATION:
|
||||
new_lr = initial_lr * (gamma ^ ((step_count - 1) // step_size))
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Use // for integer division
|
||||
- Use ** for exponentiation
|
||||
- Update optimizer.learning_rate directly
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
self.step_count += 1
|
||||
|
||||
# Calculate new learning rate
|
||||
decay_factor = self.gamma ** ((self.step_count - 1) // self.step_size)
|
||||
new_lr = self.initial_lr * decay_factor
|
||||
|
||||
# Update optimizer's learning rate
|
||||
self.optimizer.learning_rate = new_lr
|
||||
### END SOLUTION
|
||||
|
||||
def get_lr(self) -> float:
|
||||
"""
|
||||
Get current learning rate.
|
||||
|
||||
TODO: Return current learning rate.
|
||||
|
||||
IMPLEMENTATION HINTS:
|
||||
- Return optimizer.learning_rate
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
return self.optimizer.learning_rate
|
||||
### END SOLUTION
|
||||
687
tinytorch/core/training.py
Normal file
687
tinytorch/core/training.py
Normal file
@@ -0,0 +1,687 @@
|
||||
# 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
|
||||
Reference in New Issue
Block a user