mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-04-29 00:59:17 -05:00
5ae68dd4b4
CRITICAL FIX: Gradients now flow through entire training stack! Changes: 1. Enable autograd in __init__.py - patches Tensor operations on import 2. Extend enable_autograd() to patch Sigmoid and BCE forward methods 3. Fix gradient accumulation to handle broadcasting (bias gradients) 4. Fix optimizer.step() - param.grad is numpy array, not Tensor.data 5. Add debug_gradients.py for systematic gradient flow testing Architecture: - Clean patching pattern - all gradient tracking in enable_autograd() - Activations/losses remain simple (Module 02/04) - Autograd (Module 05) upgrades them with gradient tracking - Pedagogically sound: separation of concerns Results: ✅ All 6 debug tests pass ✅ Perceptron learns: 50% → 93% accuracy ✅ Loss decreases: 0.79 → 0.36 ✅ Weights update correctly through SGD
269 lines
9.4 KiB
Python
Generated
269 lines
9.4 KiB
Python
Generated
# ╔═══════════════════════════════════════════════════════════════════════════════╗
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# ║ 🚨 CRITICAL WARNING 🚨 ║
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# ║ AUTOGENERATED! DO NOT EDIT! ║
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# ║ ║
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# ║ This file is AUTOMATICALLY GENERATED from source modules. ║
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# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║
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# ║ ║
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# ║ ✅ TO EDIT: modules/source/03_activations/activations_dev.py ║
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# ║ ✅ TO EXPORT: Run 'tito module complete <module_name>' ║
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# ║ ║
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# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║
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# ║ Editing it directly may break module functionality and training. ║
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# ║ ║
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# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║
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# ║ happens! The tinytorch/ directory is just the compiled output. ║
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# ╚═══════════════════════════════════════════════════════════════════════════════╝
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# %% auto 0
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__all__ = ['Sigmoid', 'ReLU', 'Tanh', 'GELU', 'Softmax']
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# %% ../../modules/source/02_activations/activations_dev.ipynb 3
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import numpy as np
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from typing import Optional
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import sys
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import os
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# Import will be in export cell
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# %% ../../modules/source/02_activations/activations_dev.ipynb 8
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from .tensor import Tensor
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class Sigmoid:
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"""
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Sigmoid activation: σ(x) = 1/(1 + e^(-x))
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Maps any real number to (0, 1) range.
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Perfect for probabilities and binary classification.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply sigmoid activation element-wise.
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TODO: Implement sigmoid function
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APPROACH:
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1. Apply sigmoid formula: 1 / (1 + exp(-x))
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2. Use np.exp for exponential
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3. Return result wrapped in new Tensor
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EXAMPLE:
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>>> sigmoid = Sigmoid()
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>>> x = Tensor([-2, 0, 2])
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>>> result = sigmoid(x)
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>>> print(result.data)
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[0.119, 0.5, 0.881] # All values between 0 and 1
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HINT: Use np.exp(-x.data) for numerical stability
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"""
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### BEGIN SOLUTION
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# Apply sigmoid: 1 / (1 + exp(-x))
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result_data = 1.0 / (1.0 + np.exp(-x.data))
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result = Tensor(result_data)
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# Track gradients if autograd is enabled and input requires_grad
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if SigmoidBackward is not None and x.requires_grad:
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result.requires_grad = True
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result._grad_fn = SigmoidBackward(x, result)
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return result
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### END SOLUTION
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def __call__(self, x: Tensor) -> Tensor:
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"""Allows the activation to be called like a function."""
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return self.forward(x)
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def backward(self, grad: Tensor) -> Tensor:
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"""Compute gradient (implemented in Module 05)."""
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pass # Will implement backward pass in Module 05
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# %% ../../modules/source/02_activations/activations_dev.ipynb 12
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class ReLU:
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"""
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ReLU activation: f(x) = max(0, x)
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Sets negative values to zero, keeps positive values unchanged.
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Most popular activation for hidden layers.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply ReLU activation element-wise.
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TODO: Implement ReLU function
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APPROACH:
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1. Use np.maximum(0, x.data) for element-wise max with zero
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2. Return result wrapped in new Tensor
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EXAMPLE:
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>>> relu = ReLU()
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>>> x = Tensor([-2, -1, 0, 1, 2])
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>>> result = relu(x)
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>>> print(result.data)
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[0, 0, 0, 1, 2] # Negative values become 0, positive unchanged
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HINT: np.maximum handles element-wise maximum automatically
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"""
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### BEGIN SOLUTION
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# Apply ReLU: max(0, x)
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result = np.maximum(0, x.data)
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return Tensor(result)
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### END SOLUTION
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def __call__(self, x: Tensor) -> Tensor:
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"""Allows the activation to be called like a function."""
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return self.forward(x)
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def backward(self, grad: Tensor) -> Tensor:
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"""Compute gradient (implemented in Module 05)."""
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pass # Will implement backward pass in Module 05
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# %% ../../modules/source/02_activations/activations_dev.ipynb 16
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class Tanh:
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"""
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Tanh activation: f(x) = (e^x - e^(-x))/(e^x + e^(-x))
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Maps any real number to (-1, 1) range.
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Zero-centered alternative to sigmoid.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply tanh activation element-wise.
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TODO: Implement tanh function
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APPROACH:
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1. Use np.tanh(x.data) for hyperbolic tangent
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2. Return result wrapped in new Tensor
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EXAMPLE:
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>>> tanh = Tanh()
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>>> x = Tensor([-2, 0, 2])
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>>> result = tanh(x)
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>>> print(result.data)
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[-0.964, 0.0, 0.964] # Range (-1, 1), symmetric around 0
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HINT: NumPy provides np.tanh function
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"""
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### BEGIN SOLUTION
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# Apply tanh using NumPy
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result = np.tanh(x.data)
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return Tensor(result)
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### END SOLUTION
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def __call__(self, x: Tensor) -> Tensor:
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"""Allows the activation to be called like a function."""
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return self.forward(x)
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def backward(self, grad: Tensor) -> Tensor:
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"""Compute gradient (implemented in Module 05)."""
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pass # Will implement backward pass in Module 05
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# %% ../../modules/source/02_activations/activations_dev.ipynb 20
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class GELU:
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"""
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GELU activation: f(x) = x * Φ(x) ≈ x * Sigmoid(1.702 * x)
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Smooth approximation to ReLU, used in modern transformers.
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Where Φ(x) is the cumulative distribution function of standard normal.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply GELU activation element-wise.
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TODO: Implement GELU approximation
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APPROACH:
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1. Use approximation: x * sigmoid(1.702 * x)
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2. Compute sigmoid part: 1 / (1 + exp(-1.702 * x))
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3. Multiply by x element-wise
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4. Return result wrapped in new Tensor
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EXAMPLE:
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>>> gelu = GELU()
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>>> x = Tensor([-1, 0, 1])
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>>> result = gelu(x)
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>>> print(result.data)
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[-0.159, 0.0, 0.841] # Smooth, like ReLU but differentiable everywhere
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HINT: The 1.702 constant comes from √(2/π) approximation
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"""
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### BEGIN SOLUTION
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# GELU approximation: x * sigmoid(1.702 * x)
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# First compute sigmoid part
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sigmoid_part = 1.0 / (1.0 + np.exp(-1.702 * x.data))
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# Then multiply by x
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result = x.data * sigmoid_part
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return Tensor(result)
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### END SOLUTION
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def __call__(self, x: Tensor) -> Tensor:
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"""Allows the activation to be called like a function."""
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return self.forward(x)
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def backward(self, grad: Tensor) -> Tensor:
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"""Compute gradient (implemented in Module 05)."""
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pass # Will implement backward pass in Module 05
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# %% ../../modules/source/02_activations/activations_dev.ipynb 24
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class Softmax:
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"""
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Softmax activation: f(x_i) = e^(x_i) / Σ(e^(x_j))
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Converts any vector to a probability distribution.
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Sum of all outputs equals 1.0.
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"""
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def forward(self, x: Tensor, dim: int = -1) -> Tensor:
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"""
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Apply softmax activation along specified dimension.
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TODO: Implement numerically stable softmax
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APPROACH:
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1. Subtract max for numerical stability: x - max(x)
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2. Compute exponentials: exp(x - max(x))
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3. Sum along dimension: sum(exp_values)
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4. Divide: exp_values / sum
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5. Return result wrapped in new Tensor
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EXAMPLE:
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>>> softmax = Softmax()
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>>> x = Tensor([1, 2, 3])
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>>> result = softmax(x)
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>>> print(result.data)
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[0.090, 0.245, 0.665] # Sums to 1.0, larger inputs get higher probability
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HINTS:
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- Use np.max(x.data, axis=dim, keepdims=True) for max
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- Use np.sum(exp_values, axis=dim, keepdims=True) for sum
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- The max subtraction prevents overflow in exponentials
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"""
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### BEGIN SOLUTION
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# Numerical stability: subtract max to prevent overflow
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x_max = np.max(x.data, axis=dim, keepdims=True)
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x_shifted = x.data - x_max
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# Compute exponentials
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exp_values = np.exp(x_shifted)
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# Sum along dimension
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exp_sum = np.sum(exp_values, axis=dim, keepdims=True)
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# Normalize to get probabilities
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result = exp_values / exp_sum
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return Tensor(result)
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### END SOLUTION
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def __call__(self, x: Tensor, dim: int = -1) -> Tensor:
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"""Allows the activation to be called like a function."""
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return self.forward(x, dim)
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def backward(self, grad: Tensor) -> Tensor:
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"""Compute gradient (implemented in Module 05)."""
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pass # Will implement backward pass in Module 05
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