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ca8ec10427
- Remove redundant fields from module.yaml files: exports_to, files, components - Keep only essential system metadata: name, title, description, dependencies - Export command now reads actual export targets from dev files (#| default_exp directive) - Status command updated to use dev files as source of truth for export targets - Export command shows detailed source → target mapping for better clarity - Dependencies field retained as it's useful for CLI module ordering and prerequisites - Eliminates duplication between YAML and dev files - dev files are the real truth
138 lines
4.0 KiB
Python
138 lines
4.0 KiB
Python
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/activations/activations_dev.ipynb.
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# %% auto 0
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__all__ = ['ReLU', 'Sigmoid', 'Tanh']
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# %% ../../modules/activations/activations_dev.ipynb 5
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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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The most popular activation function in deep learning.
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Simple, effective, and computationally efficient.
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TODO: Implement ReLU activation function.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply ReLU: f(x) = max(0, x)
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Args:
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x: Input tensor
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Returns:
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Output tensor with ReLU applied element-wise
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TODO: Implement element-wise max(0, x) operation
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Hint: Use np.maximum(0, x.data)
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"""
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raise NotImplementedError("Student implementation required")
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def __call__(self, x: Tensor) -> Tensor:
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"""Make activation callable: relu(x) same as relu.forward(x)"""
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return self.forward(x)
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# %% ../../modules/activations/activations_dev.ipynb 6
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class ReLU:
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"""ReLU Activation: f(x) = max(0, x)"""
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def forward(self, x: Tensor) -> Tensor:
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"""Apply ReLU: f(x) = max(0, x)"""
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return Tensor(np.maximum(0, x.data))
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def __call__(self, x: Tensor) -> Tensor:
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return self.forward(x)
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# %% ../../modules/activations/activations_dev.ipynb 12
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class Sigmoid:
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"""
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Sigmoid Activation: f(x) = 1 / (1 + e^(-x))
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Squashes input to range (0, 1). Often used for binary classification.
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TODO: Implement Sigmoid activation function.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply Sigmoid: f(x) = 1 / (1 + e^(-x))
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Args:
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x: Input tensor
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Returns:
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Output tensor with Sigmoid applied element-wise
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TODO: Implement sigmoid function (be careful with numerical stability!)
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Hint: For numerical stability, use:
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- For x >= 0: sigmoid(x) = 1 / (1 + exp(-x))
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- For x < 0: sigmoid(x) = exp(x) / (1 + exp(x))
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"""
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raise NotImplementedError("Student implementation required")
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def __call__(self, x: Tensor) -> Tensor:
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return self.forward(x)
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# %% ../../modules/activations/activations_dev.ipynb 13
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class Sigmoid:
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"""Sigmoid Activation: f(x) = 1 / (1 + e^(-x))"""
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def forward(self, x: Tensor) -> Tensor:
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"""Apply Sigmoid with numerical stability"""
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# Use the numerically stable version to avoid overflow
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# For x >= 0: sigmoid(x) = 1 / (1 + exp(-x))
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# For x < 0: sigmoid(x) = exp(x) / (1 + exp(x))
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x_data = x.data
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result = np.zeros_like(x_data)
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# Stable computation
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positive_mask = x_data >= 0
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result[positive_mask] = 1.0 / (1.0 + np.exp(-x_data[positive_mask]))
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result[~positive_mask] = np.exp(x_data[~positive_mask]) / (1.0 + np.exp(x_data[~positive_mask]))
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return Tensor(result)
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def __call__(self, x: Tensor) -> Tensor:
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return self.forward(x)
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# %% ../../modules/activations/activations_dev.ipynb 19
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class Tanh:
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"""
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Tanh Activation: f(x) = tanh(x)
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Squashes input to range (-1, 1). Zero-centered output.
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TODO: Implement Tanh activation function.
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"""
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def forward(self, x: Tensor) -> Tensor:
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"""
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Apply Tanh: f(x) = tanh(x)
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Args:
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x: Input tensor
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Returns:
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Output tensor with Tanh applied element-wise
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TODO: Implement tanh function
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Hint: Use np.tanh(x.data)
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"""
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raise NotImplementedError("Student implementation required")
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def __call__(self, x: Tensor) -> Tensor:
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return self.forward(x)
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# %% ../../modules/activations/activations_dev.ipynb 20
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class Tanh:
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"""Tanh Activation: f(x) = tanh(x)"""
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def forward(self, x: Tensor) -> Tensor:
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"""Apply Tanh"""
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return Tensor(np.tanh(x.data))
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def __call__(self, x: Tensor) -> Tensor:
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return self.forward(x)
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