Add cnn model - #13273
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| self.filters = np.random.randn(8, input_shape[0], 3, 3) * 0.1 # 8 filters | ||
| self.fc_weights = np.random.randn(8 * 26 * 26, num_classes) * 0.1 | ||
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| def relu(self, x: np.ndarray) -> np.ndarray: |
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| """Apply ReLU activation.""" | ||
| return np.maximum(0, x) | ||
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| def convolve(self, x: np.ndarray, filters: np.ndarray) -> np.ndarray: |
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| output[f, i, j] = np.sum(region * filters[f]) | ||
| return output | ||
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| def flatten(self, x: np.ndarray) -> np.ndarray: |
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| """Flatten the feature map.""" | ||
| return x.reshape(-1) | ||
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| def forward(self, x: np.ndarray) -> np.ndarray: |
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Describe your change:
This pull request adds a simple Convolutional Neural Network (CNN) implementation for image classification using NumPy. The model includes a single convolutional layer followed by ReLU activation, flattening, and a fully connected output layer. It demonstrates a forward pass on a dummy input and returns class logits.
The code includes:
→ https://en.wikipedia.org/wiki/Convolutional_neural_network
Checklist: