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This commit introduces several changes to improve performance and code quality:
1. **CMakeLists.txt:**
* Changed compiler optimization level from -O2 to -O3.
* Added -Wall, -Wextra, and -pedantic flags to enable more warnings and enforce stricter C standards.
2. **Tensor Operations (src/pearl_tensor.c):**
* Optimized `pearl_tensor_copy` by:
* Consolidating the calculation of total elements.
* Using `memcpy` for data copying, which can be more efficient than a manual loop.
* Included `<string.h>`.
3. **Layer Updates (src/pearl_layer.c):**
* Parallelized the weight and bias update loop in `pearl_layer_update_fully_connected` using OpenMP (`#pragma omp parallel for`). This can speed up the training process for layers with a significant number of parameters.
These changes should provide a general uplift in performance, particularly during the training phase of neural networks, and improve code robustness through stricter compiler checks.
This commit introduces a functional dropout layer to the Pearl_NN library.
Key changes include:
1. **Dropout Layer Implementation (src/pearl_layer.c, src/pearl_layer.h):**
* Added `pearl_layer_forward_dropout` to handle the forward pass.
* In training mode, it randomly zeros out neurons based on the dropout rate and scales the remaining activations.
* The dropout mask is dynamically created to match input tensor dimensions.
* In inference mode, it acts as a pass-through layer.
* Added `pearl_layer_backward_dropout` to handle the backward pass, applying the same mask and scaling to gradients.
* Integrated these functions into the main `pearl_layer_forward` and `pearl_layer_backward` routines.
* Modified `pearl_layer_create_dropout` to initialize the mask tensor (`weights`) to NULL, allowing dynamic creation in the forward pass.
2. **Training/Inference Mode Control (src/pearl_network.h, src/pearl_network.c, src/pearl_layer.h, src/pearl_layer.c):**
* Added a `bool is_training` field to the `pearl_network` struct.
* This flag is set by `pearl_network_train_epoch` (true) and `pearl_network_calculate` (false).
* The `is_training` flag is propagated to `pearl_layer_forward` and used by the dropout layer to switch its behavior.
3. **Unit Tests (test/main.c):**
* Added `test_dropout_layer_forward_training_mode` to verify correct masking and scaling during training.
* Added `test_dropout_layer_forward_inference_mode` to verify pass-through behavior during inference.
* Added `test_dropout_layer_backward_pass` to verify correct gradient propagation.
* Updated `test_network_add_layers` to reflect changes in dropout layer initialization.
* Seeded `rand()` in `setUp()` for test randomness.
4. **Bug Fix (src/pearl_network.c):**
* Resolved a potential double-free issue in `pearl_network_destroy` by removing a redundant `free` call for the input layer.
The dropout layer provides a common regularization technique to prevent overfitting in neural networks. These changes enhance the library's capabilities and robustness.
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