forked from qoherent/modrec-workflow
Michael Luciuk
9979d84e29
Documentation and formatting updates: - Updates to project README. - Adding project health files (`LICENSE` and `SECURITY.md`) - A few minor formatting changes throughout - A few typo fixes, removal of unused code, cleanup of shadowed variables, and fixed import ordering with isort. **Note:** These changes have not been tested. Co-authored-by: Michael Luciuk <michael.luciuk@gmail.com> Co-authored-by: Liyu Xiao <liyu@qoherent.ai> Reviewed-on: https://git.riahub.ai/qoherent/modrec-workflow/pulls/1 Reviewed-by: Liyux <liyux@noreply.localhost> Co-authored-by: Michael Luciuk <michael@qoherent.ai> Co-committed-by: Michael Luciuk <michael@qoherent.ai>
84 lines
2.3 KiB
YAML
84 lines
2.3 KiB
YAML
dataset:
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# Seed for the random number generator, used for signal generation
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seed: 42
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# Number of samples per recording
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recording_length: 1024
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# List of signal modulation schemes to include in the dataset
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modulation_types:
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- bpsk
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- qpsk
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- qam16
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- qam64
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# Rolloff factor for pulse shaping filter (0 < beta <= 1)
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beta: 0.3
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# Samples per symbol (determines bandwidth of the digital signal)
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sps: 4
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# SNR sweep range: start, stop (exclusive), and step (in dB)
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snr_start: -6
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snr_stop: 13
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snr_step: 3
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# Number of iterations (signal recordings) per modulation and SNR combination
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num_iterations: 3
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# Modulation scheme settings; keys must match the `modulation_types` list above
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# Each entry includes:
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# - num_bits_per_symbol: bits encoded per symbol (e.g., 1 for BPSK, 4 for 16-QAM)
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# - constellation_type: modulation category (e.g., "psk", "qam", "fsk", "ofdm")
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# TODO: Combine entries for 'modulation_types' and 'modulation_settings'
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modulation_settings:
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bpsk:
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num_bits_per_symbol: 1
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constellation_type: psk
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qpsk:
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num_bits_per_symbol: 2
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constellation_type: psk
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qam16:
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num_bits_per_symbol: 4
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constellation_type: qam
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qam64:
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num_bits_per_symbol: 6
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constellation_type: qam
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# Number of slices to cut from each recording
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num_slices: 8
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# Training and validation split ratios; must sum to 1
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train_split: 0.8
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val_split : 0.2
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training:
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# Number of training examples processed together before the model updates its weights
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batch_size: 256
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# Number of complete passes through the training dataset during training
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epochs: 5
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# Learning rate: step size for weight updates after each batch
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# Recommended range for fine-tuning: 1e-6 to 1e-4
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learning_rate: 1e-4
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# Enable GPU acceleration for training if available
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use_gpu: true
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# Dropout rate for individual neurons/layers (probability of dropping out a unit)
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drop_rate: 0.5
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# Drop path rate: probability of dropping entire residual paths (stochastic depth)
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drop_path_rate: 0.2
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# Weight decay (L2 regularization) coefficient to help prevent overfitting
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wd: 0.01
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app:
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# Optimization style for ORT conversion; options: 'Fixed', 'None'
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optimization_style: "Fixed"
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# Target platform architecture; common options: 'amd64', 'arm64'
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target_platform: "amd64"
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