forked from qoherent/modrec-workflow
85 lines
2.4 KiB
YAML
85 lines
2.4 KiB
YAML
general:
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# Run mode. Options are 'prod' or 'dev'.
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run_mode: prod
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dataset:
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#number of slices you want to split each recording into
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num_slices: 8
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#training/val split between the 2 data sets
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train_split: 0.8
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val_split : 0.2
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#used to initialize a random number generator.
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seed: 25
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#multiple modulations to contain in the dataset
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modulation_types: [bpsk, qpsk, qam16, 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 # Start value of SNR sweep (in dB)
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snr_stop: 13 # Stop value (exclusive) of SNR sweep (in dB)
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snr_step: 3 # Step size for SNR sweep (in dB)
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# Number of iterations (samples) per modulation and SNR combination
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num_iterations: 3
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# Number of samples per generated recording
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recording_length: 1024
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# Settings for each modulation scheme
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# Keys must match entries in `modulation_types`
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# - `num_bits_per_symbol`: how many bits each symbol encodes (e.g., 1 for BPSK, 4 for 16-QAM)
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# - `constellation_type`: type of modulation (e.g., "psk", "qam", "fsk", "ofdm")
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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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training:
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# Number of training samples 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: how much weights are updated after every batch
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# Suggested range for fine-tuning: 1e-6 to 1e-4
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learning_rate: 1e-4
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# Whether to use 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) 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 |