conf/app.yaml

@@ -1,20 +1,16 @@
-general:
-  # Run mode. Options are 'prod' or 'dev'.
-  run_mode: prod
-
 dataset:
-  #number of slices you want to split each recording into
+  # Seed for the random number generator, used for signal generation
-  num_slices: 8
+  seed: 42
 
-  #training/val split between the 2 data sets
+  # Number of samples per recording
-  train_split: 0.8
+  recording_length: 1024
-  val_split : 0.2
 
-  #used to initialize a random number generator.
+  # List of signal modulation schemes to include in the dataset
-  seed: 25
+  modulation_types:
-
+    - bpsk
-  #multiple modulations to contain in the dataset
+    - qpsk
-  modulation_types: [bpsk, qpsk, qam16, qam64]
+    - qam16
+    - qam64
 
   # Rolloff factor for pulse shaping filter (0 < beta <= 1)
   beta: 0.3
@@ -23,20 +19,18 @@ dataset:
   sps: 4
 
   # SNR sweep range: start, stop (exclusive), and step (in dB)
-  snr_start: -6     # Start value of SNR sweep (in dB)
+  snr_start: -6
-  snr_stop: 13      # Stop value (exclusive) of SNR sweep (in dB)
+  snr_stop: 13
-  snr_step: 3       # Step size for SNR sweep (in dB)
+  snr_step: 3
 
-  # Number of iterations (samples) per modulation and SNR combination
+  # Number of iterations (signal recordings) per modulation and SNR combination
   num_iterations: 3
 
-  # Number of samples per generated recording
+  # Modulation scheme settings; keys must match the `modulation_types` list above
-  recording_length: 1024
+  # Each entry includes:
-
+  #   - num_bits_per_symbol: bits encoded per symbol (e.g., 1 for BPSK, 4 for 16-QAM)
-  # Settings for each modulation scheme
+  #   - constellation_type: modulation category (e.g., "psk", "qam", "fsk", "ofdm")
-  # Keys must match entries in `modulation_types`
+  # TODO: Combine entries for 'modulation_types' and 'modulation_settings'
-  #   - `num_bits_per_symbol`: how many bits each symbol encodes (e.g., 1 for BPSK, 4 for 16-QAM)
-  #   - `constellation_type`: type of modulation (e.g., "psk", "qam", "fsk", "ofdm")
   modulation_settings:
     bpsk:
       num_bits_per_symbol: 1
@@ -51,20 +45,25 @@ dataset:
       num_bits_per_symbol: 6
       constellation_type: qam
 
+  # Number of slices to cut from each recording
+  num_slices: 8
 
+  # Training and validation split ratios; must sum to 1
+  train_split: 0.8
+  val_split : 0.2
 
 training:
-  # Number of training samples processed together before the model updates its weights
+  # Number of training examples processed together before the model updates its weights
   batch_size: 256
 
   # Number of complete passes through the training dataset during training
   epochs: 5
 
-  # Learning rate: how much weights are updated after every batch
+  # Learning rate: step size for weight updates after each batch
-  # Suggested range for fine-tuning: 1e-6 to 1e-4
+  # Recommended range for fine-tuning: 1e-6 to 1e-4
   learning_rate: 1e-4
 
-  # Whether to use GPU acceleration for training (if available)
+  # Enable GPU acceleration for training if available
   use_gpu: true
 
   # Dropout rate for individual neurons/layers (probability of dropping out a unit)
@@ -73,13 +72,12 @@ training:
   # Drop path rate: probability of dropping entire residual paths (stochastic depth)
   drop_path_rate: 0.2
 
-  # Weight decay (L2 regularization) to help prevent overfitting
+  # Weight decay (L2 regularization) coefficient to help prevent overfitting
   wd: 0.01
 
-
 app:
-  # Optimization style for ORT conversion. Options: 'Fixed', 'None'
+  # Optimization style for ORT conversion; options: 'Fixed', 'None'
-  optimization_style: Fixed
+  optimization_style: "Fixed"
 
-  # Target platform architecture. Common options: 'amd64', 'arm64'
+  # Target platform architecture; common options: 'amd64', 'arm64'
-  target_platform: amd64
+  target_platform: "amd64"