46 lines
1.0 KiB
YAML
46 lines
1.0 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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#where to read the recordings from to produce the data set
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input_dir: data/recordings
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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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#where to output the datasets
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output_dir: data/dataset
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training:
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#number of training samples being processed together before model updates its weights
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batch_size: 256
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#number of passes through the data set during the training process
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epochs: 5
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#how much the weights update during training after every batch
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#suggested range for fine-tuning: (1e-6, 1e-4)
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learning_rate: 1e-4
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use_gpu: true
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inference:
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#num classes to classify on
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num_classes: 4
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#output of the onnx model
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onnx_model_filename: inference_recognition_model
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app:
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build_dir: dist |