mirror of https://github.com/BlackLight/micmon.git
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
# This script shows how to train a neural network to detect sounds given a training set of collected frequency
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# spectrum data.
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import os
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from keras import layers
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from micmon.dataset import Dataset
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from micmon.model import Model
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# This is a directory that contains the saved .npz dataset files
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datasets_dir = os.path.expanduser('~/datasets/baby-monitor/datasets')
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# This is the output directory where the model will be saved
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model_dir = os.path.expanduser(os.path.join('~', 'models', 'baby-monitor'))
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# This is the number of training epochs for each dataset sample
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epochs = 2
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# This value establishes the share of the dataset to be used for cross-validation
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validation_split = 0.3
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# Load the datasets from the compressed files
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datasets = Dataset.scan(datasets_dir, validation_split=0.3)
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# Get the number of frequency bins
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freq_bins = len(datasets[0].samples[0])
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# Create a network with 4 layers (one input layer, two intermediate layers and one output layer).
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# The first intermediate layer in this example will have twice the number of units as the number
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# of input units, while the second intermediate layer will have as many units as the number of
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# input units. We also specify the names for the labels and the low and high frequency range
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# used when sampling.
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model = Model(
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[
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layers.Input(shape=(freq_bins,)),
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layers.Dense(int(2.0 * freq_bins), activation='relu'),
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layers.Dense(int(freq_bins), activation='relu'),
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layers.Dense(len(datasets[0].labels), activation='softmax'),
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],
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labels=['negative', 'positive'],
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low_freq=datasets[0].low_freq,
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high_freq=datasets[0].high_freq,
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)
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# Train the model
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for epoch in range(epochs):
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for i, dataset in enumerate(datasets):
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print(f'[epoch {epoch+1}/{epochs}] [audio sample {i+1}/{len(datasets)}]')
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model.fit(dataset)
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evaluation = model.evaluate(dataset)
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print(f'Validation set loss and accuracy: {evaluation}')
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# Save the model
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model.save(model_dir, overwrite=True)
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