97 lines
3.5 KiB
Python
Executable File
97 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2019 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from itertools import islice
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from fitipy import Fitipy
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from prettyparse import create_parser
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from precise.scripts.train import Trainer
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from precise.util import calc_sample_hash
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usage = '''
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Train a model, sampling data points with the highest loss from a larger dataset
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:-c --cycles int 200
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Number of sampling cycles of size {epoch} to run
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:-n --num-sample-chunk int 50
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Number of new samples to introduce at a time between training cycles
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:-sf --samples-file str -
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Json file to write selected samples to.
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Default: {model_base}.samples.json
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:-is --invert-samples
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Unused parameter
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...
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'''
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class SampledTrainer(Trainer):
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def __init__(self):
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parser = create_parser(usage)
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super().__init__(parser)
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if self.args.invert_samples:
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parser.error('--invert-samples should be left blank')
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self.args.samples_file = (self.args.samples_file or '{model_base}.samples.json').format(
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model_base=self.model_base
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)
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self.samples, self.hash_to_ind = self.load_sample_data(self.args.samples_file, self.train)
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self.metrics_fiti = Fitipy(self.model_base + '.logs', 'sampling-metrics.txt')
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def write_sampling_metrics(self, predicted):
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correct = float(sum((predicted > 0.5) == (self.train[1] > 0.5)) / len(self.train[1]))
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print('Successfully calculated: {0:.3%}'.format(correct))
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lines = self.metrics_fiti.read().lines()
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lines.append('{}\t{}'.format(len(self.samples) / len(self.train[1]), correct))
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self.metrics_fiti.write().lines(lines)
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def choose_new_samples(self, predicted):
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failed_samples = {
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calc_sample_hash(inp, target)
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for i, (inp, pred, target) in enumerate(zip(self.train[0], predicted, self.train[1]))
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if (pred > 0.5) != (target > 0.5)
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}
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remaining_failed_samples = failed_samples - self.samples
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print('Remaining failed samples:', len(remaining_failed_samples))
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return islice(remaining_failed_samples, self.args.num_sample_chunk)
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def run(self):
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print('Writing to:', self.args.samples_file)
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print('Writing metrics to:', self.metrics_fiti.path)
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for _ in range(self.args.cycles):
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print('Calculating on whole dataset...')
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predicted = self.model.predict(self.train[0])
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self.samples.update(self.choose_new_samples(predicted))
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Fitipy(self.args.samples_file).write().set(self.samples)
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print('Added', self.args.num_sample_chunk, 'samples')
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self.write_sampling_metrics(predicted)
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self.model.fit(
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*self.sampled_data, self.args.batch_size, self.epoch + self.args.epochs,
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callbacks=self.callbacks, initial_epoch=self.epoch, validation_data=self.test
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)
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def main():
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SampledTrainer().run()
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if __name__ == '__main__':
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main()
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