accelbrainbase.iteratabledata._mxnet package¶
Submodules¶
accelbrainbase.iteratabledata._mxnet.drcn_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.drcn_iterator.
DRCNIterator
(image_extractor, dir_list, target_domain_dir_list, test_dir_list=None, epochs=300, batch_size=20, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.drcn_iterator.DRCNIterator
Iterator that draws from image files and generates mxnet.ndarray.
References
- Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., & Li, W. (2016, October). Deep reconstruction-classification networks for unsupervised domain adaptation. In European Conference on Computer Vision (pp. 597-613). Springer, Cham.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test. - mxnet.ndarray of obsrved data points in target domain.
accelbrainbase.iteratabledata._mxnet.gauss_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.gauss_iterator.
GaussIterator
(loc=0.0, std=1.0, dim=100, epochs=300, batch_size=20, norm_mode='z_score', noiseable_data=None, scale=1.0, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from CSV files and generates mxnet.ndarray.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.labeled_csv_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.labeled_csv_iterator.
LabeledCSVIterator
(labeled_csv_extractor, train_csv_path, test_csv_path, epochs=300, batch_size=20, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from CSV files and generates mxnet.ndarray of labeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.labeled_image_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.labeled_image_iterator.
LabeledImageIterator
(image_extractor, dir_list, test_dir_list=None, epochs=300, batch_size=20, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from image files and generates mxnet.ndarray of labeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.labeled_video_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.labeled_video_iterator.
LabeledVideoIterator
(image_extractor, dir_list, test_dir_list=None, epochs=300, batch_size=20, seq_len=10, at_intervals=1, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from image files and generates mxnet.ndarray of labeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - int of key of directory. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.unlabeled_csv_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.unlabeled_csv_iterator.
UnlabeledCSVIterator
(unlabeled_csv_extractor, train_csv_path, test_csv_path, epochs=300, batch_size=20, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from CSV files and generates mxnet.ndarray of unlabeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.unlabeled_image_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.unlabeled_image_iterator.
UnlabeledImageIterator
(image_extractor, dir_list, test_dir_list=None, epochs=300, batch_size=20, norm_mode='z_score', scale=1.0, noiseable_data=None)¶ Bases:
accelbrainbase.iteratabledata.unlabeled_image_iterator.UnlabeledImageIterator
Iterator that draws from image files and generates mxnet.ndarray of unlabeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.unlabeled_sequential_csv_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.unlabeled_sequential_csv_iterator.
UnlabeledSequentialCSVIterator
(unlabeled_csv_extractor, train_csv_path, test_csv_path, epochs=300, batch_size=20, seq_len=10, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from CSV files and generates mxnet.ndarray of unlabeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.unlabeled_t_hot_csv_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.unlabeled_t_hot_csv_iterator.
UnlabeledTHotCSVIterator
(unlabeled_csv_extractor, train_csv_path_list, test_csv_path_list, epochs=300, batch_size=20, seq_len=10, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.labeled_image_iterator.LabeledImageIterator
Iterator that draws from CSV files and generates mxnet.ndarray of unlabeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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accelbrainbase.iteratabledata._mxnet.unlabeled_t_hot_txt_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.unlabeled_t_hot_txt_iterator.
UnlabeledTHotTXTIterator
(train_txt_path_list, test_txt_path_list=None, epochs=300, batch_size=20, seq_len=10, norm_mode='z_score', scale=1.0, noiseable_data=None, ctx=gpu(0))¶ Bases:
accelbrainbase.iteratabledata.unlabeled_image_iterator.UnlabeledImageIterator
Iterator that draws from CSV files and generates mxnet.ndarray of unlabeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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get_pre_txt_arr
()¶
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get_token_arr
()¶
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get_token_list
()¶
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pre_txt_arr
¶
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set_pre_txt_arr
(value)¶
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set_token_arr
(value)¶
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set_token_list
(value)¶
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token_arr
¶
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token_list
¶
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accelbrainbase.iteratabledata._mxnet.unlabeled_video_iterator module¶
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class
accelbrainbase.iteratabledata._mxnet.unlabeled_video_iterator.
UnlabeledVideoIterator
(image_extractor, dir_list, test_dir_list=None, epochs=300, batch_size=20, seq_len=10, at_intervals=1, norm_mode='z_score', scale=1.0, noiseable_data=None)¶ Bases:
accelbrainbase.iteratabledata.unlabeled_image_iterator.UnlabeledImageIterator
Iterator that draws from image files and generates mxnet.ndarray of unlabeled samples.
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generate_inferenced_samples
()¶ Draw and generate data. The targets will be drawn from all image file sorted in ascending order by file name.
Returns: Tuple data. The shape is … - None. - None. - mxnet.ndarray of observed data points in test. - file path.
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generate_learned_samples
()¶ Draw and generate data.
Returns: Tuple data. The shape is … - mxnet.ndarray of observed data points in training. - mxnet.ndarray of supervised data in training. - mxnet.ndarray of observed data points in test. - mxnet.ndarray of supervised data in test.
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