accelbrainbase.iteratabledata._mxnet package

Submodules

accelbrainbase.iteratabledata._mxnet.drcn_iterator module

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.
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.
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

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.labeled_csv_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.labeled_image_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.labeled_video_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.unlabeled_csv_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.unlabeled_image_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.unlabeled_sequential_csv_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.unlabeled_t_hot_csv_iterator module

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.

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.
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.

accelbrainbase.iteratabledata._mxnet.unlabeled_t_hot_txt_iterator module

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.

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.
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.
get_pre_txt_arr()
get_token_arr()
get_token_list()
pre_txt_arr
set_pre_txt_arr(value)
set_token_arr(value)
set_token_list(value)
token_arr
token_list

accelbrainbase.iteratabledata._mxnet.unlabeled_video_iterator module

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.

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.
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.

Module contents