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Add a new ImageDataset Class #579
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Should the output not be a table instead of a column? Say we're doing object classification, then the output could be described as a one-hot encoded vector. |
Yes, you are right, the table should have the same amount of columns as the nn module has as output neurons. In this case the table should only contain numerical columns with values between 0 and 1. For easy use, we could also allow a column that will be one hot encoded in the Dataset. |
) Closes #579, #580, #581 ### Summary of Changes feat: added `Convolutional2DLayer`, `ConvolutionalTranspose2DLayer`, `FlattenLayer`, `MaxPooling2DLayer` and `AvgPooling2DLayer` feat: added `InputConversionImage`, `OutputConversionImageToColumn`, `OutputConversionImageToTable` and `OutputConversionImageToImage` feat: added generic `ImageDataset` feat: added class `ImageSize` and methods `ImageList.sizes` and `Image.size` to get the sizes of the respective images feat: added ability to iterate over `SingleSizeImageList` feat: added param to return filenames in `ImageList.from_files` feat: added option `None` for no activation function in `ForwardLayer` feat: added `Image.__array__` to convert a `Image` to a `numpy.ndarray` feat: added equals check to `OneHotEncoder` fix: fixed bug #581 in removing the Softmax function from the last layer in `NeuralNetworkClassifier` refactor: move `image.utils` to `image._utils` refactor: extracted test devices from `test_image` to `helpers.devices` --------- Co-authored-by: megalinter-bot <[email protected]>
## [0.24.0](v0.23.0...v0.24.0) (2024-05-09) ### Features * `Column.plot_histogram()` using `Table.plot_histograms` for consistent results ([#726](#726)) ([576492c](576492c)) * `Regressor.summarize_metrics` and `Classifier.summarize_metrics` ([#729](#729)) ([1cc14b1](1cc14b1)), closes [#713](#713) * `Table.keep_only_rows` ([#721](#721)) ([923a6c2](923a6c2)) * `Table.remove_rows` ([#720](#720)) ([a1cdaef](a1cdaef)), closes [#698](#698) * Add `ImageDataset` and Layer for ConvolutionalNeuralNetworks ([#645](#645)) ([5b6d219](5b6d219)), closes [#579](#579) [#580](#580) [#581](#581) * added load_percentage parameter to ImageList.from_files to load a subset of the given files ([#739](#739)) ([0564b52](0564b52)), closes [#736](#736) * added rnn layer and TimeSeries conversion ([#615](#615)) ([6cad203](6cad203)), closes [#614](#614) [#648](#648) [#656](#656) [#601](#601) * Basic implementation of cell with polars ([#734](#734)) ([004630b](004630b)), closes [#712](#712) * deprecate `Table.add_column` and `Table.add_row` ([#723](#723)) ([5dd9d02](5dd9d02)), closes [#722](#722) * deprecated `Table.from_excel_file` and `Table.to_excel_file` ([#728](#728)) ([c89e0bf](c89e0bf)), closes [#727](#727) * Larger histogram plot if table only has one column ([#716](#716)) ([31ffd12](31ffd12)) * polars implementation of a column ([#738](#738)) ([732aa48](732aa48)), closes [#712](#712) * polars implementation of a row ([#733](#733)) ([ff627f6](ff627f6)), closes [#712](#712) * polars implementation of table ([#744](#744)) ([fc49895](fc49895)), closes [#638](#638) [#641](#641) [#649](#649) [#712](#712) * regularization for decision trees and random forests ([#730](#730)) ([102de2d](102de2d)), closes [#700](#700) * Remove device information in image class ([#735](#735)) ([d783caa](d783caa)), closes [#524](#524) * return fitted transformer and transformed table from `fit_and_transform` ([#724](#724)) ([2960d35](2960d35)), closes [#613](#613) ### Bug Fixes * make `Image.clone` internal ([#725](#725)) ([215a472](215a472)), closes [#626](#626) ### Performance Improvements * improved performance of `TabularDataset.__eq__` by a factor of up to 2 ([#697](#697)) ([cd7f55b](cd7f55b))
🎉 This issue has been resolved in version 0.24.0 🎉 The release is available on:
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Is your feature request related to a problem?
This ImageDataset should handle an input as an ImageList and an output as either an ImageList a column that will be one hot encoded internally or a numerical table with values between 0 and 1. If the output is a table it should have the same amount of columns as the nn module has output neurons and all columns should have values between 0 and 1. If the output is a column the amount of one hot encoded values should not exceed the amount of output neurons of the nn module.
Desired solution
Add a new ImageDataset Class
This class should also deliver batching methods for NN models
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