decoupled the model from the training process via the introduciton of a Trainer class. All seems easier now. Need to adjust all documentation. Also added a few losses and an R2 metric, as well as the possibiliy of customising virually every single component of the model

added the possibility of passing a few more loss functions and custom ones if required. Now I need to think if I want it registered as a child or not. Also, need to test all new implementation

changed the dense layer to be almost identical to that of fastai, which I really like. Changed the code accordingly. Changed the name of DeepDense and DeepDenseResnet to TabMlp and TabResnet. Change the tests acccordingly

Adjusted documentation and examples. Refactored the LRHistory callback and fix bug in the History callback. Added multiple tests and increased code coverage to 94 per cent

Mainly re-written all tests. Also, refined parameter naming and save 0 for unseen categories (as padding) for both wide and deeptabular component

first commit towards adding the TabTransformer. This will be a mix between a number of implementation (that will of course be credited in the code)

Deeppreproc inverse transform

added inverse_transform for DeepPreprocessor. Also added NameError and AttributeError to avoid bare excepts

Fix image format 

Added comment for Mac users regarding the dataloaders not running in parallel for the latest torch version or python versions >= 3.8. Also, upgraded sub-version of the package to 0.4.7

adjusting two of the example notebooks to include comments on the possibility of using individual components in isolation

updated docs so they are consistent with new functionalities. Updated logo. Updated README and fix a typo in setup.py

all started by trying to add a line so that the builder accepted 2D images (or in general images of dim different than 3. But it ended up by adding functionalities so that each individual component (wide, deepdense, deeptext and deepimage) can be used individually

Added a condition to account for 2D images (images with no color channels). This way we replicate exactly what ToTensor does within the WideDeepDataset class

Dense resnet

pypi README consistent with README. Note that this pypi README solution is temporal given how painful is to format the current README to look nice in pypi

changed pytest syntax (split in two. doctest-modules and the rest) and remove init files in doc dir (nonsense)

Modified documentation and added doc test. Also changed some of the code according to the increase of test coverage

Modified documentation and added doc test. Also changed some of the code in the main WideDeep class to increase test coverage