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

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

Changed loss value printed on the screen for regression. From RMSE to MSE. Adjusted documentation. Also changed unit test for callbacks to the new Linear-Embedding implementation, which implied incresing delta for early stopping

Changed README, wide_deep.py, docs, and examples according to the new metrics. Only thing left is adjust the notebooks

fixed a bug related to the focal loss. Remove activations before the losses in the fit method in WideDeep. Sigmoid and logSoftmax happen within the loss. Added activations in the train and eval step and in the predict method

Adapted the examples to the small code changes. Replace add_ with add in WideDeep to avoid annoying warnings. Replace output_dim with pred_dim in Wide for consistentcy

finished docs apart from examples. Changed name of DeepPreprocessor to DensePreprocessor for consistency. Added raise error if model components do not have an 'output_dim' attribute

Deal with when either deeptext or deepimage does not exist.

fixed bug related to the activation function in the case of multiclass classification. F.cross_entropy already applies logSoftmax

refined the documentation for the WarmUp class. Fix an issue regarding to the model type. Added a test for the warm up functionalities

refined documentation in the WideDeep class. Change the reduction method for the FocalLoss from sum to mean

improved deep_dense syntax. Replaced Adam with AdamW as default. Added n_cpus to n_workers in Dataloader

Added support for sparse matrices. Added the possibility of using a head for the deep branch. Started with the examples

changed order of input params in a number of methods to sensical order. Also fixed a number of bugs in the predict methods