#18 Implementation of the Linear model that is the wide component via an Embedding layer. This helps optimize speed and memory usage. Adapted all the submodules accordingly

Precision recall

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

added link to the docs in the pypi_README file. Note this is a temporal solution. As soon as I manage to include the pngs in the pypi publication this file will dissapear

Build docs

Added a README file so that it looks decent in pypi (I know is not elegant, but I have not managed to properly display png

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

fixing bug related to the Focal Loss. Replaced binary_cross_entropy_with logits with binary_cross_entropy for the binary classification case

fixing bug related to the Focal Loss. Replaced binary_cross_entropy_with logits with binary_cross_entropy

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

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