Add documentation and README, use Upsampling2D rather than image Resizing layer

README.md

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+# Fast Depth TF
+
+Tensorflow 2.0 Implementation of Fast Depth.
+
+Original Implementation in PyTorch is here: https://github.com/dwofk/fast-depth
+
+This code has been tested with Tensorflow 2.4.1, however any version of Tensorflow >2 should work
+
+The model has also been successfully optimised using the OpenVINO model optimiser
+
+## Basic Usage
+
+To train and evaluate the model on the nyu_v2 dataset, simply run:
+
+`python main.py`
+
+WARNING: This will download nyu_v2 which is ~100gb when including archived and extracted files, plus another 70gb for
+generated examples.
+
+The following sample demonstrates creating a FastDepth model that can later be used for inference, training or
+evaluation.
+
+```python
+import fast_depth_functional as fd
+
+# No Pretrained weights
+model = fd.mobilenet_nnconv5()
+
+# Imagenet weights
+model = fd.mobilenet_nnconv5(weights='imagenet')
+
+# Load trained model from file
+model = fd.load_model('my_fastdepth_model')
+```
+
+### Train
+
+Training with the NYU dataset is as simple as running the following:
+WARNING: This will download ~30gb and extra ~70gb if you haven't downloaded it already. It also takes a long time to
+prepare the examples (>1 hour)
+
+```python
+import fast_depth_functional as fd
+
+model = fd.mobilenet_nnconv5(weights='imagenet')
+
+# Train then save the model as keras h5 format
+fd.train(model, save_file='fast_depth')
+
+# A custom dataset can be passed in if required
+fd.train(model, dataset=my_dataset)
+```
+
+### Evaluate
+
+Evaluation is similar to training. The nyu dataset validation split will be used by default, and if you trained as shown
+above, the dataset will have already been downloaded.
+
+```python
+import fast_depth_functional as fd
+
+model = fd.load_model('fast_depth')
+fd.compile(model)
+fd.evaluate(model)
+
+# A custom dataset for evaluation is supported
+fd.evaluate(model, dataset=my_evaluation_dataset)
+```
+
+## Troubleshooting
+
+### Windows GPU Fix
+
+If you are using Windows and encounter an error opening cudnn (you should see CUDNN_STATUS_ALLOC_FAILED somewhere before
+the error), first check you have correctly installed CUDA toolkit and cuDNN. If you have, then run the Windows GPU fix
+included in this repo:
+
+```python
+import fast_depth_functional as fd
+
+# Windows GPU Fix
+fd.fix_windows_gpu()
+```
+
+More information about this error can be found here:
+https://forums.developer.nvidia.com/t/could-not-create-cudnn-handle-cudnn-status-alloc-failed/108261