**This has the solution to my problem, Tl;Dr: It’s hidden in a advanced option.**

I keep on getting this error:

```
You are passing a target array of shape (32, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via: ``` from keras.utils.np_utils import to_categorical y_binary = to_categorical(y_int) ``` Alternatively, you can use the loss function `sparse_categorical_crossentropy` instead, which does expect integer targets.
```

Regretfully I have no idea how to fix this, All the data-types are floats so I set them to numerical. (and they are being ingested correctly) At the moment I just have two dense layers, one with 100 nodes out and linear activation, the other with 1 node out and again linear activation.

The objective is to output a single float based off the input numbers. However I can’t even get it to start, let alone train.