Hi All,
New to TensorFlow and Neural Networks in general, apologies if this is an overly simple question. I did do some digging but didn’t find what I am looking for. I’m wanting to build a model that accepts different types of data as multiple inputs. For example, a US based, 5 digit, postal code and the time of day, expressed as an integer (1 - morning, 2 - afternoon, 3 - evening, etc.).
I’m not sure if it’s the right approach but, I’m one hot encoding the data. Postal codes in this case look like (note, I’m working in JS):
tf.tensor3d([
	[ 
		// 94701
		[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ],
		[ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ],
		[ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 ],
		[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
		[ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 ]
	],
	...
]);

This has worked really well as a single input, struggling to introduce the second one however. Data for the time of day looks like:
tf.tensor3d([
	[ 
		// evening
		[ 0, 0, 1, 0 ]
	],
	...
]);

Everything I’ve tried so far leads me to shape mismatch errors. I’ve tried concatenating the tensors across different axis, tried concatenating layers, and have tried using an array of inputs with tf.model().
Seems like multiple, different data type, inputs should be a common use case. Am I thinking about this wrong? Should I be trying to normalize the data so it takes the same shape (use 10 digits to represent the time of day for example)? Should I be combining the data into a single tensor? Something else I’m missing?
Many thanks!