DeepLearning: does it make sense to have more nodes in the initial layer than inputs--for tabular data
This might seem a little bit confusing, especially for newcomers. But the problem here is basically agreeing on some terminology. 

Typical Neural Networks by definition have 3 types of layers, namely Input, Hidden and Output. Because the Input layer has no specific function other that passing the input values to the next layer, many times is not mentioned. This layer does not have any weights, biases or activations and in the vast majority of frameworks (such as Keras) you don't have to write it in you code. You only need to specify the size of that input layer with the argument input_shape=(x_size,) in the first hidden layer.

What you call in your example initial layer of 100 nodes is actually that first hidden layer. Which can be on any size. And your intuition is completely right: the number of nodes in the input layer of course has to match the size of the inputs. 

Hope this clarifies.