How does Keras layout works in Tensorflow-CodePudding

I'm testing Tensorflow but I can't figure out how the models are structered. For example, in the official documentation there are the following indications :

A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.

...

A Sequential model is not appropriate when:

 - Your model has multiple inputs or multiple outputs
 - Any of your layers has multiple inputs or multiple outputs
 - You need to do layer sharing
 - You want non-linear topology (e.g. a residual connection, a multi-branch model)

Does this mean that the model only accepts 1 value type of data as Input/Output or just only 1 value as Input/Output?

What I want to do, is use two hexadecimals values predict a third hexadecimal value, for this i have the dataset structured in bits e.g:

hex0[0], hex0[1],.... hex0[n], hex1[0], hex1[1], ... hex1[n], result[0], result[1]... result[n]
0       ,1      ,..., 1      , 1      , 9      , ...,1      , 1        , 1       ,... , 0

The keras.Sequential model works for this type of problem?

CodePudding user response：

It means that it accepts only one type of data (all values numeric or all categorical) because in Sequential model all input values go thru all layers sequentialy.

I prefere documentation in keras.io https://keras.io/api/models/sequential/#sequential-class

In your case all input data are the same type so You can use sequential model.

input is: [hex0[0], hex1[0]

output result[0]

The shape of input and output array is very important.