You model consists of:
- One or more Components that connect the Input and Target to transform your data. For example, in the following model generated for MNIST dataset, a Convolution and two Dense Components make up the model and transform your Input data to predictions that are comparable with your Targets:
The following are the key customizations you can make as you iterate on your model:
For example, follow the steps below to incorporate a second Convolution Component into the MNIST model:
1) Remove the connection between the Convolution Component and the first Dense Component.
2) Move the two Dense Components and the Target to the right.
3) Click the Deep Learning menu, select Convolution, and drag it onto the model.
4) Connect the Output socket of the existing Convolution Component to the Input socket of your new Convolution Component.
5) Connect the Output socket of your new Convolution Component to the Input socket of the first Dense Component.
6) Select your new Convolution Component and configure its Feature maps setting to 16 in the Settings pane.
The updated model should now look as follows:
This example showed a few of the ways that you can modify an existing model.