After you have loaded your data and PerceptiLabs has generated a model for you, it's time to start working with that model.
Basic Model Architecture
You model consists of:
On or more Input Components: these act as your data source for your inputs.
One or more Target Components: these contain your targets. You need to ensure that your predictions (last layer of your model) is connected to them.
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:
Customizing your Model
The following are the key customizations you can make as you iterate on your model:
Swap Components (e.g., employ Deep Learning Components like VGG16 which have been pre-trained with ImageNet data, replace the Convolution and Dense Components with a UNet Component, etc.).
Adjust Component settings in the Settings Pane. See the Component topics for descriptions of each setting.