The CNN + FC snippet is a basic convolutional neural network with an additional tabular data input. This means that the network takes two different features as inputs:
An image input feature that is fed into the convolutional layers
A tabular data set that is fed into the dense layers
This snippet is appropriate for getting started with the platform and starting to explore multi-input models.
The CNN + FC snippet consists of the following types of blocks:
2D Convolution. This block is used to detect spatial features in an image.
2D Max pooling. This layer reduces the size of the data. You can say that 2D max pooling is similar to scaling down the size of an image.
Batch normalization. This normalizes all input features to a similar range of values which will speed up learning.
Concatenate. This block concatenates a list of inputs.
2D Global average pooling. This block is used as an alternative to the Flatten block as it reduces the tensor of the last convolution layer from HxWx128 to a tensor of size 1x1x128.
Dense. This is a densely connected neural network layer.
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