CNN snippet
The CNN snippet is a basic convolutional neural network appropriate for getting started with the platform.
This network is looking for low-level features such as edges and curves and then building up to more abstract concepts through a series of convolutional layers.
CNN snippet architecture
The CNN snippet consists of the following types of blocks:
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2D Convolution. This block is used to detect spatial features in an image.
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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.
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Batch normalization. This normalizes all input features to a similar range of values which will speed up learning.
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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.
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Dense. This is a densely connected neural network layer.