Test it on the Peltarion Platform
A platform to build and deploy deep learning projects.
Even if you’re not an AI superstar.
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:
-
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.
-
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.