- The network has 3 layers of neurons: an input layer of size 28*28 = 784, one hidden layer of size 100, and the output layer of size 10.
- You can easily add more layers resulting in a more complex neural network.
- input Picture [28, 28]; // Note that alternatively we could declare input layer as: // input Picture [28 * 28]; // or just // input Picture ; // Net# compiler will be able to infer the number of dimensions automatically.
- // This defines an output layer of size 10 which is fully-connected to layer ‘H’, // with softmax activation function.
- The language also supports various types of layers which will be described in subsequent posts.
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@RickKing16: “Neural Nets in Azure ML – Introduction to Net# #ai”
Neural networks are one of the most popular machine learning algorithms today. One of the challenges when using neural networks is how to define a network topology given the variety of possible layer types, connections among them, and activation functions. Net# solves this problem by providing a succinct way to define almost any neural network architecture in a descriptive, easy-to-read format. This post provides a short tutorial for building a neural network using the Net# language to classify images of handwritten numeric digits in Microsoft Azure Machine Learning.
Neural Nets in Azure ML – Introduction to Net#