Implementing Artificial Neural Networks in TensorFlow
The advanced flexibility that TensorFlow offers lends itself well to creating artificial neural networks (ANNs). ANNs are algorithms that are inspired by the connectivity of neurons in the brain and are intended to replicate the process in which humans learn. They consist of layers through which information propagates from the input to the output.
Figure 1.1 shows a visual representation of an ANN. An input layer is on the left-hand side, which, in this example, has two features (X
1 and X
2). The input layer is connected to the first hidden layer, which has three units. All the data from the previous layer gets passed to each unit in the first hidden layer. The data is then passed to the second hidden layer, which also has three units. Again, the information from each unit of the prior layer is passed to each unit of the second hidden layer. Finally, all the information from the second hidden layer is passed to the output layer...