Introduction to Keras
Building ANNs involves creating layers of nodes. Each node can be thought of as a tensor of weights that are learned in the training process. Once the ANN is fitted to the data, a prediction is made by multiplying the input data by the weight matrices layer by layer, applying any other linear transformation when needed, such as activation functions, until the final output layer is reached. The size of each weight tensor is determined by the size of the shape of input nodes and the shape of the output nodes. For example, in a single-layer ANN, the size of our single hidden layer can be thought of as follows:
Figure 2.41: Solving the dimensions of the hidden layer of a single-layer ANN
If the input matrix of features has n rows, or observations, and m columns, or features, and we want our predicted target to have n rows (one for each observation) and 1 column (the predicted value), we can determine the size of our hidden layer by what is needed to make the matrix multiplication...
