PyTorch - Neural Networks to Functional Blocks


Training a deep learning algorithm involves the following steps −

  • Building a data pipeline
  • Building a network architecture
  • Evaluating the architecture using a loss function
  • Optimizing the network architecture weights using an optimization algorithm

Training a specific deep learning algorithm is the exact requirement of converting a neural network to functional blocks as shown below −

Functional Blocks

With respect to the above diagram, any deep learning algorithm involves getting the input data, building the respective architecture which includes a bunch of layers embedded in them.

If you observe the above diagram, the accuracy is evaluated using a loss function with respect to optimization of the weights of neural network.