# Overview¶

I wrote Tinynet for learning purpose, and I found it is useful and interesting to figure out what is a deep learning framework doing under their apis. I hope this project can be useful for other students to understand how deep learning works.

In Tinynet, we focus on three main tasks: construct the neural networks, perform the training, evaluating and visualizing processes and export the trained weight to the persistent storage (i.e. the hard disk). We have made the following modules to achieve these goals:

Core. In this module, we implement a base class for all the parameters that need to be updated during training. A parameter includes two components: the tensor that saves the actual data, and the gradient that saves the derivatives of the loss with respect to the parameter for updating.

Layers. We implement all the needed layers in this module, including the fully connected layer, the convolutional layer, ReLu and Dropout layer, etc. All these layers are Python classes that are extended from a base class, which requires the subclasses to implement a

*forward*and a*backward*function.Losses. We implement the needed cross-entropy loss in this module. The loss function is implemented as a Python function that has two inputs,

*predicted*and*ground truth*. Then the function needs to return two values, the*loss value*, which measures the distance between the ground truth and the predicted output, and the*gradient*, which calculates the derivatives of loss value with respect to the predicted output.Net. Net is a class that stacks several different layers, and provides three functions:

*forward*,*backward*and*update*. The forward function will compute the output of the forward pass from the beginning of the stacked layers, while the backward function will first reverse those layers and then compute the backward pass from the end of the given layers. The update function simply updates all those parameters in a neural network at once.Optimizer. The SGD optimizer is implemented in this module. The optimizer receives a parameter from the

*Core*module, computes the next value by \(new=old-\epsilon\nabla\) where \(\epsilon\) is the preset learning rate, and \(\nabla\) is the computed derivative of the loss with respect to the parameter.Learner. We perform the actual training process inside the

*Learner*module. A learner receives a user-defined neural network architecture, a training dataset, an optimizer and some other hyperparameters such as batch size. Then the learner will read the training dataset batch by batch, and in each batch, the learner will call the forward function of the given neural network architecture on the batch, compute the loss value and then perform the backward pass. After the backward pass in each batch, the learner will update all the parameters in the network.