feed-forward neural networks adam_loss train_network(model,adam_optimizer ,loss_function,num_epochs,batch_ size,x_train,y_trainbefore we get into the specifics of listing - let' look at the individual components we defined leveraging pytorch' readily provided building blocks we need to define loss function that will be used to measure the difference between our predictions and actual labels pytorch provides comprehensive list of loss functions for different outcomes these loss functions are available under torch nn examples include mseloss (mean squared error loss)crossentropyloss (for multi-class classification)and bceloss (binary cross-entropy loss)which is used for binary classification for our use casewe will leverage binary crossentropy loss this is defined as loss_function torch nn bceloss(nextwe define an optimizer for our network earlier in the we explored the sgdadamand rmsprop optimizers pytorch provides comprehensive list of optimizers that can be used for building various kinds of neural networks all optimizers are organized under torch optim ( torch optim sgdfor sgd optimizerfor our use casewe are using the adam optimizer (the most recommended optimizer for the majority of use caseswhile defining the optimizerwe also need to define the parameters for which the gradient needs to be computed during backpropagation for the neural networkthis list would be all the weights in the feed-forward network we can easily denote the entire list of model weights to the optimizer by using model parameters(within the definition of the optimizer we can then additionally define hyperparameters for the selected optimizer by defaultpytorch provides fairly good values for all necessary hyperparameters howeverwe can further override them to tailor optimizers for our use case adam_optimizer tch optim adam(model parameters(),lr