Deep-Learning-with-PyTorch

Pretraining setup and initialization
285
Init model
Initialized
with Random
weights
FuLly
Trained
Init data loaders
LOop over epochs
Training LOop
Load batch tuple
ClaSsify Batch
Calculate LoSs
Record metrics
Update weights
Validation LOop
Load
batch tuple
ClaSsify Batch
Calculate LoSs
Record metrics
11.3.1 Initializing the model and optimizer
Log Metrics
console
tensorboard
Figure 11.3 The training and validation script we will implement in this chapter, with
a focus on the preloop variable initialization
For this section, we are treating the details of LunaModel as a black box. In section 11.4,
we will detail the internal workings. You are welcome to explore changes to the implementation
to better meet our goals for the model, although that’s probably best done
after finishing at least chapter 12.
Let’s see what our starting point looks like.
Listing 11.4
training.py:31, class LunaTrainingApp
class LunaTrainingApp:
def __init__(self, sys_argv=None):
# ... line 70
self.use_cuda = torch.cuda.is_available()
self.device = torch.device("cuda" if self.use_cuda else "cpu")
self.model = self.initModel()
self.optimizer = self.initOptimizer()