Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Notice that the model is set to training mode after loading the checkpoint.
What about making predictions? To make it easier for the user to make predictions
for any new data points, we will be handling all the Numpy to PyTorch back and
forth conversion inside the function.
Making Predictions
def predict(self, x):
# Set it to evaluation mode for predictions
self.model.eval()
# Take a Numpy input and make it a float tensor
x_tensor = torch.as_tensor(x).float()
# Send input to device and use model for prediction
y_hat_tensor = self.model(x_tensor.to(self.device))
# Set it back to train mode
self.model.train()
# Detach it, bring it to CPU and back to Numpy
return y_hat_tensor.detach().cpu().numpy()
setattr(StepByStep, 'predict', predict)
First, we set the model to evaluation mode, as it is required in order to make
predictions. Then, we convert the x argument (assumed to be a Numpy array) to a
float PyTorch tensor, send it to the configured device, and use the model to make a
prediction.
Next, we set the model back to training mode. The last step includes detaching the
tensor containing the predictions and making it a Numpy array to be returned to
the user.
We have already covered most of what was developed in the previous chapters,
except for a couple of visualization functions. Let’s tackle them now.
Visualization Methods
Since we have kept track of both training and validation losses as attributes, let’s
build a simple plot for them:
192 | Chapter 2.1: Going Classy