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

The logits (z), as shown in Figure 4.4, are given by the following expression:
Model Configuration
As usual, we only need to define a model, an appropriate loss function, and an
optimizer. Since we have five-by-five single-channel images as inputs now, we
need to flatten them first so they can be proper inputs to our linear layer (without
bias). We will keep using the SGD optimizer with a learning rate of 0.1 for now.
This is what the model configuration looks like for our classification problem:
Model Configuration
1 # Sets learning rate - this is "eta" ~ the "n"-like Greek letter
2 lr = 0.1
3
4 torch.manual_seed(17)
5 # Now we can create a model
6 model_logistic = nn.Sequential()
7 model_logistic.add_module('flatten', nn.Flatten())
8 model_logistic.add_module('output', nn.Linear(25, 1, bias=False))
9 model_logistic.add_module('sigmoid', nn.Sigmoid())
10
11 # Defines an SGD optimizer to update the parameters
12 optimizer_logistic = optim.SGD(
13 model_logistic.parameters(), lr=lr
14 )
15 # Defines a binary cross-entropy loss function
16 binary_loss_fn = nn.BCELoss()
300 | Chapter 4: Classifying Images