# Initialize the ``BCELoss`` function
[criterion](https://docs.pytorch.org/docs/stable/generated/torch.nn.BCELoss.html#torch.nn.BCELoss "torch.nn.BCELoss") = [nn.BCELoss](https://docs.pytorch.org/docs/stable/generated/torch.nn.BCELoss.html#torch.nn.BCELoss "torch.nn.BCELoss")()

# Create batch of latent vectors that we will use to visualize
#  the progression of the generator
[fixed_noise](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") = [torch.randn](https://docs.pytorch.org/docs/stable/generated/torch.randn.html#torch.randn "torch.randn")(64, nz, 1, 1, [device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device "torch.device")=[device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device "torch.device"))

# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.

# Setup Adam optimizers for both G and D
[optimizerD](https://docs.pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam "torch.optim.Adam") = [optim.Adam](https://docs.pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam "torch.optim.Adam")([netD.parameters](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.parameters "torch.nn.Module.parameters")(), lr=lr, betas=(beta1, 0.999))
[optimizerG](https://docs.pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam "torch.optim.Adam") = [optim.Adam](https://docs.pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam "torch.optim.Adam")([netG.parameters](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.parameters "torch.nn.Module.parameters")(), lr=lr, betas=(beta1, 0.999))