To train a Network in Network (NiN) model, practitioners typically apply the same optimization strategies and datasets used for architectures like AlexNet and VGG. For instance, when training on the Fashion-MNIST dataset, the input images are resized to the required resolution (e.g., $224 \times 224$) and processed in batches (e.g., $128$). The model parameters are optimized using a specific learning rate (e.g., $0.05$) over multiple epochs (e.g., $10$). Modern deep learning frameworks provide high-level APIs to facilitate this training loop efficiently.

# PyTorch Implementation
model = NiN(lr=0.05)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(224, 224))
model.apply_init([next(iter(data.get_dataloader(True)))[0]], d2l.init_cnn)
trainer.fit(model, data)

# MXNet Implementation
model = NiN(lr=0.05)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(224, 224))
trainer.fit(model, data)

# JAX Implementation
model = NiN(lr=0.05)
trainer = d2l.Trainer(max_epochs=10, num_gpus=1)
data = d2l.FashionMNIST(batch_size=128, resize=(224, 224))
trainer.fit(model, data)

# TensorFlow Implementation
trainer = d2l.Trainer(max_epochs=10)
data = d2l.FashionMNIST(batch_size=128, resize=(224, 224))
with d2l.try_gpu():
    model = NiN(lr=0.05)
    trainer.fit(model, data)