What are nn.Sequential and other container modules in PyTorch?

PyTorch provides several container modules that compose layers without requiring a custom nn.Module subclass. They are convenient for simple feedforward architectures but less flexible than full subclassing.

import torch
import torch.nn as nn

# ── nn.Sequential: layers applied in order
model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Dropout(0.3),
    nn.Linear(256, 128),
    nn.ReLU(),
    nn.Linear(128, 10),
)
out = model(torch.randn(32, 784))  # (32, 10)

# Named layers in Sequential (for easier access)
model_named = nn.Sequential(
    ("fc1",  nn.Linear(784, 256)),
    ("relu", nn.ReLU()),
    ("fc2",  nn.Linear(256, 10)),
)
print(model_named.fc1.weight.shape)   # torch.Size([256, 784])

# ── nn.ModuleList: list of modules (for dynamic use)
class ResNet(nn.Module):
    def __init__(self, n_blocks: int):
        super().__init__()
        # ModuleList properly registers all contained modules
        self.blocks = nn.ModuleList([
            nn.Linear(64, 64) for _ in range(n_blocks)
        ])
    def forward(self, x):
        for block in self.blocks:
            x = torch.relu(block(x)) + x  # residual
        return x

# ── nn.ModuleDict: dict of modules (for conditional routing)
class MultiHead(nn.Module):
    def __init__(self):
        super().__init__()
        self.heads = nn.ModuleDict({
            "sentiment": nn.Linear(128, 2),
            "topic":     nn.Linear(128, 10),
        })
    def forward(self, x, task: str):
        return self.heads[task](x)

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