Sequential (original) (raw)

class torch.nn.Sequential(*args: Module)[source]#

class torch.nn.Sequential(arg: OrderedDict[str, Module])

A sequential container.

Modules will be added to it in the order they are passed in the constructor. Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains. It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.

The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that performing a transformation on theSequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).

What’s the difference between a Sequential and atorch.nn.ModuleList? A ModuleList is exactly what it sounds like–a list for storing Module s! On the other hand, the layers in a Sequential are connected in a cascading way.

Example:

Using Sequential to create a small model. When model is run,

input will first be passed to Conv2d(1,20,5). The output of

Conv2d(1,20,5) will be used as the input to the first

ReLU; the output of the first ReLU will become the input

for Conv2d(20,64,5). Finally, the output of

Conv2d(20,64,5) will be used as input to the second ReLU

model = nn.Sequential( nn.Conv2d(1, 20, 5), nn.ReLU(), nn.Conv2d(20, 64, 5), nn.ReLU() )

Using Sequential with OrderedDict. This is functionally the

same as the above code

model = nn.Sequential( OrderedDict( [ ("conv1", nn.Conv2d(1, 20, 5)), ("relu1", nn.ReLU()), ("conv2", nn.Conv2d(20, 64, 5)), ("relu2", nn.ReLU()), ] ) )

append(module)[source]#

Append a given module to the end.

Parameters

module (nn.Module) – module to append

Return type

Self

Example:

import torch.nn as nn n = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3)) n.append(nn.Linear(3, 4)) Sequential( (0): Linear(in_features=1, out_features=2, bias=True) (1): Linear(in_features=2, out_features=3, bias=True) (2): Linear(in_features=3, out_features=4, bias=True) )

extend(sequential)[source]#

Extends the current Sequential container with layers from another Sequential container.

Parameters

sequential (Sequential) – A Sequential container whose layers will be added to the current container.

Return type

Self

Example:

import torch.nn as nn n = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3)) other = nn.Sequential(nn.Linear(3, 4), nn.Linear(4, 5)) n.extend(other) # or n + other Sequential( (0): Linear(in_features=1, out_features=2, bias=True) (1): Linear(in_features=2, out_features=3, bias=True) (2): Linear(in_features=3, out_features=4, bias=True) (3): Linear(in_features=4, out_features=5, bias=True) )

forward(input)[source]#

Runs the forward pass.

insert(index, module)[source]#

Inserts a module into the Sequential container at the specified index.

Parameters

• index (int) – The index to insert the module.
• module (Module) – The module to be inserted.

Return type

Self

Example:

import torch.nn as nn n = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3)) n.insert(0, nn.Linear(3, 4)) Sequential( (0): Linear(in_features=3, out_features=4, bias=True) (1): Linear(in_features=1, out_features=2, bias=True) (2): Linear(in_features=2, out_features=3, bias=True) )

pop(key)[source]#

Pop key from self.

Return type

Module