MLP¶

A multi-layer perceptron.

If MLP receives more than one input, it concatenates them all along the last dimension before passing the resulting tensor through the network. This is aimed at allowing for a seamless interface with calls of the type of

>>> model(state, action)  # compute state-action value

In the future, this feature may be moved to the ProbabilisticTDModule, though it would require it to handle different cases (vectors, images, …)

Parameters:

in_features (int, optional) – number of input features;
out_features (int, torch.Size or equivalent) – number of output features. If iterable of integers, the output is reshaped to the desired shape.
depth (int, optional) – depth of the network. A depth of 0 will produce a single linear layer network with the desired input and output size. A length of 1 will create 2 linear layers etc. If no depth is indicated, the depth information should be contained in the num_cells argument (see below). If num_cells is an iterable and depth is indicated, both should match: len(num_cells) must be equal to depth.
num_cells (int or sequence of int, optional) – number of cells of every layer in between the input and output. If an integer is provided, every layer will have the same number of cells. If an iterable is provided, the linear layers out_features will match the content of num_cells. Defaults to 32;
activation_class (Type[nn.Module] or callable, optional) – activation class or constructor to be used. Defaults to Tanh.
activation_kwargs (dict or list of dicts, optional) – kwargs to be used with the activation class. Aslo accepts a list of kwargs of length depth + int(activate_last_layer).
norm_class (Type or callable, optional) – normalization class or constructor, if any.
norm_kwargs (dict or list of dicts, optional) – kwargs to be used with the normalization layers. Aslo accepts a list of kwargs of length depth + int(activate_last_layer).
dropout (float, optional) – dropout probability. Defaults to None (no dropout);
bias_last_layer (bool) – if True, the last Linear layer will have a bias parameter. default: True;
single_bias_last_layer (bool) – if True, the last dimension of the bias of the last layer will be a singleton dimension. default: True;
layer_class (Type[nn.Module] or callable, optional) – class to be used for the linear layers;
layer_kwargs (dict or list of dicts, optional) – kwargs for the linear layers. Aslo accepts a list of kwargs of length depth + 1.
activate_last_layer (bool) – whether the MLP output should be activated. This is useful when the MLP output is used as the input for another module. default: False.
device (torch.device, optional) – device to create the module on.

Examples

>>> # All of the following examples provide valid, working MLPs
>>> mlp = MLP(in_features=3, out_features=6, depth=0) # MLP consisting of a single 3 x 6 linear layer
>>> print(mlp)
MLP(
  (0): Linear(in_features=3, out_features=6, bias=True)
)
>>> mlp = MLP(in_features=3, out_features=6, depth=4, num_cells=32)
>>> print(mlp)
MLP(
  (0): Linear(in_features=3, out_features=32, bias=True)
  (1): Tanh()
  (2): Linear(in_features=32, out_features=32, bias=True)
  (3): Tanh()
  (4): Linear(in_features=32, out_features=32, bias=True)
  (5): Tanh()
  (6): Linear(in_features=32, out_features=32, bias=True)
  (7): Tanh()
  (8): Linear(in_features=32, out_features=6, bias=True)
)
>>> mlp = MLP(out_features=6, depth=4, num_cells=32)  # LazyLinear for the first layer
>>> print(mlp)
MLP(
  (0): LazyLinear(in_features=0, out_features=32, bias=True)
  (1): Tanh()
  (2): Linear(in_features=32, out_features=32, bias=True)
  (3): Tanh()
  (4): Linear(in_features=32, out_features=32, bias=True)
  (5): Tanh()
  (6): Linear(in_features=32, out_features=32, bias=True)
  (7): Tanh()
  (8): Linear(in_features=32, out_features=6, bias=True)
)
>>> mlp = MLP(out_features=6, num_cells=[32, 33, 34, 35])  # defines the depth by the num_cells arg
>>> print(mlp)
MLP(
  (0): LazyLinear(in_features=0, out_features=32, bias=True)
  (1): Tanh()
  (2): Linear(in_features=32, out_features=33, bias=True)
  (3): Tanh()
  (4): Linear(in_features=33, out_features=34, bias=True)
  (5): Tanh()
  (6): Linear(in_features=34, out_features=35, bias=True)
  (7): Tanh()
  (8): Linear(in_features=35, out_features=6, bias=True)
)
>>> mlp = MLP(out_features=(6, 7), num_cells=[32, 33, 34, 35])  # returns a view of the output tensor with shape [*, 6, 7]
>>> print(mlp)
MLP(
  (0): LazyLinear(in_features=0, out_features=32, bias=True)
  (1): Tanh()
  (2): Linear(in_features=32, out_features=33, bias=True)
  (3): Tanh()
  (4): Linear(in_features=33, out_features=34, bias=True)
  (5): Tanh()
  (6): Linear(in_features=34, out_features=35, bias=True)
  (7): Tanh()
  (8): Linear(in_features=35, out_features=42, bias=True)
)
>>> from torchrl.modules import NoisyLinear
>>> mlp = MLP(out_features=(6, 7), num_cells=[32, 33, 34, 35], layer_class=NoisyLinear)  # uses NoisyLinear layers
>>> print(mlp)
MLP(
  (0): NoisyLazyLinear(in_features=0, out_features=32, bias=False)
  (1): Tanh()
  (2): NoisyLinear(in_features=32, out_features=33, bias=True)
  (3): Tanh()
  (4): NoisyLinear(in_features=33, out_features=34, bias=True)
  (5): Tanh()
  (6): NoisyLinear(in_features=34, out_features=35, bias=True)
  (7): Tanh()
  (8): NoisyLinear(in_features=35, out_features=42, bias=True)
)

forward(*inputs: Tuple[Tensor]) → Tensor[source]¶

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

MLP¶

Docs

Tutorials

Resources