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amend
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@vmoens
vmoens committed Mar 5, 2024
1 parent 17b96c8 commit 6a1dd2e
Showing 1 changed file with 13 additions and 13 deletions.
26 changes: 13 additions & 13 deletions torchrl/modules/models/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,13 +60,13 @@ class MLP(nn.Sequential):
class or constructor to be used.
Defaults to :class:`~torch.nn.Tanh`.
activation_kwargs (dict or list of dicts, optional): kwargs to be used
with the activation class. Aslo accepts a list of kwargs, one for
each layer.
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, one for
each layer.
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.
Expand All @@ -77,7 +77,7 @@ class or constructor to be used.
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, one for each layer.
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.
Expand Down Expand Up @@ -330,13 +330,13 @@ class ConvNet(nn.Sequential):
class or constructor to be used.
Defaults to :class:`~torch.nn.Tanh`.
activation_kwargs (dict or list of dicts, optional): kwargs to be used
with the activation class. A list of kwargs can also be passed,
with one element per layer.
with the activation class. A list of kwargs of length ``depth``
can also be passed, with one element per 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. A list of kwargs can also be passed,
with one element per layer.
the normalization layers. A list of kwargs of length ``depth`` can
also be passed, with one element per layer.
bias_last_layer (bool): if ``True``, the last Linear layer will have a
bias parameter. Defaults to ``True``.
aggregator_class (Type[nn.Module] or callable): aggregator class or
Expand Down Expand Up @@ -578,12 +578,12 @@ class Conv3dNet(nn.Sequential):
activation_class (Type[nn.Module] or callable): activation class or
constructor to be used. Defaults to :class:`~torch.nn.Tanh`.
activation_kwargs (dict or list of dicts, optional): kwargs to be used
with the activation class. A list of kwargs with one element per
layer can also be provided.
with the activation class. A list of kwargs of length ``depth``
with one element per layer can also be provided.
norm_class (Type or callable, optional): normalization class, if any.
norm_kwargs (dict or list of dicts, optional): kwargs to be used with
the normalization layers. A list of kwargs with one element per
layer can also be provided.
the normalization layers. A list of kwargs of length ``depth``
with one element per layer can also be provided.
bias_last_layer (bool): if ``True``, the last Linear layer will have a
bias parameter. Defaults to ``True``.
aggregator_class (Type[nn.Module] or callable): aggregator class or
Expand Down

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