Module providing Consciousness Exploration Tools for PyTorch.
Note
This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.
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class
consciousnet.models.distributions.ConditionalNormal(input_dim, final_dim, dense_hidden_dims=None, sigma_min=0.0, raw_sigma_bias=0.25, hidden_activation_fn=<class 'torch.nn.modules.activation.ReLU'>, dropout=0)[source]¶ A multivariate Normal distribution conditioned on inputs via a dense network.
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__init__(input_dim, final_dim, dense_hidden_dims=None, sigma_min=0.0, raw_sigma_bias=0.25, hidden_activation_fn=<class 'torch.nn.modules.activation.ReLU'>, dropout=0)[source]¶ Init class.
- Parameters
input_dim : int
the input size.
final_dim : int
the dimension of the random variable.
dense_hidden_dims : list of int, default None
the sizes of the hidden layers of the fully connected network used to condition the distribution on the inputs. If None, then the default is a single-layered dense network.
sigma_min : float, default 0
the minimum standard deviation allowed.
raw_sigma_bias : float, default 0.25
a scalar that is added to the raw standard deviation output from the fully connected network. Set to 0.25 by default to prevent standard deviations close to 0.
hidden_activation_fn : @callable, default relu
the activation function to use on the hidden layers of the fully connected network.
dropout : float, default 0
define the dropout rate.
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