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.

class consciousnet.models.distributions.ConditionalBernoulli(input_dim, final_dim, dense_hidden_dims=None, bias_init=0.0, hidden_activation_fn=<class 'torch.nn.modules.activation.ReLU'>, dropout=0)[source]¶

A Bernoulli distribution conditioned on inputs via a dense network.

__init__(input_dim, final_dim, dense_hidden_dims=None, bias_init=0.0, 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.

bias_init : float, default 0

a scalar or tensor that is added to the output of the fully connected network and parameterizes the distribution mean.

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.

forward(tensor_list)[source]¶

Creates a Bernoulli distribution conditioned on the inputs.

Parameters: tensor_list : list of torch.Tensor

a list of tensors that will be first concatenatedd on the last dimension.