RBM¶

class qucumber.rbm.BinaryRBM(num_visible, num_hidden, zero_weights=False, gpu=True)[source]¶

Bases: torch.nn.Module

effective_energy(v)[source]¶

The effective energies of the given visible states.

$\mathcal{E}(\bm{v}) &= -\sum_{j}b_j v_j - \sum_{i}\log \left\lbrack 1 + \exp\left(c_{i} + \sum_{j} W_{ij} v_j\right) \right\rbrack$

Parameters: v (torch.Tensor) – The visible states.
Returns: The effective energies of the given visible states.
Return type: torch.Tensor

effective_energy_gradient(v, reduce=True)[source]¶

The gradients of the effective energies for the given visible states.

Parameters

v (torch.Tensor) – The visible states.
reduce – If True, will sum over the gradients resulting from each visible state. Otherwise will return a batch of gradient vectors.

Returns

Will return a vector (or matrix if reduce=False and multiple visible states were given as a matrix) containing the gradients for all parameters (computed on the given visible states v).

Return type

torch.Tensor

gibbs_steps(k, initial_state, overwrite=False)[source]¶

Performs k steps of Block Gibbs sampling. One step consists of sampling the hidden state $\bm{h}$ from the conditional distribution $p(\bm{h}\:|\:\bm{v})$ , and sampling the visible state $\bm{v}$ from the conditional distribution $p(\bm{v}\:|\:\bm{h})$ .

Parameters

k (int) – Number of Block Gibbs steps.
initial_state (torch.Tensor) – The initial state of the Markov Chains.
overwrite (bool) – Whether to overwrite the initial_state tensor, if it is provided.

initialize_parameters(zero_weights=False)[source]¶: Randomize the parameters of the RBM

partition(space)[source]¶

Compute the partition function of the RBM.

Parameters: space (torch.Tensor) – A rank 2 tensor of the visible space.
Returns: The value of the partition function evaluated at the current state of the RBM.
Return type: torch.Tensor

prob_h_given_v(v, out=None)[source]¶

Given a visible unit configuration, compute the probability vector of the hidden units being on.

Parameters

h (torch.Tensor) – The hidden unit.
out (torch.Tensor) – The output tensor to write to.

Returns

The probability of hidden units being active given the visible state.

Return type

torch.Tensor

prob_v_given_h(h, out=None)[source]¶

Given a hidden unit configuration, compute the probability vector of the visible units being on.

Parameters

h (torch.Tensor) – The hidden unit
out (torch.Tensor) – The output tensor to write to.

Returns

The probability of visible units being active given the hidden state.

Return type

torch.Tensor

sample_h_given_v(v, out=None)[source]¶

Sample/generate a hidden state given a visible state.

Parameters

h (torch.Tensor) – The visible state.
out (torch.Tensor) – The output tensor to write to.

Returns

The sampled hidden state.

Return type

torch.Tensor

sample_v_given_h(h, out=None)[source]¶

Sample/generate a visible state given a hidden state.

Parameters

h (torch.Tensor) – The hidden state.
out (torch.Tensor) – The output tensor to write to.

Returns

The sampled visible state.

Return type

torch.Tensor