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Restricted Boltzmann Machine node. An RBM is an undirected probabilistic network with binary variables. The graph is bipartite into observed ('visible') and hidden ('latent') variables. By default, the 'execute' function returns the *probability* of one of the hiden variables being equal to 1 given the input. Use the 'sample_v' function to sample from the observed variables given a setting of the hidden variables, and 'sample_h' to do the opposite. The 'energy' function can be used to compute the energy of a given setting of all variables. The network is trained by Contrastive Divergence, as described in Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1711-1800 Internal variables of interest: self.w -- generative weights between hidden and observed variables self.bv -- bias vector of the observed variables self.bh -- bias vector of the hidden variables For more information on RBMs, see Geoffrey E. Hinton (2007) Boltzmann machine. Scholarpedia, 2(5):1668
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__metaclass__ This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc... |
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_train_seq List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ... |
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dtype dtype |
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input_dim Input dimensions |
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output_dim Output dimensions |
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supported_dtypes Supported dtypes |
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Arguments: hidden_dim -- number of hidden variables visible_dim -- number of observed variables
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If 'return_probs' is True, returns the probability of the hidden variables h[n,i] being 1 given the observations v[n,:]. If 'return_probs' is False, return a sample from that probability.
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Return the list of dtypes supported by this node.
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This method contains all pre-inversion checks. It can be used when a subclass defines multiple inversion methods.
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Update the internal structures according to the input data 'v'. The training is performed using Contrastive Divergence (CD). v -- a binary matrix having different variables on different columns and observations on the rows n_updates -- number of CD iterations. Default value: 1 epsilon -- learning rate. Default value: 0.1 decay -- weight decay term. Default value: 0. momentum -- momentum term. Default value: 0.
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Compute the energy of the RBM given observed variables state 'v' and hidden variables state 'h'. |
If 'return_probs' is True, returns the probability of the hidden variables h[n,i] being 1 given the observations v[n,:]. If 'return_probs' is False, return a sample from that probability.
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Return True if the node can be inverted, False otherwise.
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Sample the hidden variables given observations v. Returns a tuple (prob_h, h), where prob_h[n,i] is the probability that variable 'i' is one given the observations v[n,:], and h[n,i] is a sample from the posterior probability. |
Sample the observed variables given hidden variable state h. Returns a tuple (prob_v, v), where prob_v[n,i] is the probability that variable 'i' is one given the hidden variables h[n,:], and v[n,i] is a sample from that conditional probability. |
Update the internal structures according to the input data 'v'. The training is performed using Contrastive Divergence (CD). v -- a binary matrix having different variables on different columns and observations on the rows n_updates -- number of CD iterations. Default value: 1 epsilon -- learning rate. Default value: 0.1 decay -- weight decay term. Default value: 0. momentum -- momentum term. Default value: 0.
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