pydbm.dbm.restrictedboltzmannmachines package¶
Submodules¶
pydbm.dbm.restrictedboltzmannmachines.rt_rbm module¶
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class
pydbm.dbm.restrictedboltzmannmachines.rt_rbm.
RTRBM
¶ Bases:
pydbm.dbm.restricted_boltzmann_machines.RestrictedBoltzmannMachine
Reccurent temploral restricted boltzmann machine.
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get_feature_points
¶ Extract feature points from hidden layer.
Returns: np.ndarray of feature points.
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get_reconstruct_error_arr
¶ Extract reconstructed errors.
- Retruns:
- np.ndarray of reconstructed errors.
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get_reconstructed_arr
¶ Extract reconstructed points.
Returns: np.ndarray of reconstructed points.
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inference
¶ Inferencing.
Parameters: - observed_data_arr – The np.ndarray of observed data points, which is a rank-3 array-like or sparse matrix of shape: (The number of samples, The length of cycle, The number of features)
- r_batch_size –
Batch size. If this value is 0, the inferencing is a recursive learning. If this value is more than 0, the inferencing is a mini-batch recursive learning. If this value is ‘-1’, the inferencing is not a recursive learning.
If you do not want to execute the mini-batch training, the value of batch_size must be -1. And r_batch_size is also parameter to control the mini-batch training but is refered only in inference and reconstruction. If this value is more than 0, the inferencing is a kind of reccursive learning with the mini-batch training.
- batch_size – Batch size in learning.
Returns: The np.ndarray of feature points.
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learn
¶ Learning.
Parameters: - observed_data_arr – The np.ndarray of observed data points, which is a rank-3 array-like or sparse matrix of shape: (The number of samples, The length of cycle, The number of features)
- traning_count – Training counts.
- batch_size – Batch size.
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