pydbm.approximation package¶
Subpackages¶
Submodules¶
pydbm.approximation.contrastive_divergence module¶

class
pydbm.approximation.contrastive_divergence.
ContrastiveDivergence
¶ Bases:
pydbm.approximation.interface.approximate_interface.ApproximateInterface
Contrastive Divergence.
Conceptually, the positive phase is to the negative phase what waking is to sleeping.
In relation to RBM, Contrastive Divergence(CD) is a method for approximation of the gradients of the loglikelihood(Hinton, G. E. 2002).
The procedure of this method is similar to Markov Chain Monte Carlo method(MCMC). However, unlike MCMC, the visbile variables to be set first in visible layer is not randomly initialized but the observed data points in training dataset are set to the first visbile variables. And, like Gibbs sampler, drawing samples from hidden variables and visible variables is repeated k times. Empirically (and surprisingly), k is considered to be 1.
References
 Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural computation, 14(8), 17711800.

approximate_inference
¶ Inference with function approximation.
Parameters:  graph – Graph of neurons.
 learning_rate – Learning rate.
 observed_data_arr – observed data points.
 training_count – Training counts.
 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 minibatch recursive learning. If this value is ‘1’, the inferencing is not a recursive learning.
Returns: Graph of neurons.

approximate_learn
¶ learning with function approximation.
Parameters:  graph – Graph of neurons.
 learning_rate – Learning rate.
 observed_data_arr – observed data points.
 training_count – Training counts.
 batch_size – Batch size (0: not minibatch)
Returns: Graph of neurons.

get_reconstruct_error_list
¶ getter

reconstruct_error_list
¶ getter

set_readonly
¶ setter
pydbm.approximation.rt_rbm_cd module¶

class
pydbm.approximation.rt_rbm_cd.
RTRBMCD
¶ Bases:
pydbm.approximation.interface.approximate_interface.ApproximateInterface
Recurrent Temporal Restricted Boltzmann Machines based on Contrastive Divergence.
Conceptually, the positive phase is to the negative phase what waking is to sleeping.
The RTRBM (Sutskever, I., et al. 2009) is a probabilistic timeseries model which can be viewed as a temporal stack of RBMs, where each RBM has a contextual hidden state that is received from the previous RBM and is used to modulate its hidden units bias.
Parameters:  graph.weights_arr – $W$ (Connection between v^{(t)} and h^{(t)})
 graph.visible_bias_arr – $b_v$ (Bias in visible layer)
 graph.hidden_bias_arr – $b_h$ (Bias in hidden layer)
 graph.rnn_hidden_weights_arr – $W’$ (Connection between h^{(t1)} and b_h^{(t)})
 graph.rnn_visible_weights_arr – $W’‘$ (Connection between h^{(t1)} and b_v^{(t)})
 graph.hat_hidden_activity_arr – $hat{h}^{(t)}$ (RNN with hidden units)
 graph.pre_hidden_activity_arr – $hat{h}^{(t1)}$
References
 BoulangerLewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling temporal dependencies in highdimensional sequences: Application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392.
 Lyu, Q., Wu, Z., Zhu, J., & Meng, H. (2015, June). Modelling HighDimensional Sequences with LSTMRTRBM: Application to Polyphonic Music Generation. In IJCAI (pp. 41384139).
 Lyu, Q., Wu, Z., & Zhu, J. (2015, October). Polyphonic music modelling with LSTMRTRBM. In Proceedings of the 23rd ACM international conference on Multimedia (pp. 991994). ACM.
 Sutskever, I., Hinton, G. E., & Taylor, G. W. (2009). The recurrent temporal restricted boltzmann machine. In Advances in Neural Information Processing Systems (pp. 16011608).

approximate_inference
¶ Inference with function approximation.
Parameters:  graph – Graph of neurons.
 learning_rate – Learning rate.
 observed_data_arr – observed data points.
 training_count – Training counts.
 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 minibatch recursive learning. If this value is ‘1’, the inferencing is not a recursive learning.
 seq_len – The length of sequences. If None, this value will be considered as observed_data_arr.shape[1].
Returns: Graph of neurons.

approximate_learn
¶ learning with function approximation.
Parameters:  graph – Graph of neurons.
 learning_rate – Learning rate.
 observed_data_arr – observed data points.
 training_count – Training counts.
 batch_size – Batch size (0: not minibatch)
Returns: Graph of neurons.

back_propagation
¶ Details of the backpropagation through time algorithm.

batch_size
= 0¶

batch_step
= 0¶

get_opt_params
¶ getter

get_reconstrct_error_list
¶ getter

graph
= None¶

learning_rate
= 0.5¶

memorize_activity
¶ Memorize activity.
Parameters:  observed_data_arr – Observed data points in positive phase.
 negative_visible_activity_arr – visible acitivty in negative phase.

negative_visible_activity_arr
= None¶

opt_params
¶ getter

r_batch_size
= 0¶

r_batch_step
= 0¶

reconstruct_error_list
¶ getter

rnn_learn
¶ Learning for RNN.
Parameters: observed_data_list – observed data points.

set_readonly
¶ setter

wake_sleep_inference
¶ Sleeping, waking, and inferencing.
Parameters: observed_data_arr – feature points.

wake_sleep_learn
¶ Waking, sleeping, and learning.
Standing on the premise that the settings of the activation function and weights operation are common.
The binary activity is unsupported.
Parameters: observed_data_list – observed data points.
pydbm.approximation.shape_bm_cd module¶

class
pydbm.approximation.shape_bm_cd.
ShapeBMCD
¶ Bases:
pydbm.approximation.interface.approximate_interface.ApproximateInterface
Contrastive Divergence for ShapeBoltzmann machine(ShapeBM).
Conceptually, the positive phase is to the negative phase what waking is to sleeping.
The concept of Shape Boltzmann Machine (Eslami, S. A., et al. 2014) provided inspiration to this library.
The usecases of ShapeBM are image segmentation, object detection, inpainting and graphics. ShapeBM is the model for the task of modeling binary shape images, in that samples from the model look realistic and it can generalize to generate samples that differ from training examples.
References
 Eslami, S. A., Heess, N., Williams, C. K., & Winn, J. (2014). The shape boltzmann machine: a strong model of object shape. International Journal of Computer Vision, 107(2), 155176.

approximate_inference
¶ Inference with function approximation.
Parameters:  graph – Graph of neurons.
 learning_rate – Learning rate.
 observed_data_arr – observed data points.
 training_count – Training counts.
 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 minibatch recursive learning. If this value is ‘1’, the inferencing is not a recursive learning.
Returns: Graph of neurons.

approximate_learn
¶ learning with function approximation.
Parameters:  graph – Graph of neurons.
 learning_rate – Learning rate.
 observed_data_arr – observed data points.
 training_count – Training counts.
 batch_size – Batch size (0: not minibatch)
Returns: Graph of neurons.

get_reconstrct_error_list
¶ getter

reconstruct_error_list
¶ getter

set_readonly
¶ setter