accelbrainbase.observabledata._mxnet.neuralnetworks package

Submodules

accelbrainbase.observabledata._mxnet.neuralnetworks.auto_encoder module

class accelbrainbase.observabledata._mxnet.neuralnetworks.auto_encoder.AutoEncoder(encoder, decoder, computable_loss, initializer=None, learning_rate=1e-05, learning_attenuate_rate=1.0, attenuate_epoch=50, units_list=[100, 1], dropout_rate_list=[0.0, 0.5], optimizer_name='SGD', activation_list=['tanh', 'sigmoid'], ctx=gpu(0), hybridize_flag=True, regularizatable_data_list=[], scale=1.0, tied_weights_flag=False, **kwargs)

Bases: accelbrainbase.observabledata._mxnet.neural_networks.NeuralNetworks

Auto-Encoder.

References

  • Kamyshanska, H., & Memisevic, R. (2014). The potential energy of an autoencoder. IEEE transactions on pattern analysis and machine intelligence, 37(6), 1261-1273.
collect_params(select=None)

Overrided collect_params in mxnet.gluon.HybridBlok.

compute_loss(pred_arr, labeled_arr)

Compute loss.

Parameters:
  • pred_arrmxnet.ndarray or mxnet.symbol.
  • labeled_arrmxnet.ndarray or mxnet.symbol.
Returns:

loss.

extract_feature_points()

Extract the activities in hidden layer and reset it, considering this method will be called per one cycle in instances of time-series.

Returns:The mxnet.ndarray of array like or sparse matrix of feature points or virtual visible observed data points.
extract_learned_dict()

Extract (pre-) learned parameters.

Returns:dict of the parameters.
forward_propagation(F, x)

Hybrid forward with Gluon API.

Parameters:
  • Fmxnet.ndarray or mxnet.symbol.
  • xmxnet.ndarray of observed data points.
Returns:

mxnet.ndarray or mxnet.symbol of inferenced feature points.

get_init_deferred_flag()

getter for bool that means initialization in this class will be deferred or not.

hybrid_forward(F, x)

Hybrid forward with Gluon API.

Parameters:
  • Fmxnet.ndarray or mxnet.symbol.
  • xmxnet.ndarray of observed data points.
Returns:

mxnet.ndarray or mxnet.symbol of inferenced feature points.

inference(observed_arr)

Inference the feature points.

Parameters:observed_arr – rank-2 Array like or sparse matrix as the observed data points. The shape is: (batch size, feature points)
Returns:mxnet.ndarray of inferenced feature points.
init_deferred_flag

getter for bool that means initialization in this class will be deferred or not.

load_parameters(filename, ctx=None, allow_missing=False, ignore_extra=False)

Load parameters to files.

Parameters:
  • filename – File name.
  • ctxmx.cpu() or mx.gpu().
  • allow_missingbool of whether to silently skip loading parameters not represents in the file.
  • ignore_extrabool of whether to silently ignre parameters from the file that are not present in this Block.
regularize()

Regularization.

save_parameters(filename)

Save parameters to files.

Parameters:filename – File name.
set_init_deferred_flag(value)

setter for bool that means initialization in this class will be deferred or not.

set_readonly(value)

setter

accelbrainbase.observabledata._mxnet.neuralnetworks.neural_networks_3d module

class accelbrainbase.observabledata._mxnet.neuralnetworks.neural_networks_3d.NN3DHybrid(computable_loss, initializer=None, learning_rate=1e-05, learning_attenuate_rate=1.0, attenuate_epoch=50, units_list=[100, 1], dropout_rate_list=[0.0, 0.5], optimizer_name='SGD', activation_list=['tanh', 'sigmoid'], hidden_batch_norm_list=[None, None], ctx=gpu(0), hybridize_flag=True, regularizatable_data_list=[], scale=1.0, output_no_bias_flag=False, all_no_bias_flag=False, not_init_flag=False, **kwargs)

Bases: accelbrainbase.observabledata._mxnet.neural_networks.NeuralNetworks

3D Neural networks.

References

  • Kamyshanska, H., & Memisevic, R. (2014). The potential energy of an autoencoder. IEEE transactions on pattern analysis and machine intelligence, 37(6), 1261-1273.
batch_size

getter

forward_propagation(F, x)

Hybrid forward with Gluon API.

Parameters:
  • Fmxnet.ndarray or mxnet.symbol.
  • xmxnet.ndarray of observed data points. The shape is … - batch. - sequence. - dimention.
Returns:

mxnet.ndarray or mxnet.symbol of inferenced feature points.

get_batch_size()

getter

get_seq_len()

getter

seq_len

getter

set_batch_size(value)

setter

set_seq_len(value)

setter

Module contents