#extracting variables
Motion = mat_dict['Motion'];
#setting CRBM parameters
L1Config = {};
L1Config['n_visible'] = Motion[0,0].shape[1];
L1Config['n_hidden'] = 150;
L1Config['delay'] = 3;
L1Config['learning_rate'] = 1e-3;
L1Config['training_epochs'] = 200;
L1Config['batch_size'] = 100;
#train CRBM and sample hidden giving visible
crbm1, batchdata_l1 = CO.train_crbm(L1Config, batchdata1, seqlen1, data_mean1, data_std1);
#save the layer 1 crbm1
L1Config['data_mean'] = data_mean1;
L1Config['data_std'] = data_std1;
L1Config['model'] = {'A':crbm1.A.get_value(), 'B':crbm1.B.get_value(), 'W':crbm1.W.get_value(), 'hbias':crbm1.hbias.get_value(),'vbias':crbm1.vbias.get_value()};
scipy.io.savemat('crbmconfig_1.mat',{'PyCRBMConfig':L1Config});
# put together the features for each input sequence
Feat1 = np.ndarray(shape=(1,9), dtype='O');
for idx, data in enumerate(Motion[0]):
Feat1[0,idx] = sample_h_v(data, crbm1, L1Config);
#setting CRBM parameters
# coding: utf-8
import crbm as CO
import pickle
import scipy
crbm, batchdata = CO.train_crbm()
# Just dumping the model for further use in Python
with open('crbmconfig_sean_100h_3p.pkl', 'wb') as output:
pickle.dump(crbm, output, pickle.HIGHEST_PROTOCOL)
#scipy.io.savemat('crbmconfig_sean_100h_3p.mat',{'A':crbm.A, 'B':crbm.B, 'W':crbm.W, 'hbias':crbm.hbias,'vbias':crbm.vbias})
scipy.io.savemat('crbmconfig_sean_100h_3p.mat',{'A':crbm.A.get_value(), 'B':crbm.B.get_value(), 'W':crbm.W.get_value(), 'hbias':crbm.hbias.get_value(),'vbias':crbm.vbias.get_value()})
#extracting variables
Motion = mat_dict['Motion']
#setting CRBM parameters
L1Config = {}
L1Config['n_visible'] = Motion[0, 0].shape[1]
L1Config['n_hidden'] = 150
L1Config['delay'] = 3
L1Config['learning_rate'] = 1e-3
L1Config['training_epochs'] = 200
L1Config['batch_size'] = 100
#train CRBM and sample hidden giving visible
crbm1, batchdata_l1 = CO.train_crbm(L1Config, batchdata1, seqlen1,
data_mean1, data_std1)
#save the layer 1 crbm1
L1Config['data_mean'] = data_mean1
L1Config['data_std'] = data_std1
L1Config['model'] = {
'A': crbm1.A.get_value(),
'B': crbm1.B.get_value(),
'W': crbm1.W.get_value(),
'hbias': crbm1.hbias.get_value(),
'vbias': crbm1.vbias.get_value()
}
scipy.io.savemat('crbmconfig_1.mat', {'PyCRBMConfig': L1Config})
# put together the features for each input sequence
Feat1 = np.ndarray(shape=(1, 9), dtype='O')
