Ejemplo n.º 1
0
pl.imshow(oldspec, origin="lower", aspect="auto")
pl.show()


batchdata = np.asarray(test_data, dtype=theano.config.floatX)

delay = 30
# seqlen = [ test_data.shape[0] ]  #for multiple files after one another: takes as first input the signal at later time, so delayed training corresponds
hidden_layers_sizes = [10]
numpy_rng = np.random.RandomState(123)
n_dim = [test_data.shape[1]]

dbn_tadbn = TADBN(
    numpy_rng=numpy_rng,
    n_ins=[n_dim],
    hidden_layers_sizes=hidden_layers_sizes,
    sparse=0.0,
    delay=delay,
    learning_rate=0.01,
)

dbn_tadbn.pretrain(
    batchdata,
    plot_interval=5,
    static_epochs=80,
    save_interval=10,
    ae_epochs=80,
    all_epochs=50,
    batch_size=5,
    seqlen=seqlen,
)
Ejemplo n.º 2
0
         
         test_data = np.concatenate((test_data,cepstrum))
 
 #print 'time slizes: %i || input dimensions: %i || window size:%i' %(test_data.shape[0],test_data.shape[1],nfft)
 
 
 batchdata = np.asarray(test_data, dtype=theano.config.floatX)
 
 
 #seqlen = [ test_data.shape[0] ]  #for multiple files after one another: takes as first input the signal at later time, so delayed training corresponds
 
 numpy_rng = np.random.RandomState(123)
 n_dim = [test_data.shape[1]]
 
 dbn_tadbn = TADBN(numpy_rng=numpy_rng, n_ins=[n_dim],
          hidden_layers_sizes=hidden_layers_sizes,
          sparse=sparse, delay=delay, learning_rate=0.01)
 
 dbn_tadbn.pretrain(batchdata, plot_interval=5, static_epochs=80,
                   save_interval=10, ae_epochs=80, all_epochs=50,
                   batch_size=5,seqlen=seqlen)
 
 output = open('trained_models/useful/' + savedname + '.pkl', 'wb')
 cPickle.dump(dbn_tadbn, output)
 output.close()
 #===============================================================================
 # 
 # # sanity check
 # generated_series = dbn_tadbn.generate(batchdata, n_samples=300)[0,:,:]
 # output = open('output/gen_' + savednamed + '.pkl', 'wb')
 # cPickle.dump([generated_series,test_data,delay,hidden_layers_sizes,invD,mu,sigma], output)
Ejemplo n.º 3
0
import numpy as np
import theano


test_data = np.array([np.sin(np.arange(400) * 0.2),
                      np.sin(np.arange(400) * 0.4)]).T


batchdata = numpy.asarray(test_data, dtype=theano.config.floatX)
delay = 0

numpy_rng = numpy.random.RandomState(123)
n_dim = [test_data.shape[1]]

dbn_tadbn = TADBN(numpy_rng=numpy_rng, n_ins=[n_dim],
          hidden_layers_sizes=[100],
          sparse=0.0, delay=delay, learning_rate=0.01)

dbn_tadbn.pretrain(batchdata, plot_interval=5, static_epochs=50,
                   save_interval=10, ae_epochs=0, all_epochs=0,
                   batch_size=5)

up = dbn_tadbn.propup(batchdata, static=True)


up = np.array(up)
print up

generated_series = dbn_tadbn.generate(batchdata, n_samples=40)
plt.figure()
plt.subplot(211)
Ejemplo n.º 4
0
import sys
sys.path.append('..')
from models.tadbn import TADBN
import numpy
import numpy as np
import theano


test_data = np.array([np.sin(np.arange(400) * 0.2),
                      np.sin(np.arange(400) * 0.4)]).T


batchdata = numpy.asarray(test_data, dtype=theano.config.floatX)
delay = 3

numpy_rng = numpy.random.RandomState(123)
n_dim = [test_data.shape[1]]

dbn_tadbn = TADBN(numpy_rng=numpy_rng, n_ins=[n_dim],
          hidden_layers_sizes=[10],
          sparse=0.0, delay=delay, learning_rate=0.01)

up = dbn_tadbn.propup(batchdata, static=False)



up = np.array(up)
print up.shape, batchdata.shape