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,
)
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)
output.close()
output = open("trained_models/" + savednamed + ".pkl", "wb")
cPickle.dump(dbn_tadbn, output)
output.close()
recon.main(generated_series, test_data, invD, mu, sigma, savednamed, delay, hidden_layers_sizes, plotting=True)
# ===============================================================================
# plt.figure()
# plt.subplot(211)
# plt.plot(test_data[:generated_series.shape[1]])
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)
plt.plot(test_data[:generated_series.shape[1]])
plt.subplot(212)
plt.plot(generated_series[0])
plt.show()
