def test_autoencoder_logistic_linear_tied():
data = np.random.randn(10, 5).astype(config.floatX)
ae = Autoencoder(5, 7, act_enc='sigmoid', act_dec='linear',
tied_weights=True)
w = ae.weights.get_value()
ae.hidbias.set_value(np.random.randn(7).astype(config.floatX))
hb = ae.hidbias.get_value()
ae.visbias.set_value(np.random.randn(5).astype(config.floatX))
vb = ae.visbias.get_value()
d = tensor.matrix()
result = np.dot(1. / (1 + np.exp(-hb - np.dot(data, w))), w.T) + vb
ff = theano.function([d], ae.reconstruct(d))
assert _allclose(ff(data), result)
def test_autoencoder_tanh_cos_untied():
data = np.random.randn(10, 5).astype(config.floatX)
ae = Autoencoder(5, 7, act_enc='tanh', act_dec='cos', tied_weights=False)
w = ae.weights.get_value()
w_prime = ae.w_prime.get_value()
ae.hidbias.set_value(np.random.randn(7).astype(config.floatX))
hb = ae.hidbias.get_value()
ae.visbias.set_value(np.random.randn(5).astype(config.floatX))
vb = ae.visbias.get_value()
d = tensor.matrix()
result = np.cos(np.dot(np.tanh(hb + np.dot(data, w)), w_prime) + vb)
ff = theano.function([d], ae.reconstruct(d))
assert _allclose(ff(data), result)
def test_autoencoder_logistic_linear_tied():
data = np.random.randn(10, 5).astype(config.floatX)
ae = Autoencoder(5, 7, act_enc='sigmoid', act_dec='linear',
tied_weights=True)
w = ae.weights.get_value()
ae.hidbias.set_value(np.random.randn(7).astype(config.floatX))
hb = ae.hidbias.get_value()
ae.visbias.set_value(np.random.randn(5).astype(config.floatX))
vb = ae.visbias.get_value()
d = tensor.matrix()
result = np.dot(1. / (1 + np.exp(-hb - np.dot(data, w))), w.T) + vb
ff = theano.function([d], ae.reconstruct(d))
assert _allclose(ff(data), result)
def test_autoencoder_tanh_cos_untied():
data = np.random.randn(10, 5).astype(config.floatX)
ae = Autoencoder(5, 7, act_enc='tanh', act_dec='cos',
tied_weights=False)
w = ae.weights.get_value()
w_prime = ae.w_prime.get_value()
ae.hidbias.set_value(np.random.randn(7).astype(config.floatX))
hb = ae.hidbias.get_value()
ae.visbias.set_value(np.random.randn(5).astype(config.floatX))
vb = ae.visbias.get_value()
d = tensor.matrix()
result = np.cos(np.dot(np.tanh(hb + np.dot(data, w)), w_prime) + vb)
ff = theano.function([d], ae.reconstruct(d))
assert _allclose(ff(data), result)
def create_ae(conf, layer, data, model=None):
"""
This function basically train an autoencoder according
to the parameters in conf, and save the learned model
"""
savedir = utils.getboth(layer, conf, 'savedir')
clsname = layer['autoenc_class']
# Guess the filename
if model is not None:
if model.endswith('.pkl'):
filename = os.path.join(savedir, model)
else:
filename = os.path.join(savedir, model + '.pkl')
else:
filename = os.path.join(savedir, layer['name'] + '.pkl')
# Try to load the model
if model is not None:
print '... loading layer:', clsname
try:
return Autoencoder.load(filename)
except Exception, e:
print 'Warning: error while loading %s:' % clsname, e.args[0]
print 'Switching back to training mode.'
def get_autoencoder(structure):
n_input, n_output = structure
config = {
'nhid': n_output,
'nvis': n_input,
'tied_weights': True,
'act_enc': 'tanh',
'act_dec': 'sigmoid',
'irange': 0.001,
}
return Autoencoder(**config)
