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_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)