def test_slstm_iter_fit():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
rnn = SupervisedLstm(2, 10, 3, max_iter=10)
for i, info in enumerate(rnn.iter_fit(X, Z)):
if i >= 10:
break
def test_slstm():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
rnn = SupervisedLstm(2, 10, 3, max_iter=10)
rnn.fit(X, Z)
