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_iter_fit(): raise SkipTest() X = np.random.standard_normal((10, 5, 2)) Z = np.random.standard_normal((10, 5, 3)) 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)
def test_slstm_predict(): raise SkipTest() X = np.random.standard_normal((10, 5, 2)) rnn = SupervisedLstm(2, 10, 3, max_iter=10) rnn.predict(X)
def test_slstm(): raise SkipTest() X = np.random.standard_normal((10, 5, 2)) Z = np.random.standard_normal((10, 5, 3)) rnn = SupervisedLstm(2, 10, 3, max_iter=10) rnn.fit(X, Z)
def test_slstm_predict(): raise SkipTest() X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX) rnn = SupervisedLstm(2, 10, 3, max_iter=10) rnn.predict(X)