def test_usrnn_iter_fit():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
X, = theano_floatx(X)
rnn = UnsupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], loss=lambda x: T.log(x), max_iter=10)
for i, info in enumerate(rnn.iter_fit(X)):
if i >= 10:
break
def test_usrnn_iter_fit():
raise SkipTest()
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
X, = theano_floatx(X)
rnn = UnsupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], loss=lambda x: T.log(x), max_iter=10)
for i, info in enumerate(rnn.iter_fit(X)):
if i >= 10:
break
def test_usrnn_iter_fit():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
rnn = UnsupervisedRnn(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
for i, info in enumerate(rnn.iter_fit(X)):
if i >= 10:
break
def test_usrnn_iter_fit():
X = np.random.standard_normal((10, 5, 2))
rnn = UnsupervisedRnn(2, 10, 3, loss=lambda x: T.log(x), max_iter=10)
for i, info in enumerate(rnn.iter_fit(X)):
if i >= 10:
break
