def test_slstm_iter_fit():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z = theano_floatx(X, Z)
rnn = SupervisedLstmRnn(2, [10], 3, hidden_transfers=['sigmoid'], max_iter=10)
for i, info in enumerate(rnn.iter_fit(X, Z)):
if i >= 10:
break
def test_slstm_iter_fit():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z = theano_floatx(X, Z)
rnn = SupervisedLstmRnn(2, [10], 3, hidden_transfers=['sigmoid'], max_iter=10)
for i, info in enumerate(rnn.iter_fit(X, Z)):
if i >= 10:
break
def test_slstm_predict():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
X, = theano_floatx(X)
rnn = SupervisedLstmRnn(2, [10],
3,
hidden_transfers=['sigmoid'],
max_iter=10)
rnn.predict(X)
def test_slstm_predict():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
X, = theano_floatx(X)
rnn = SupervisedLstmRnn(2, [10], 3, hidden_transfers=['sigmoid'], max_iter=10)
rnn.predict(X)
def test_slstm():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z = theano_floatx(X, Z)
rnn = SupervisedLstmRnn(2, [10], 3, hidden_transfers=["sigmoid"], max_iter=10)
rnn.fit(X, Z)