def test_srnn_predict(): X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX) X, = theano_floatx(X) rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=10) rnn.predict(X) rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], skip_to_out=True, max_iter=10) rnn.predict(X)
def test_srnn_predict(): X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX) X, = theano_floatx(X) rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=2) rnn.predict(X) rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=2) rnn.predict(X)
def test_srnn_predict(): X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX) rnn = SupervisedRnn(2, 10, 3, max_iter=10) rnn.predict(X)
def test_srnn_predict(): X = np.random.standard_normal((10, 5, 2)) rnn = SupervisedRnn(2, 10, 3, max_iter=10) rnn.predict(X)
def test_srnn_predict(): X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX) rnn = SupervisedRnn(2, 10, 3, max_iter=10) rnn.predict(X)