def test_srnn_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 = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], skip_to_out=True, max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break
def test_srnn_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 = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=2) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=2) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break
def test_srnn_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) rnn = SupervisedRnn(2, 10, 3, max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break
def test_srnn_iter_fit(): X = np.random.standard_normal((10, 5, 2)) Z = np.random.standard_normal((10, 5, 3)) rnn = SupervisedRnn(2, 10, 3, max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break