def test_fd_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 = SupervisedFastDropoutRnn(2, [10], 3, hidden_transfers=['rectifier'], max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break rnn = SupervisedFastDropoutRnn( 2, [10, 20], 3, hidden_transfers=['rectifier', 'tanh'], skip_to_out=True, max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break
def test_fd_srnn_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) X, Z = theano_floatx(X, Z) rnn = SupervisedFastDropoutRnn(2, [10], 3, hidden_transfers=['rectifier'], max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break rnn = SupervisedFastDropoutRnn( 2, [10, 20], 3, hidden_transfers=['rectifier', 'tanh'], skip_to_out=True, max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break
def test_fd_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 = SupervisedFastDropoutRnn(2, [10], 3, hidden_transfer='rectifier', max_iter=10) for i, info in enumerate(rnn.iter_fit(X, Z)): if i >= 10: break