def test_fd_srnn_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)
rnn.fit(X, Z)
def test_fd_srnn_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)
rnn.fit(X, Z)
rnn = SupervisedFastDropoutRnn(
2, [10, 20], 3, hidden_transfers=['rectifier', 'tanh'],
skip_to_out=True, max_iter=10)
rnn.fit(X, Z)
def test_fdsrnn_lstm_fit():
X = np.random.standard_normal((13, 5, 4)).astype(theano.config.floatX)
Z = np.random.standard_normal((13, 5, 3)).astype(theano.config.floatX)
W = np.random.standard_normal((13, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedFastDropoutRnn(4, [10], 3, hidden_transfers=['lstm'],
max_iter=2)
rnn.mode = 'FAST_COMPILE'
rnn.fit(X, Z)
def test_fdrnn_pickle():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
W = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedFastDropoutRnn(2, [10], 3, hidden_transfers=['rectifier'],
max_iter=2)
rnn.fit(X, Z)
rnn.predict(X)
pickle.dumps(rnn)
def test_fdsrnn_lstm_fit():
X = np.random.standard_normal((13, 5, 4)).astype(theano.config.floatX)
Z = np.random.standard_normal((13, 5, 3)).astype(theano.config.floatX)
W = np.random.standard_normal((13, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedFastDropoutRnn(4, [10],
3,
hidden_transfers=['lstm'],
max_iter=2)
rnn.mode = 'FAST_COMPILE'
rnn.fit(X, Z)
def test_fdrnn_pickle():
X = np.random.standard_normal((10, 5, 2)).astype(theano.config.floatX)
Z = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
W = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedFastDropoutRnn(2, [10],
3,
hidden_transfers=['rectifier'],
max_iter=2)
rnn.fit(X, Z)
rnn.predict(X)
pickle.dumps(rnn)
def test_fd_srnn_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)
rnn.fit(X, Z)
rnn = SupervisedFastDropoutRnn(2, [10, 20],
3,
hidden_transfers=['rectifier', 'tanh'],
skip_to_out=True,
max_iter=10)
rnn.fit(X, Z)
def test_fd_srnn_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)
W = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedFastDropoutRnn(2, [10], 3, hidden_transfers=["rectifier"], max_iter=10)
rnn.fit(X, Z)
rnn = SupervisedFastDropoutRnn(
2, [10, 20], 3, hidden_transfers=["rectifier", "tanh"], skip_to_out=True, max_iter=10
)
rnn.fit(X, Z)
rnn = SupervisedFastDropoutRnn(
2, [10, 20], 3, hidden_transfers=["rectifier", "tanh"], skip_to_out=True, max_iter=10, imp_weight=True
)
rnn.fit(X, Z, W)
def test_fd_srnn_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)
rnn.fit(X, Z)
