def test_srnn_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 = SupervisedRnn(4, [10], 3, hidden_transfers=['lstm'], max_iter=2)
rnn.fit(X, Z)
def test_srnn_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 = SupervisedRnn(4, [10], 3, hidden_transfers=['lstm'], max_iter=2)
rnn.fit(X, Z)
def test_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 = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=10)
rnn.fit(X, Z)
rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], skip_to_out=True, max_iter=10)
rnn.fit(X, Z)
def test_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)
W = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=2)
rnn.fit(X, Z)
rnn = SupervisedRnn(
2, [10], 3, hidden_transfers=['tanh'], max_iter=2, imp_weight=True)
rnn.fit(X, Z, W)
def test_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)
W = np.random.standard_normal((10, 5, 3)).astype(theano.config.floatX)
X, Z, W = theano_floatx(X, Z, W)
rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], max_iter=10)
rnn.fit(X, Z)
rnn = SupervisedRnn(2, [10], 3, hidden_transfers=['tanh'], skip_to_out=True,
max_iter=10, imp_weight=True)
rnn.fit(X, Z, W)
def test_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 = SupervisedRnn(2, 10, 3, max_iter=10)
rnn.fit(X, Z)
def test_srnn_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)
rnn.fit(X, Z)
def test_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 = SupervisedRnn(2, 10, 3, max_iter=10)
rnn.fit(X, Z)
