def test_stateful_lstm2(self):
weights = np.ones(
(self.lstm.get_shape_in(), self.lstm.get_shape_out()))
for _ in range(10):
X = np.random.normal(size=(10, 1, 1))
for s in range(1, 3):
state = np.zeros((1, self.lstm.get_shape_out()))
out = np.zeros((1, self.lstm.get_shape_out()))
for i in range(s):
out, state = self.lstm_forward(X[i], out, state, weights)
lstm = Sequence(Vector(1, 1), s) >> LSTM(1, 1, stateful=True)
self.set_weights(lstm.right, 1)
lstm_out = lstm.predict(X[:s])[-1]
lstm_hidden = T.get_value(lstm.right.states[0])
lstm_state = T.get_value(lstm.right.states[1])
np.testing.assert_almost_equal(lstm_hidden, lstm_out, 5)
np.testing.assert_almost_equal(lstm_out, out, 5)
np.testing.assert_almost_equal(out, lstm_hidden, 5)
np.testing.assert_almost_equal(lstm_state, state, 5)
def test_stateful_lstm2(self):
weights = np.ones((self.lstm.get_shape_in(), self.lstm.get_shape_out()))
for _ in range(10):
X = np.random.normal(size=(10, 1, 1))
for s in range(1, 3):
state = np.zeros((1, self.lstm.get_shape_out()))
out = np.zeros((1, self.lstm.get_shape_out()))
for i in range(s):
out, state = self.lstm_forward(X[i], out, state, weights)
lstm = Sequence(Vector(1, 1), s) >> LSTM(1, 1, stateful=True)
self.set_weights(lstm.right, 1)
lstm_out = lstm.predict(X[:s])[-1]
lstm_hidden = T.get_value(lstm.right.states[0])
lstm_state = T.get_value(lstm.right.states[1])
np.testing.assert_almost_equal(lstm_hidden, lstm_out, 5)
np.testing.assert_almost_equal(lstm_out, out, 5)
np.testing.assert_almost_equal(out, lstm_hidden, 5)
np.testing.assert_almost_equal(lstm_state, state, 5)
def test_stateful_lstm(self):
self.lstm.reset_states()
self.set_weights(self.lstm.right, 2)
X = np.random.normal(size=(1, 1, 1))
weights = np.ones((self.lstm.get_shape_in(), self.lstm.get_shape_out())) * 2
state = np.zeros((1, self.lstm.get_shape_out()))
out = np.zeros((1, self.lstm.get_shape_out()))
for i in range(1000):
out, state = self.lstm_forward(X, out, state, weights)
lstm_out = self.lstm.predict(X)
lstm_state = T.get_value(self.lstm.right.states[1])
np.testing.assert_almost_equal(lstm_out[0], out[0], 4)
np.testing.assert_almost_equal(lstm_state, state[0], 4)
def test_stateful_lstm(self):
self.lstm.reset_states()
self.set_weights(self.lstm.right, 2)
X = np.random.normal(size=(1, 1, 1))
weights = np.ones(
(self.lstm.get_shape_in(), self.lstm.get_shape_out())) * 2
state = np.zeros((1, self.lstm.get_shape_out()))
out = np.zeros((1, self.lstm.get_shape_out()))
for i in range(1000):
out, state = self.lstm_forward(X, out, state, weights)
lstm_out = self.lstm.predict(X)
lstm_state = T.get_value(self.lstm.right.states[1])
np.testing.assert_almost_equal(lstm_out[0], out[0], 4)
np.testing.assert_almost_equal(lstm_state, state[0], 4)